Optimal Design and Management of Industrial Waste-to-Energy Systems

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Process Systems Engineering

Optimal design and management of industrial waste-to-energy systems Vasco Bolis, Elisabet Capon-Garcia, Marta Roca-Puigros, Aline Gazzola, and Konrad Hungerbuehler Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b03129 • Publication Date (Web): 08 Jan 2019 Downloaded from http://pubs.acs.org on January 10, 2019

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Industrial & Engineering Chemistry Research

Optimal design and management of industrial waste-to-energy systems Vasco Bolis,

y

Elisabet Cap on-Garc a,

,z

y

Marta Roca-Puigros,

Aline Gazzola,

y

y

1

and Konrad Hungerb uhler

yInstitute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093

Zurich, Switzerland zABB Switzerland Ltd., Segelhofstrasse 1K, 5405 Baden-Dattwil, Switzerland E-mail: [email protected]

Abstract

2

3

This work proposes a novel methodology for the optimization of industrial waste-to-

4

energy networks, combining logistic, site management and process elements into a single

5

MILP formulation. The developed model is validated with real information stemming

6

from industrial and institutional partners, and consequently applied to a multi-site case

7

study covering the whole Swiss industrial waste-to-energy network. Due to the sparse

8

nature of its chemical and pharmaceutical industry, and to centralized incineration

9

facilities, this can be considered as an ideal case for testing the application of developed

10

methodology to complex systems.

11

the implications of diverging goals on economic, environmental and safety aspects, with

12

particular focus on investments and possible synergies. Results indicate the importance

13

of investments in oxy-combustion that, together with a more cooperative management

14

of the network including the cement industry, enable a more local waste treatment with

15

a consequent reduction of shipments.

A set of ob jective functions is used to investigate

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ACS Paragon Plus Environment

Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1

Introduction

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In the last years, the increasing environmental awareness has lead to a more stringent envi-

18

ronmental legislation in many countries, aiming to foster a more sustainable consumption of

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natural resources. This is of special concern for energy-intensive industrial sectors, such as

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the chemical one, which currently relies predominantly on fossil sources for the supply of both

21

energy and raw chemicals. In fact, the chemical and petrochemical industry is the largest

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energy consumer among all industrial branches, with approximately 29% of the total indus-

23

trial energy use worldwide, corresponding to about 7.8% of the global energy consumption 1 .

24

Therefore, a large number of studies have tackled sustainability issues in the manufacturing

25

industry, for instance in the form of process intensi cation and heat integration opportuni-

26

ties. Nevertheless, these works have mostly focused on the process level; the integration with

27

other perspectives, as waste and supply chain management, have only recently started to be

28

addressed 2,3 . In fact, current scienti c interests in process system engineering increasingly

29

consider more global approaches, such as enterprise-wide optimization 4 . An important is-

30

sue, especially for multi-purpose plants, is the coordination between production process and

31

treatment facilities for the generated hazardous waste to avoid manufacturing hold-ups. In

32

this sense, Chakraborty and Linninger have discussed the importance of a well-coordinated

33

design of production processes and waste management in batch manufacturing sites in a

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series of publications, demonstrating that this can lead to important economical and envi-

35

ronmental bene ts at plant level 5 . The exibility of the obtained waste management design

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and strategies can then be assessed by considering longer multi-period horizons and uncertain

37

conditions 6,7 .

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Another interesting example of synergistic plant bene ts can be found in the work of

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Wassick about enterprise-wide optimization in integrated chemical complexes, which are

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described as a set of interconnected sub-systems that can be optimized separately 8 . Each of

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them combines several planning and operational challenges associated to a given product or

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service, such as utilities or waste treatment, thus integrating dierent process and logistic 2


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elements into a single formulation. In particular, this publication presents a detailed case

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study on the waste management sub-system of a chemical complex, showing a signi cant

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optimization potential. New opportunities arising from waste solvent mixing have been

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discussed by Rerat et al., in a formulation optimizing the choice between dierent treatment

47

options available in the investigated batch manufacturing site for a set of given streams 9 .

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An optimization model including heat integration between the dierent treatment processes

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has been proposed by Capon-Garca et al. for the same chemical site studied by Rerat et al.,

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highlighting the energetic importance of thermal valorization of waste residues 10 .

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Among the available technologies for the thermal treatment of hazardous waste residues,

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incineration is surely one of the most interesting options because of its large versatility.

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Indeed, it can be applied to a broad set of waste types with dierent properties and com-

54

position, as well as to solid, liquid and gaseous residuals. The remaining pollutant species

55

are removed from the produced fumes, as requested by the corresponding regulation, with a

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combination of washing and dust-removing steps. An additional advantage of incineration

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is the possibility to partially recover the released combustion heat in the form of steam,

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which can be directly used as utility or converted into electricity. This is particularly in-

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teresting in the case of integrated chemical sites, whose utility requirements ensure that

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the generated heat can be successfully exploited through a straightforward integration with

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existing steam networks. However, the complexity of the incineration process necessitates

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a careful planning of both operation and waste streams management to avoid any possible

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production hold-up. Speci cally, the main diculty arises from the huge variety of waste

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residues that the chemical and process industry generates in large volumes, with disparate

65

composition and physicochemical properties. Thus, an appropriate management of tempo-

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rary storage facilities is particularly critical to prevent incompatible mixing and consequent

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accidents. Moreover, it is common operational practice to simultaneously burn waste streams

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with dierent water content and heating value, so as to exploit the energy content of highly

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calori c combustible residues to sustain the incineration of non-combustible ones. Auxiliary 3



70

fuels, such as natural gas, and water are used in case of energy shortage and excess, respec-

71

tively. In this sense, recent studies have shown that an optimized planning and scheduling

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of hazardous waste incineration can be an ecient tool to reduce the consumption of such

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auxiliary fuels, with important economic and environmental bene ts 11,12 . The model pro-

74

posed by Abaecherli et al. optimizes waste ows between dierent storage and treatment

75

units, while imposing relevant operational constraints on waste mixing, treated capacity and

76

combustion temperature under an auxiliary fuel minimization objective.

77

Even though the previously discussed works have successfully introduced dierent per-

78

spectives for hazardous waste management, either more operational or more investment-

79

oriented, none of them has gone beyond the scale of an integrated chemical site. An im-

80

portant reason behind this is presumably the lack of information about both shipments and

81

generation of such waste residues, which has also been reported by the Organization for

82

Economic Cooperation and Development 13 . In fact, whereas incinerators usually perform

83

detailed analysis of their residues, companies without treatment facilities only classify them

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roughly based on the source process according to the current waste transport legislation.

85

Speci cally, all transboundary waste transports are subject to the Basel Convention 14 , but

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many European countries, such as EU members and Switzerland, also require noti cation of

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intra-national shipments of hazardous residuals 15,16 . Nonetheless, incineration operators typ-

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ically rely on internal classi cation systems that are often inconsistent with both the other

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incineration sites and the transport noti cation system. Therefore, incineration-relevant

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physicochemical properties of the residues, such as heating value and composition, are not

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available for a signi cant fraction of hazardous waste. Recently, Bolis et al. have proposed

92

a methodology for linking waste information from incineration plants to shipment noti -

93

cation data, thus allowing for the estimation of waste properties of uncharacterized liquid

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hazardous residues 17 . Such knowledge enables the development of optimization tools for the

95

management of industrial waste-to-energy networks, including chemical sites without any

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treatment facility. 4


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As previously mentioned, existing research has limited scope in terms of time and space

98

scales; in contrast, this work truly goes beyond the scale of an integrated chemical site

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and considers the full chemical industrial waste-to-energy perspective. The importance of

100

considering a network perspective for industrial waste-to-energy systems is given by two

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main reasons. First, chemical sites without incineration or other treatment facilities need

102

to outsource the disposal of their residues, leading to considerable waste transports towards

103

incineration plants. Besides representing an excellent economic opportunity for incinerators,

104

as it allows to accept more external residues, an optimized waste management stands for an

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ecient tool to both diminish the consumption of auxiliary fuels and increase the amount of

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energy recovered from the residues. Second, the potentially dangerous nature of the residuals

107

requires particular attention for avoiding unnecessary shipments that goes beyond purely

108

economic or energetic considerations. Indeed, the transportation of hazardous chemicals

109

through densely populated areas is already a major concern, as shown by recent safety

110

measures introduced in Switzerland 18 . Both aspects are in line with the envisaged future

111

challenges for thermal waste valorization, which include a minimization of transport and

112

measures aimed at enhancing energy recovery 19 .

113

An industrial waste-to-energy network consists in i), a set of waste-generating chemical

114

sites without treatment facilities; ii), a set of incineration plants with a possible internal

115

waste generation; and iii), a set of transportation means, such as road and rail, connecting

116

all these sites. Thus, it can be considered as a supply chain that incinerates waste residues

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to produce thermal energy, which is directly utilized on the site as process heat or converted

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into electricity. Nevertheless, a classical optimization approach for supply chain, appears

119

inappropriate for such system. Incineration and temporary storage capacities critically af-

120

fect the capability of accepting external waste, and it is consequently essential to include

121

them in the mathematical description of the system, together with supply chain aspects. In

122

this sense, recent research activities have already raised the necessity of integrating process

123

considerations in supply chain optimization for more accurate results, since hierarchical solu5



124

tions might not correctly identify all relevant trade-o solutions 2 . Furthermore, integration

125

of multiple aspects is frequently identi ed as a technical challenge in supply chain design,

126

but it also brings intrinsic opportunities in terms of both enterprise-wide optimization and

127

sustainability issues 3 . The latter are especially interesting since the already considerable

128

energy consumption of the industrial sector does not include the huge number of shipments

129

necessary to operate the corresponding supply chains.

130

The aim of this work is to evaluate possible synergies for a multi-site waste-to-energy

131

network. This requires the development of a novel methodology for the optimization of

132

such systems, integrating dierent dimensions of the waste management problem into a

133

single formulation. The proposed mathematical model considers both operational and de-

134

sign decisions, with particular focus on incineration feasibility, investment prioritization and

135

implications of diverging economic and transportation objectives.

136

This publication presents the waste-to-energy problem for liquid hazardous residues and

137

proposes a mathematical model capturing the most relevant logistic, management and opera-

138

tional aspects of a multi-site network. Such formulation is validated with real data stemming

139

from several industrial partners and consequently applied to a case study about the whole

140

Swiss industrial waste-to-energy network to investigate economic, environmental and safety

141

aspects under several objective functions. Because of the extremely sparse nature of its

142

chemical and pharmaceutical industry 20 , and centralized incineration facilities for hazardous

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waste 21 , Switzerland represents one of the most complex industrial waste-to-energy net-

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works and can thus be considered as an ideal case for testing the developed methodology.

145

The obtained results are critically discussed to identify relevant management policies and

146

investments, as well as possible research opportunities.

6


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2

Problem statement

148

Although the management of waste shipments between dierent actors is undoubtely the

149

main aspect of a waste-to-energy network, storage and operational policies within single

150

incineration sites are also signi cant at systemic level and have then to be included in the

151

problem. In fact, these determine the amount of waste that can be accepted from the other

152

actors of the network, or how much waste has to be outsourced for external treatment. This is

153

of particular importance in the context of this study, which aims at evaluating possible multi-

154

site synergies. In this sense, the investigated system can be conceptually divided in three

155

dierent dimensions representing i) the supply chain logistics, ii) the internal management

156

of the incineration sites, and iii) the operating conditions of the incineration process.

157

The great diversity concerning the nature of local chemical and process industries leads

158

to waste-to-energy systems that can be very dierent from country to country. Neverthe-

159

less, international regulations, such as the Basel Convention 14 and the EU legislation 22 ,

160

impose a certain number of common features, especially concerning shipments of hazardous

161

waste, thus enabling the formulation of a generic mathematical programming problem with

162

a broad application range. Moreover, for any kind of waste-to-energy system, it is possible

163

to distinguish between:

164

nor the nancial means to operate own waste-to-energy or other treatment facilities.

165

166

Industrial sites that generate chemical hazardous waste but have neither the technical

Incineration plants that burn chemical hazardous waste for treatment purposes. These

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are typically located within large integrated chemical complexes that can directly utilize

168

the released combustion heat to produce steam, which is then used as utility in dierent

169

production processes on the site or converted into electricity.

170 171

Cement plants that can use a selected, low-polluted, fraction of waste solvents as substitution fuel in their calcination processes. 7



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Such elements are interconnected through a transportation network. Waste shipments be-

173

tween incineration plants, as well as from industrial sites to cement and incineration plants,

174

normally occur quite frequently. Additionally, there usually are several intermediary compa-

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nies that perform temporary storage and/or some pre-treatment activities before transferring

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the residues to incineration and cement plants. However, the considerable number of these

177

intermediaries, and the fact that waste exchanges can be repeated would presumably result

178

in excessive computational requirements. Even though their technical support is undoubtely

179

very important, in particular for smaller enterprises without dedicated waste management

180

sections, the assessment of such bene ts is practically impossible without complex consid-

181

erations about size and business model for each company. Hence, it has been decided to

182

disregard the role of the aforementioned intermediaries for the scope of this study. The sup-

183

ply chain dimension of the investigated multi-enterprise waste-to-energy network consists

184

of:

185 186

187 188

189 190

191 192

193

1. A set of regions g, grouping industrial sites without treatment facilities, that produce several waste types w with dierent heating value and composition. 2. A set of incineration sites a, including both incineration and cement plants, each of them with plant-speci c features. 3. A speci c amount of waste imported into incineration sites a, together with outsourced waste from both regions g and such sites. 4. A set of inter-site transportation means connecting production and incineration facilities, such as road and rail networks. as graphically represented on the left side of Fig. 1.

8


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Figure 1: Schematic overview of the investigated system. On the left side: the supply chain dimension including incineration and cement plants a, together with chemical sites without treatment capability grouped in regions g. On the right side: internal waste management in sites a with temporary storage means and incineration units u, as well as the capacity expansion possibilities considered in this study. Decisions on waste transfer in terms of mass are represented as follows: m : mass of waste w transferred from tank e to tank : mass of waste w transferred from tank e to unit u in period t (not i in period t, m : mass of waste w transferred from tank i to unit u in represented in the gure), m : period t, m : mass of waste w transferred from truck k to tank i in period t, m mass of waste w transferred from wagon r to tank i in period t, m : mass of waste w : mass of waste w transferred from transferred from truck k to unit u in period t, m wagon r to unit u in period t, m : mass of waste w transferred from tank e to truck k in period t, m : mass of waste w transferred from tank e to wagon r in period t, m : mass of waste w transferred from region g to internal tank i in period t, m : mass of waste w transferred from region g to unit u in period t, m : mass of waste w exported from tank e during period t (abroad), m : mass of waste w exported from region g in period t (abroad). trei

e;i;w;t

treu

e;u;w;t

triu

i;u;w;t

trik

trir

i;r;w;t

i;k;w;t

trku

k;u;w;t

trru

r;u;w;t

trek

e;k;w;t

trgi

trer

e;r;w;t

g;i;w;t

trgu

g;u;w;t

EAB e;w;t

GAB g;w;t

194

The second dimension considered in the problem describes the internal management of

195

the incineration sites a. Although the operation can signi cant diverge from site to site, all

196

of them share a set of basic structural elements that allow for a generic description of the

197

internal waste management, as shown on the right side of Fig. 1. Speci cally, integrated

198

chemical sites usually operate centralized incineration facilities that burn waste residues

199

stemming either from internal production lines, sparse within the site, or from third sources.

200

The waste generated on the site is temporarily stored by the production facilities in a set

201

external tanks e, from which it can be transferred to dierent storage units, outsourced to 9



202

other incineration sites a or shipped outside the system boundaries. Storage equipment can

203

be distinguished into xed internal tanks i, located in tank farms at the incineration facilities,

204

and mobile items, such as trucks

205

purposes. From trucks and wagons, the waste can be conveyed to both internal tanks i and

206

incineration units

207

of residues accepted from external sources. Alternatively, some plants present a pipeline

208

network connecting external tanks e to internal ones i, and linking the latter to incineration

209

units u, thus allowing for a direct transfer without going through unloading stations.

210

u

k

and rail wagons r, which are also used for transferring

through dedicated unloading stations, which are also used in the case

Residues of dierent types

w

can only be mixed in certain internal tanks and storage

211

requires compatibility between waste and tank types for avoiding possible damages, such as

212

equipment corrosion. Nonetheless, it is possible to simultaneously incinerate several waste

213

types in the same unit. This enables to exploit the heating value of highly-calori c residues

214

for sustaining the incineration of low-calori c ones, which is extremely interesting from an

215

energetic perspective as it can decrease the consumption of auxiliary fuels. In this sense,

216

incineration units can be separated in ovens (U ), which can accept both combustibles and

217

mother liquors, and burners (U ) that can only incinerate combustible waste. In contrast

218

to integrated complexes, cement plants (U

219

generation and only accept external residuals as substitution fuels. In this case, storage tanks

220

are only used for buering purposes; consequently, cement kilns can be modelled as simple

221

incineration units (burners) that can only accept a selected fraction of combustible waste.

222

The storage capacity of each incineration plant can be increased by purchasing additional

223

internal tanks, trucks and rail wagons, whereas the treatment capacity can be incremented

224

by upgrading existing ovens with oxy-combustion technologies. Indeed, the oxy-combustion

225

process consists of burning fuel by partially replacing air with pure oxygen. As a result,

226

since some nitrogen is removed, a larger amount of waste can be treated and the volumetric

227

ow of ue gas through the treatment processes can be reduced.

228

O

u

B

u

cem u

) typically do not present any internal waste

The process dimension of the investigated system takes relevant operating aspects of 10


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229

the incineration units into account. These typically consist of an incineration chamber, or

230

furnace, a heat exchanger to partially recover the released energy (steam boiler), and a set of

231

consequent puri cation steps for the fumes. In order to ensure that the emission regulation

232

is ful lled, it is crucial to operate the incineration step in a proper way. Precisely, the

233

injected residues have to be completely incinerated and the furnace temperature has always

234

to be maintained in the speci ed operational range. Such conditions can be achieved by

235

providing a stoichiometric excess of combustion air and a sucient residence time in the

236

combustion chamber, as well as an appropriate energy input. Amounts of combustible and

237

non-combustible waste have to be carefully balanced such that the released heat is enough for

238

a complete thermal decomposition, while not exceeding a maximum temperature to prevent

239

the formation of nitrogen oxides and possible damages to the equipment. Auxiliary fuels, such

240

as natural gas, and water are provided in case of energy shortages and excesses, respectively.

241

Moreover, the volumetric throughput of the generated fumes must not exceed a certain

242

value to ensure that the subsequent cleaning units can remove the remaining pollutants in

243

compliance with the emission limits. A graphical representation of an incineration unit can

244

be found in Fig. 2.

Figure 2: Schematic overview of the operation of incineration units. 245

Cement plants can only use a low-polluted fraction of the waste as substitution fuel and,

246

apart from this, do not have to respect any additional constraint for the scope of the study. 11



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Therefore, the only limitation is the total amount of suitable residues.

248

This work introduces a novel methodology for the optimization of multi-enterprise waste-

249

to-energy networks, combining in a single formulation supply chain logistics, site management

250

and operation of the incineration process. Given:

251

252

A nite time horizon divided in multiple time periods t with xed duration.

A set of waste classes w with known heating value, elementary composition, water con-

253

tent and density, together with the corresponding amounts produced in each location

254

during every period t.

255

256

A set of mixing constraints for combinations of waste classes w.

A set of treatment sites a, including cement and incineration plants, with site-speci c features concerning

257

258

{ waste production, fed to external tanks e;

259

{ storage equipment (trucks k, rail wagons r and internal tanks i) with capacity,

260

waste compatibility and connections to both external tanks

261

units u;

e

and incineration

262

{ incineration units u, including maximum fumes throughput, required air excess,

263

operational temperature range, compatible waste and emission limits, together

264

with the capability of performing oxy-combustion;

{ initial waste content for each storage equipment.

265

266

with the corresponding waste production.

267

268

A set of regions g, grouping all waste-generating sites without treatment capability,

A set of transportation means, such as rail and road, connecting regions to sites, sites

269

between each other and both of them to other treatment facilities located outside the

270

system boundaries. 12


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271

Economic information about capital, operating, maintenance and shipment costs, as well as revenues from waste treatment services and sales of the recovered heat.

272

273

The goal is to obtain the following information, which can be provided to stakeholder as

274

decision-supporting tools:

275

All waste shipments occurring outside the incineration sites, including exports outside the system boundaries.

276

277

All waste transfers within incineration sites a, together with lling levels for each storage equipment.

278

279

An investment plan considering storage items and oxy-combustion upgrades to existing ovens.

280

281

The incineration performance, expressed in terms of treated waste, fumes volumetric

ow, recovered heat and consumption of auxiliary fuels.

282

283

Cash ow considerations for each site a, with all costs and incomes in detail.

284

Furthermore, several objective functions should be applied to the considered system with

285

the aim of investigating their in uence on the dierent stakeholders. Particular focus is

286

dedicated to economic bene ts for the incinerators, to waste shipments, and to trade-o

287

solutions between such perspectives.

288

3

Mathematical formulation

289

The investigated waste-to-energy system is modelled as a mixed-integer linear problem

290

(MILP) with discrete time representation, as successfully introduced in previous studies

291

on the optimization of industrial waste incineration 11,12 . The developed multi-period model

292

is written with a modular formulation, such that it can be applied to both single-site sys-

293

tems and multi-site networks. Moreover, it is easily adaptable to dierent time resolutions by

294

changing the aected parameters accordingly, in order to achieve the desired result precision. 13



295

3.1 Assumptions

296

Because of the extremely complex nature of the investigated system, it has been necessary to

297

introduce a set of dierent assumptions for keeping the model size within reasonable limits.

298

The goal is to enable its application to relevant case studies, whose results can then be

299

used as decision-support by stakeholders. The following assumptions are then considered to

300

develop the mathematical formulation of the problem:

301

For energy balance purposes and the calculation of the fumes volume, it is assumed

302

that the fumes are only composed of their most abundant species, namely nitrogen,

303

oxygen, carbon dioxide and water. Such assumption can be justi ed by the extremely

304

low concentration of other combustion products, which can be due to the low amounts

305

present in the residues, as in the case of halogenated compounds and salts, or achieved

306

through an appropriate setting of the operation. In fact, incomplete combustion is

307

prevented by ensuring a sucient residence time in the furnace, an excess of combustion

308

air and by providing enough energy to reach a minimum temperature. An upper

309

temperature bound has to be imposed for hindering the formation of nitrogen oxides,

310

as well as to prevent possible equipment damages caused by acid species that might be

311

produced during the incineration. If necessary, auxiliary fuels and water can be added

312

to the combustion for keeping the furnace temperature within the de ned operation

313

range.

314

The fumes volume is calculated from the mass using the ideal gas law. Such assumption

315

allows for a linear formulation of the model and is justi ed by the operating pressure

316

of about 1 bar used through the whole incineration process. Further details about the

317

validity of this assumption are provided as supporting information.

318

During inventory, the dierent waste classes w are considered as immiscible concern-

319

ing physicochemical properties. This approximation can be used as all incineration-

320

relevant properties are additive with respect to mass (i.e., energy content and elemen14


Page 14 of 63

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321

tary amounts), and since density is similar for all classes 17 . Additionally, it is common

322

operational practice to avoid potentially reactive waste mixtures, in order to prevent

323

infrastructure damages and other technical issues.

324

The energy balance is linearised with two inequalities setting upper and lower bounds

325

for the energy input as a function of the incinerated residues, as successfully introduced

326

in previous studies 12 .

327

w

is assumed to be constant in time. Changes

concerning waste residues are expressed as variations of the amounts for each class.

328

329

The composition of each waste class

The choice between rail and road transportation for inter-site shipments depends on

330

several empirical aspects that cannot be satisfactorily captured in a mathematical

331

model, including forewarning and forecast reliability. As a consequence, it has been

332

decided to consider a xed share of rail and road transportation for modelling waste

333

shipments. For this reason, speci c unloading stations are not considered and an overall

334

constraint on the waste throughput is considered to model the limited availability of

335

pump capacity.

336

Because of data unavailability about age and lifetime of each equipment piece, it is not

337

possible to consider its replacement or dismantlement in the formulation. Therefore,

338

it is assumed that both existing and purchased pieces can be neither substituted nor

339

removed. Consequently, salvage is also not considered in the proposed methodology.

340

Taxation is not included, since the related information could not be disclosed for con-

341

dentiality reasons. Moreover, local tax dierences, as incentives and rates, might be

342

signi cant and, in this sense, a speculative tax optimization favoring certain actors of

343

the network is beyond the scope of the study. This choice has been agreed with all

344

involved research and industrial partners 23 .

345

Transport expenses are paid by the corresponding shipping parties. 15



346

Page 16 of 63

The released combustion heat is partially recovered in the form of process steam,

347

for incineration plants, or directly used, in the case of cement plants. A subsequent

348

conversion into electricity is not considered for the purpose of this study. Economic

349

revenues arising from energy recovery are accounted as steam sales for both incineration

350

and cement plants.

351

3.2 Waste management logistics

352

Concerning the management of hazardous liquid waste, the following aspects should be

353

considered for planning purposes:

354

1. Waste transfers from production sites to internal incineration facilities.

355

2. Waste transfers from production sites to external incineration facilities.

356

3. The capacity of dierent storage units, both at production locations and at the incin-

357

eration facilities.

358

4. The mixing of dierent waste streams in storage units at incineration facilities.

359

5. Waste transfers from storage means to incineration units.

360

In order to model waste transfers and storage, it is rst necessary to impose mass balances

361

around each storage unit. In this sense, the mass inventory of an external tank m

362

end of period t can be de ned with waste ows in and out of the storage unit, together with

363

the stored amount at the end of the previous period m

364

represent waste streams coming from connected production facilities and are given by the

365

parameter M

366

units, trucks, rail wagons and to other treatment facilities not included in the system, which

367

are represented by variables m

ste e;w;t

P

e;w;t

st e;w;t

1

at the

(Eq. 1). In this case, in ows

. Instead, out ows stand for waste transfer to internal tanks, incineration trei e;i;w;t

,m

treu e;u;w;t

,m

trek e;k;w;t

16

,m

trer e;r;w;t


and m

EAB e;w;t

, respectively. Initial

Page 17 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

368


conditions for waste levels are provided with the parameter M mste e;w;t

=

ste;0 Me;w t=1

X

+m

st e;w;t

X

mtrei e;i;w;t

1

2 + M

t

u

i

.

mEAB e;w;t

P e;w;t

X

mtreu e;u;w;t

ste;0 e;w

X

mtrek e;k;w;t

mtrer e;r;w;t

8e; w; t

(1)

r

k

369

The binary variable b

370

external storage tank e at the end of period t, as well as for setting minimum and maximum

371

volumetric lling levels for each contained waste stream w, which are given by the parameters

372

V min

373

results, whereas the maximum one is simply determined by the volumetric capacity of the

374

equipment. The volume of a stored waste stream

375

mste e;w;t

ste e;w;t

and

Vee ,

is implemented to de ne if a waste stream w is stored in a speci c

respectively (Eq. 2). The minimum lling level is set to avoid unrealistic can be obtained by dividing its mass

w

by the corresponding density . w w

bste V min e;w;t

6

mste e;w;t

6

w w

8e; w; t

bste Vee e;w;t

(2)

376

Since a considerable fraction of storage tanks can also stock a mixture of dierent waste

377

streams simultaneously, an additional constraint (Eq. 3) is necessary to ensure that the

378

maximum volumetric capacity is not exceeded. Vee

>

X mste e;w;t

379

8e; t

w w

w

Similarly, mass balances are de ned for trucks

k

(3)

(Eq. 4) and rail wagons

r

(Eq. 5) at

380

the end of each time period t. In both cases, input ows are only allowed in the form of

381

transfers from external tanks (variables m

382

and wagons, waste can be transferred to internal tanks (m

383

units (m

384

given by the initial conditions parameters M

trek e;k;w;t

and m

trer e;r;w;t

, respectively). From both trucks trik k;i;w;t

trku k;u;w;t

and m

trru r;u;w;t

and m

trir r;i;w;t

) or incineration

). The stored mass at the end of previous period t stk;0 k;w

17

and M

str;0

r;w


1 is instead

for the rst period (t = 1), or


385

by the variables m

st k;w;t

mstk k;w;t

=

1

and m

stk;0 Mk;w t=1

st r;w;t

1

, otherwise (t 2).

+m

st

1

k;w;t

8k; w; t mstr r;w;t

=

str;0 Mr;w t=1

+m

st r;w;t

Page 18 of 63

1

2 +

X

t

2 +

mtrek e;k;w;t

X

e

X

t

X

mtrer e;r;w;t

X

mtrku k;u;w;t

u

i

e

8r; w; t

mtrik k;i;w;t

mtrir r;i;w;t

X

(4) mtrru r;u;w;t

u

i

(5)

386

Minimum lling levels and capacity constraints are de ned as for the previously discussed

387

case of external tanks (Eqs. 6-7 and 8-9 for trucks and rail wagons, respectively). In this

388

sense, the volumetric capacity of external tanks has to be substituted with the values for

389

trucks (V ) and wagons (V ). k

r

bk;w;t V stk

min

Vkk

6

mstk k;w;t

>

X mstk k;w;t w w

w

br;w;t V str

min

Vrr

6

w w

6

mstr r;w;t

>

X mstr r;w;t

6

w w

w

w w

bstk Vkk k;w;t

8k; w; t

8k; t

bstr Vr r;w;t

(6) (7)

8r; w; t

8r; t

(8) (9)

390

Moreover, it is assumed that the considered trucks and wagons can only be used internally for

391

waste transfers within the same plant. Waste shipments between dierent sites are allowed

392

through the variables m

393

as operated by third parties, such as transport companies. Concerning investments in new

394

trucks and rail wagons, their availability is controlled by subordinating the binary variables

395

bstk k;w;t

trei e;i;w;t

and b

str r;w;t

and m

treu e;u;w;t

, but they are subject to higher costs and considered

to the purchase of the corresponding equipment (section 3.4.2).

396

As previously mentioned, the production sites without incineration facilities have been

397

grouped into regions for modelling purposes. Therefore, a mass balance over each region g is

398

de ned for all time periods t and waste streams w (Eq. 10). The sole in ow consists in the 18


Page 19 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


399

local waste generation de ned by the parameter M

400

incineration units or outside the system boundaries are given by variables

401

and m

402

the variable m

403

For these reasons, an upper bound

404

accumulation (Eq. 11). Concerning this, the stored amount at the end of the previous period

405

t

406

rst period (as t 1 is not de ned in this case).

GAB g;w;t

CH g;w;t

, whereas transfers to internal tanks, mg;i;w;t , mg;u;w;t trgi

trgu

, respectively. Even though no information about inventory capacity is provided, stg g;w;t

has been added to represent temporary storage at these production sites.

1 is given by the variable m

stg

mg;w;t

mg;w;t

1

g;w;t

=

stg;0 Mg;w t=1

X stg

st

, or with the initial conditions parameter M

+m

trgu

mg;u;w;t

st

1

g;w;t

2 + M

t

CH

X

g;w;t

stg;0 g;w

for the

trgi

mg;i;w;t

i

8g; w; t

mg;w;t

GAB

u

6

has been assumed for avoiding in nite waste

Mgstg;max

(10)

8g; w; t

Mgstg;max

(11)

407

From the described external tanks, trucks, wagons and regions, waste can be shipped

408

to internal tanks, incineration units or, in the case of external tanks and regions, outside

409

the system. Internal tanks are located at incineration facilities and are used for temporary

410

storage, buering and mixing dierent waste streams w before these are incinerated. Thus,

411

an additional equation block is used to model storage and throughput operation in such

412

equipment (Eq. 12). This mass balance involves the amount of waste stored in the previous

413

period t 1 (m

414

tanks (m

415

waste can only be transferred to incineration units of the same integrated site (m

st i;w;t

trei e;i;w;t

1

or, for t = 1, the initial amount M

), trucks (m

trik k;i;w;t

msti i;w;t

=

), wagons (m

sti;0 Mi;w t=1

X

mr;i;w;t

r

416

The minimum lling level V

+m

trir

+

st i;w;t

X

trir r;i;w;t

1

X

t

trgi

), as well as transfers from external

) and regions (m

2 +

mg;i;w;t

g

min

sti;0 i;w

mtrei e;i;w;t

e

X

+

trgi g;i;w;t

X k

mi;u;w;t triu

). From internal tanks,

mtrik k;i;w;t

8i; w; t

triu i;u;w;t

).

+ (12)

u

for every waste stream w is also imposed in the case of internal 19



Page 20 of 63

417

tanks through the binary variable b

418

impractical solutions. Nevertheless, it is necessary to introduce some modi cations to the

419

approach used for external tanks, trucks and rail wagons, in order to account for possible

420

investments in capacity expansion. Speci cally, every internal tank is characterized by a

421

speci c pipeline connectivity from dierent external tanks e and to several incineration units

422

u belonging to the same site a.

423

combination leads to excessive computational requirements, particularly when considering

424

that multiple investments (storage tanks of dierent possible sizes) should be possible for the

425

same connectivity combination. Hence, in order to reduce the required computing power,

426

each existing internal tank is considered as a slot i, to which n

427

size s can be added to the initial capacity V . A lower and upper bound for the lling level

428

of each waste stream

429

Instead, the maximum volumetric capacity of an internal tank slot

430

14. The additional capacity in an internal tank slot i available during a certain period

431

is obtained by multiplying the number of new tanks of size

432

volumetric capacity V . The total slot capacity can then be expressed as the sum of initial

433

and additional capacity, as listed on the left side of Eq. 14.

sti i;w;t

As explained in detail in section 3.4, considering every possible

i i;t;s

i

w

is then included with Eq. 13, so as to avoid unrealistic solutions.

s

i

is enforced by Eq.

in the slot

nii;t;s

t

with their

s

s

X

Vs

s

n

i i;t;s

+V

i i

6

msti i;w;t

>

X msti i;w;t

s

435

additional storage tanks of

i

bsti V min i;w;t

434

(left side of Eq. 13), with the same aim of avoiding

w

w w

w w

6

bsti i;w;t

i;max

Ni

8i; t

V

s

smax

+V

i

i

8i; w; t

(13) (14)

A nal mass balance is used to model the waste transferred and treated in every incineration unit u, given by m

inc u;w;t

. Since such waste is immediately incinerated, meaning that

20


Page 21 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

436


no accumulation can occur, the mass balance only involves a single time period (Eq. 15). X

=

minc u;w;t

+

mtriu i;u;w;t

mtreu e;u;w;t

X

+

e

i

X

X

+

mr;u;w;t trru

X

r

mtrku k;u;w;t

+

k

8u; w; t

trgu

mg;u;w;t

(15)

g

437

Furthermore, waste shipments by trucks or wagons have to go through unloading stations

438

before storage in internal tanks or direct treatment in an incineration unit. Thus, the waste

439

throughput is limited by the installed pump capacity

440

stations, respectively. As previously explained, the proposed formulation does not distinguish

441

between rail and road transportation for inter-site waste shipments. Consequently, a single

442

constraint is imposed for all unloading stations (Eq. 16)

tot a

X

>

mtrir r;i;w;t

j( )2

r;i

a;i

j(

r;u

2

a;u)

a;i

j(

mg;i;w;t

j(

e

X a;i

2

j(

mtrik k;i;w;t

X

X

+

U Aa;u ;w

2

a;u)

mtrei e;i;w;t

trgu

mg;u;w;t

+

U Aa;u ;w

+

mtreu e;u;w;t

2

a;u)

+

mtrku k;u;w;t

2

a;u)

I Aa;i ;w

X

a;e) = EAa;e ;u

j(

g;u

j( )2

for wagon and truck unloading

I Aa;i ;w

j(

+

I Aa;i ;w

2

a;i

k;u trgi

a;e) = EAa;e ;i

e

j( )2

+

U Aa;u ;w

X

j( )2

g;i

k;i

mtrru r;u;w;t

a

X

+

I Aa;i ;w

X

tot

8a; t

(16)

U Aa;u ;w

443

3.2.1 Allowed waste transfers

444

By considering the dimension of the investigated system, it is clear that solutions involving

445

small and consequently unrealistic values for indirect waste transfers should be avoided in

446

the model. Similarly to storage modelling, this can be achieved by implementing lower and

447

upper bounds for all waste transfers between model units. For each set of transfer variables

448

mtr ,

449

occurs or not. For instance, a binary variable b

a corresponding set of binary variables b is de ned, indicating if a given waste transfer tr

trei e;i;w;t

21

is assigned to each waste transfer from



Page 22 of 63

450

external tank e to internal one i, represented by positive variable m

451

upper bounds are imposed for shipments leaving the system boundaries from external tanks

452

e

453

variables

454

outside the system boundaries, whereas parameters M

455

and upper bounds for such shipments. A detailed description of such lower and upper bounds

456

is listed in the supporting information for every waste transfer variable.

or regions g, represented by variables m

EAB e;w;t

bEAB e;w;t

and

bGAB g;w;t

and m

GAB g;w;t

trei e;i;w;t

. Similarly, lower and

, respectively. In this case, binary

indicate if residues from external tanks and regions are shipped and M

exp;min

exp;max

represent lower

457

Infeasible waste movements are speci ed by setting the value of corresponding transfer

458

variables m to zero. As previously mentioned, the model does not distinguish between rail

459

and road shipments for inter-plant waste transport. Consequently, waste movements through

460

trucks k and wagons r are only modelled for transfers possible within the same incineration

461

plant a. This can be achieved by xing to zero the value of all variables representing inter-

462

plant waste shipments with trucks k and wagons r (further details are provided as supporting

463

information). These are instead expressed with variables m

464

internal tanks i and incineration units u, respectively. On the one hand, m

465

to model direct waste transfers within the same incineration according to a speci c pipeline

466

connectivity EI . Thus, m

467

EIe;i ,

468

other hand, m

469

are already modelled as combination of transfers via trucks, wagons or incineration tanks.

470

Intra-plant waste transfers through m

471 472

tr

trei e;i;w;t

dir

trei

e;i

e;i;w;t

dir

e;i

e;u;w;t

treu e;u;w;t

for transfers to

trei e;i;w;t

is also used

transfers are only allowed for (e; i) pairs belonging to subset

de ned as the union between EI treu

and m

and inter-site connectivity EI

CH

e;i

(Eq. 17). On the

are only used to describe inter-plant shipments, since at plant level they treu e;u;w;t

mtrei e;i;w;t

= 0

mtreu e;u;w;t

= 0

are then forbidden with Eq. 18.

8 (e; i; w; t j (e; i) 2= EI ) 8 (a; e; u; w; t j (a; e) 2 EA

(17)

e;i

a;e

and (a; u) 2 U A )

(18)

a;u

Direct waste transfers from internal tank slots i to units u, described with variable m

triu i;u;w;t

,

can only be performed if these are physically connected through the pipeline network (Eq. 22


Page 23 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

473


19).

8 (i; u; w; t j

= 0

mtriu i;u;w;t

(i; u) 2= IU )

(19)

i;u

474

Likewise, indirect waste movements via trucks or wagons can only occur if the corresponding

475

source or destination units are equipped accordingly. Further details about this are provided

476

as supporting information.

477

3.3 Plant operation

478

3.3.1 Incineration process

479

Besides inventory mass balances and fumes capacity constraints, the operation of an inciner-

480

ation plant for hazardous requires to consider other technical limitations, such as the mixing

481

of potentially reactive waste streams and the ful lment of speci c temperature conditions

482

for the process. Moreover, physical and regulative constraints have to be considered for the

483

combustion fumes.

484

Dierently to storage units, the incineration capacity is determined by the volumetric

485

ow of the combustion fumes. In fact, these have to undergo several washing steps to re-

486

move possible pollutant species before being released into the environment. For this reason,

487

the fumes volumetric throughput per period cannot exceed a maximum value V

488

fumes volume

489

w

490

summing the incinerated waste m

491

bustion, represented by the term m

492

and combustion stoichiometry, the composition

493

combustion of stream w, the mass of each component m

494

wardly from the fumes mass m

495

volume

f umes

vu;w;t

inc u;w;t

u;w;t

f umes u;w;t

f umes

mu;w;t

is calculated by

with the amount of air required for its complete com-

inc

f umes

. The

resulting from the combustion of a given amount of a waste stream

can be obtained with the following procedure: rst, its mass

vu;w;t

f umes u

Q

air w

(Eq. 20). Given, from waste composition f umes Xc;w

of the fumes produced during the

c c;u;w;t

can be determined straightfor-

(Eq. 21). Then, assuming ideal gas behavior, the fumes

resulting from the incineration of stream 23

w


can be estimated as the sum of


Page 24 of 63

496

the volume of all the single components c, obtained by dividing the mass of each of them by

497

its density in the considered incineration unit

498

to achieve a linear model formulation, it is a reasonable approximation for the operating

499

conditions of the incineration units, which involve a temperature range comprised from 750

500

to 1200 C and a pressure very close to the atmospheric one. mu;w;t

=

minc u;w;t

mcc;u;w;t

=

mu;w;t

f umes

=

f umes

vu;w;t

f umes

cc;u .

1+Q

X

air

8u; w; t

(20)

8c; u; w; t

f umes

X mc;u;w;t

w

c;w

c

(21)

8u; w; t

cc;u

c

Although this assumption is necessary

(22)

501

The same procedure is done for calculating the volume v

502

eventual use of natural gas as auxiliary fuel to sustain the incineration process, with the only

503

dierence of using the gas-speci c air demand Q

gas

u;t

gas

vu;t

X

=

gas

mu;t

gas

(1 + Q

of the fumes resulting from an

and fumes composition X

gas

)

c

Xcgas

!

cc;u

gas c

( Eq. 23).

8u; t

(23)

504

In case of mother liquor scarcity, water might be injected in the incineration unit for

505

temperature control purposes. As this does not involve any chemical reaction, the added

506

water completely evaporates. Thus, the volume v

507

calculated from its mass m

water u;t

of the vaporized water can be directly

and its density H2 O (Eq. 24). c

;u

water vu;t

508

water

u;t

=

mwater u;t c H2 O;u

8u; t

(24)

Additionally, there is the possibility of performing oxy-combustion, if the concerned

509

equipment has already been installed. Since the amount of injected oxygen m

510

sion variable of the problem, it is not possible to include it directly in the fumes composition

511

parameter, required to calculate the fumes volume, while keeping the model formulation

512

linear. For this reason, the partial substitution of air with pure oxygen has to be considered

oxy u;t

24


is a deci-

Page 25 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


513

as follows: rst, the amount of \spared" nitrogen mN2 , corresponding to the amount of air

514

containing an oxygen mass equal to the injected m , can be easily obtained from the air

515

composition (Eq. 25). The nitrogen volume vN2 is then calculated by dividing mN2 with its

516

oven-speci c density N2 (Eq. 26).

u;t

oxy u;t

u;t

c

;u

= 3:253 m

N

mu;t2

=

N

vu;t2

517

u;t

8u; t

oxy u;t

N

mu;t2

(25)

8u; t

c N2 ;u

(26)

Finally, the maximum fumes volumetric throughput per period

Vuf umes

limits the to-

518

tal fumes volume produced in unit

519

the volumes resulting from waste incineration, natural gas combustion and water addition,

520

and subtracting the amount of nitrogen contained in the air amount substituted by oxy-

521

combustion (Eq. 27). As previously discussed, the ideal gas approximation is required to

522

keep the model linear, which is of outermost importance in the case of large and complex

523

problems, such as the investigated one. If necessary, the proposed formulation has also the

524

possibility of specifying shutdown periods for the dierent incineration units, as well as to

525

optimize them. In this sense, the availability of incineration unit u in period t is expressed

526

by binary variable bu .

u

during any period t, which is obtained by summing

u;t

buu;t Vuf umes

>

X

f umes

vu;w;t

+v

gas

u;t

+v

water

u;t

N

vu;t2

8u; t

(27)

w

527

The amount of pure oxygen (oxy-combustion) allowed in unit u during period t cannot

528

exceed a value corresponding to the oxygen fraction, including excess, of the required com-

529

bustion air (Eq. 28). Additionally, more stringent limitations might have to be applied for

530

meeting emission legislations, which are often based on a reference oxygen composition 24 . (3:253 + 1) m

oxy u;t

6

X

minc u;w;t

Q

w

25


air w

8u; t

(28)


The combustion heat

531

inc

u;t

Page 26 of 63

released during the waste incineration in unit u during period

can be estimated from the lower heating value (Eq. 29). In incineration plants, the

532

t

533

released combustion heat is then partially recovered in the form of steam with an unit-

534

speci c eciency

535

cement plants, no steam is generated as the combustion heat is directly utilized in the kiln

536

and for this reason a complete recovery of the released heat is assumed in the model ( = 1).

537

The recovered heat in plant a during time period t ( ) can then be obtained as presented

538

in Eq. 30 for both cement and incineration plants.

u ,

which has been retrieved from industrial partners. In the case of

u

rec

a;t

=

inc u;t

X w

=

a;t

rec

j(

minc Hw u;w;t

+m

H

X

8a; t

2

a;u)

u

gas u;t

inc u;t u

8u; t

gas

(29) (30)

U Aa;u

539

In order to keep the combustion temperature within the given operational range, several

540

residuals with dierent heating values are simultaneously incinerated in variable amounts,

541

according to their availability. In case of energy de cits, natural gas is used to sustain the

542

incineration process, whereas water is added when the energy content of the residuals is too

543

high. From a modelling perspective, enforcing such unit-speci c temperature range requires

544

an energy balance, which would still result in a non-linear formulation. Therefore, it has been

545

decided to rely on the approach proposed by Abacherli for linearising the energy balance of

546

an incineration process for liquid hazardous waste 12 . In practice, the released combustion

547

heat

548

heat the resulting fumes up to the lowest allowed operational temperature

549

energy

inc u;t

should be comprised between the value

max

u;t

, representing the energy required to

needed for reaching the maximum temperature T min u;t

550

min

u;t

The values of

max u;t

and

6

min u;t

inc u;t

6

max u;t

8u 2= U

max u

cem u

;t

Tumin ,

and the

(Eq. 31). (31)

depend on the amounts of fumes resulting from the com26


Page 27 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


bustion of dierent waste streams (m

552

with the additional use of pure oxygen (m ) and water (m

553

amounts

554

fumes components with their average speci c energy (Eq. 32 and 33). As the operational

555

temperature range is equipment-speci c, the average speci c energy (\E "-parameters), and

556

consequently also the value of

f umes u;w;t

max u;t

and

min u;t

=

X

max

f umes

mu;w;t

E

w;max

(1 + Q X m E gas

mu;t

=

w gas

mu;t

u;t

oxy

water

u;t

u;t

gas

w;min

u;w;t

u;w

gas

min u;t

+m

gas

)), together

). The required energy

, are dierent for each incineration unit u.

water

)E

f umes

(1 + Q

and

u;t

u;w

w

min u;t

gas

can then be estimated by multiplying the mass of the mentioned

u;t

max u;t

) and auxiliary fuels (m

(1 + Q

551

water;max u

+

3:253 m

oxy

gas;max

u;t

u

+m

)E

E

water u;t

E

water;min u

N2 ;max

u

8u 2= U

;t

(32)

;t

(33)

cem u

+

3:253 m

oxy

gas;min

u;t

u

E E

N2 ;min

u

8u 2= U

cem u

557

Since both operation and design of incineration furnaces and cement kilns can be quite

558

dierent, the described energy balance (Eq. 31-33) does not apply to cement plants units

559

(u 2 U

cem u

). In this case, only certain waste streams are suitable for being used as substitution

560

fuels and no constraint about the combustion heat is considered, apart from a limited waste

561

availability. The use of natural gas is also not considered for cement plants, as there is no

562

need of auxiliary fuels for sustaining the combustion.

563

3.3.2 Storage units

564

Although multiple waste streams can be simultaneously incinerated to better exploit the

565

energy content of the residues, there are logistic constraints concerning the temporary stor-

566

age prior to thermal treatment. Precisely, incompatible waste streams cannot be mixed in

567

order to avoid possibly dangerous chemical reactions, such as acid-base ones. Moreover, the

568

storage of certain waste streams requires special equipment, especially in the presence of

569

corrosive species. Based on information from the involved industrial partners 23 , this study

570

approximates such limitations by dividing internal storage tanks in the three categories, 27



Page 28 of 63

571

namely shared combustibles, shared mother liquors and dedicated mother liquors, as further

572

described in the supporting information. Speci cally, only one mother liquor stream at the time can be stored in each suitable

573 574

internal tank, namely the ones belonging to types I

575

as slots i, consisting of one existing storage unit, to which new tanks can be purchased at

576

dierent times of the optimization horizon, thus making possible to store more than one

577

mother liquor stream in a single slot i. In fact, the maximum number of mother liquors

578

that can be stocked in a slot i is given by the current number of tanks installed in it, which

579

is obtained by summing the number of purchased tanks

580

Ni

581

tanks, the number of present mother liquor streams, expressed as sum of storage variables

582

, bsti i;w;t

M L;0

ML i

and I . Internal tanks are modelled B

i

nii;t;s

in the slot to the parameter

, representing the initial number of storage tanks in slot i. For all suitable storage

is then set lower or equal to the current tank number with Eq. 34. X w

2

bsti i;w;t

6

M L;0

Ni

+

X

ML Ww

8i 2

nii;t;s

IiB

[I

ML

i

s

583

In order to avoid the presence of mother liquor waste in combustible tanks I

584

bsti i;w;t

585

to prevent the storage of combustibles in mother liquor tanks (Eq. 36).

586

(34)

;t

CO i

, the value of

is xed to zero in case of non-compatible storage (Eq. 35). A similar constraint is used

bsti i;w;t

= 0

bsti i;w;t

= 0

8 8

i; w; t i; w; t

j j

2I i2I i

CO i

ML i

and and

2W w2W

w

ML

w

CO w

(35) (36)

Additionally, no mixing is allowed in external tanks, trucks and wagons (Eq. 37, 38 and 39,

28


Page 29 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

587


respectively). X

bste e;w;t

6

1

8e; t

(37)

bstk k;w;t

6

1

8k; t

(38)

bstr r;w;t

6

1

8r; t

(39)

w

X w

X w

588

Transferring residues to incineration units in periodic maintenance is clearly not possible.

589

Thus, a set of tightening constraints is added to the model for waste transfers from exter-

590

nal and internal tanks, trucks, wagons, and regions by setting the corresponding transfer

591

variables lower than the use of the sink unit (bu ), as shown in Eqs. 40-44. u;t

btreu e;u;w;t btriu i;u;w;t btrku k;u;w;t btrru r;u;w;t trgu

bg;u;w;t

6 6 6 6 6

buu;t buu;t buu;t buu;t buu;t

8e; u; w; t 8i; u; w; t 8k; u; w; t 8r; u; w; t 8g; u; w; t

(40) (41) (42) (43) (44)

592

3.3.3 Oxy-combustion

593

In order to avoid unrealistic solutions, the amount of oxygen has to be larger than a minimum

594

value M

595

46. Such constraints only apply to incineration ovens, since oxy-combustion is not used in

596

burners and cement plants. If necessary, the upper bound M

597

that the resulting fumes composition is in compliance with the current emission regulation.

598

In this sense, it has been decided to consider a conservative approach for preventing solutions

oxy;min

if oxy-combustion is used in a given period t, as imposed with Eqs. 45 and

29


oxy;max

can also be set to impose


599

Page 30 of 63

at limit values.

> 6

oxy

mu;t

oxy

mu;t

b

oxy

M oxy;min bu;t M oxy;max

oxy u;t

8u 2 U 8u 2 U

O

u

;t

(45)

;t

(46)

O

u

600

Additionally, an upper bound is set for total amount of oxygen available in each period t for

601

an incineration site a (Eq. 47). X

j(

u

2

a;u)

oxy

mu;t

6

8a; t

M oxy;max

(47)

U Aa;u

602

The impossibility of using oxy-combustion when its corresponding oven is shut down is

603

included as listed in Eq. 48. buu;t

604

>

oxy

bu;t

8u 2 U

O

u

;t

(48)

3.4 Investments in new equipment

605

A realistic modelling of investments is crucial to evaluate long-term bene ts in terms of eco-

606

nomic and environment aspects. Besides cash ow calculations (section 3.5), it is necessary

607

to link the purchase of each new equipment piece to the possibility to use it. On the one

608

hand, oxy-combustion upgrades, new trucks and wagons can be simply considered as inactive

609

items that are unlocked from the purchase period onwards through binary variables. This

610

means that all equipment pieces are included in the formulation, with the value of variables

611

corresponding to existing ones xed to 1. A single size is considered for both truck and

612

wagons, whereas the amount of pure oxygen used during oxy-combustion is a model vari-

613

able. On the other hand, internal tanks can be directly linked to dierent external tanks

614

and incineration units, as well as being equipped for indirect transfers through trucks and

615

wagons, resulting in a complex connectivity matrix. Therefore, the same approach used for 30


Page 31 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


616

the other types of investments, based on inactive equipment pieces that can be unlocked

617

as part of the optimization, would lead to excessive computational requirements because of

618

the large number of possible combinations. This work proposes a novel approach based on

619

the addition of new items to slots representing the existing equipment. In order to partially

620

capture the considerable amount diversity of available tank volumes, investments in internal

621

tanks also involve the choice between dierent sizes.

622

3.4.1 Oxy-combustion

623

Concerning oxy-combustion upgrades to existing incineration ovens, the possibility of using

624

such technology, modelled with variable

625

pressed through binary variable

626

former (Eq. 49).

, boinst u;t

boinst u;t

oxy

bu;t

, is subordinated to equipment purchase, ex-

whose value has then to be larger or equal to the

>

oxy

bu;t

8u 2 U

O

u

(49)

;t

627

As previously mentioned, it is assumed that all types of equipment, including oxy-combustion

628

upgrades, cannot be decommissioned or replaced. Thus, the value of variable bo

629

ing if such equipment is installed, cannot decrease in time (Eq. 50).

inst u;t

boinst u;t

>

8u 2 U

boinst u;t 1

O

u

;t

>2

, indicat-

(50)

630

For cash ow calculations, it is also necessary to know, for each plant a, exactly when new

631

equipment is bought. Purchase events are represented by integer variable n , which can

632

be obtained by subtracting the number of available oxy-combustion upgrades in the previous

633

period t 1 to the value n

oxy a;t

oxy a;t

for the current one (Eq. 51), calculated by summing variables

31



634

boinst u;t

Page 32 of 63

over the ovens belonging to the same plant (Eq. 52). oxy

na;t

=

oxy

na;t

oxy

na;t

1

2 +

t

oxy

na;t

=

X

j(

u

oxy

na;t

2

a;u)

O (Uu

Naoxy;0 t=1 boinst u;t

\

8a; t 8a; t

(51) (52)

U Aa;u )

635

3.4.2 Trucks and wagons

636

Similarly to oxy-combustion, the availability of trucks and wagons is expressed by binary

637

variables b

638

Waste storage and transfers from and to such units must then be lower or equal than these

639

variables, as imposed with Eqs. 53-56 for trucks, and with Eqs. 57-60 for rail wagons.

truck k;t

and b

wag r;t

, respectively, which are set to 1 in the case of existing equipment.

btrek e;k;w;t btrik k;i;w;t btrku k;u;w;t bstk k;w;t btrer e;r;w;t btrir r;i;w;t btrru r;u;w;t bstr r;w;t

640

As for oxy-combustion, the value of

641

62). btruck k;t wag

br;t

6 6 6 6 6 6 6 6

btruck k;t btruck k;t btruck k;t wag

br;t

wag

br;t

wag

br;t

wag

br;t

btruck k;t

> >

8e; k; w; t 8k; i; w; t 8k; u; w; t 8k; w; t 8e; r; w; t 8r; i; w; t 8r; u; w; t 8r; w; t

btruck k;t

and

btruck k;t 1 wag

br;t

1

32

wag

br;t

(53) (54) (55) (56) (57) (58) (59) (60)

cannot decrease in time (Eqs. 61 and

8k; t > 2 8r; t > 2


(61) (62)

Page 33 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


642

Similarly, the purchase of new equipment in each period t is expressed through variables

643

nka;t

644

the number of available trucks (n ) and wagons (n ) is again calculated from activation

645

variables b

and n for trucks and rail wagons, respectively (Eqs. 63 and 65). For each plant a, r

a;t

truck k;t

and b

wag k;t

r

a;t

a;t

, as shown in Eqs. 64 and 66.

nka;t

=

nka;t

=

na;t

=

nrt

=

nka;t

nka;t

1

X k

r

k

j(

2

a;k )

na;t

nra;t

X r

j(

2

a;r )

2 +

t

nka;t

btruck k;t

8a; t

nra;t

K Aa;k

r

1

2 +

t

Nak;0 t=1

(63) (64)

Nar;0 t=1

8a; t

wag

br;t

8a; t 8a; t

(65) (66)

RAa;r

646

3.4.3 Internal tanks

647

As previously mentioned, internal tanks require a dierent approach for overcoming compu-

648

tational issues arising from their complex connectivity with external tanks e and incineration

649

units u, as well as from the possibility of purchasing new equipment in dierent sizes s. For

650

these reasons, a slot i is de ned for all existing internal tanks. New storage tanks can then

651

be added to such slots, keeping the same connectivity as the original. Concerning model

652

formulation, this implies shared material balance and capacity constraints (Eqs. 12 and 14,

653

respectively), as well as a constraint on the mixing of mother liquor streams (Eq. 34). Ad-

654

ditionally, upper bounds on the total number of purchased internal tanks is limited at both

655

plant (N

656

in the tank farm by the incineration facility, as listed in Eqs. 67 and 68. Moreover, such

657

constraints allow to decrease the model's complexity, which is of great importance in the

i;tot

a

) and slot (N

i;max i

) level with the aim of simulating the limited space availability

33



658

Page 34 of 63

case of large problems such as the investigated one. Nai;tot

X

>

j( )2

s;i i;max

Ni

>

nii;t;s

a;i

X

I Aa;i

nii;t;s

8a; t

(67)

8i; t

(68)

s

659

As for other equipment types, the number of purchased tanks of size

660

decrease in time (Eq. 69). The number of tanks purchased in each period t for plant

661

(n

662

N

663

problem (Eq. 71).

i a;t;s

in slot

i

cannot a

) is obtained as listed in Eq. 70 for cash ow calculation purposes. An upper bound

i;max a

s

is then imposed to decrease the number of possible combinations considered in the

nii;t;s

>

nia;t;s

=

nii;t

X

j( )2

i

N

i;max a

>

1;s

a;i

X

I Aa;i

nia;t;s

8i; t > 2; s nii;t;s

nii;t

(69) 1;s

2 + n

i

t

8a; t

i;t;s t=1

8a; t; s

(70) (71)

s

664

3.5 Economic evaluation

665

The net present value is undoubtedly one of the most used tools when evaluating dierent

666

investment opportunities in process design 25 . The formulation used in this work is based on

667

the approaches proposed by Chakraborty et al. 6 , Sahinidis and Grossmann 26 , and Wassick 8

668

with some adaptations to capture characteristic features of the investigated system. In

669

order to determine the net present value, time value of money has to be considered in the

670

problem. In this sense, a nominal compounding period of one year is considered as basis

671

for the calculations, with a xed interest rate

672

of the problem requires the possibility of using a time resolution for the operation higher

673

than the compounding one (i.e., a duration of periods t inferior to one year). Therefore, the

674

actualization term

t

I R.

Nevertheless, the modular formulation

is introduced to compound the interest only at the end of selected time 34


Page 35 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


675

periods t, expressed through the binary parameter

676

of

677

otherwise.

BtN P V

(Eq. 72). Speci cally, the value

BtN P V

is set equal to 1 when time periods t coincide with compounding ones, or zero,

=

t

1+I

B

R

NP V

8t

t

t

(72)

678

The cumulative cash ow,

679

a;t

680

73). Precisely, the partial income results from extracting the expenses derived from out-

681

sourcing the residues for external treatment to the income obtained from both steam sales

682

and waste treatment.

f low a;t

op

and maintenance

mai a;t

, is the sum of cumulative actualized capital , operating cap a;t

costs plus an additional term, so-called partial income

ni a;t

=

f low

a;t

cap

op

a;t

a;t

mai a;t

8a; t

+

ni a;t

683

The cumulative actualized capital costs

684

purchased equipment to the value of the previous period,

cap

a;t

1

a;t

a;t

=

t

cap

a;t

na;t Ct k

k

1

2 +

X

t

+ n

r a;t

(73)

can be obtained by adding the cost of newly cap

cap

(Eq.

i nia;t;s Ct;s

+

C

oxc

s

C

r t

+ n

oxy a;t

(Eq. 74):

t

8a; t

(74)

685

where C , C , C and C

686

and oxy-combustion upgrades, respectively. The cumulative actualized operating costs

687

are similarly obtained from the value of previous period

688

ments of the current period t. All costs that are not directly in uenced by optimization

689

are expressed as a function of incinerated mass through parameter C

690

sumption of natural gas, oxygen and water is part of the model's decision variables and is

691

then considered separately with cost parameters

692

75). Maintenance costs are instead expressed simply as multiplication of the amount of

i

k

r

oxc

t;s

t

t

t

represent the cost of internal tanks of size s, trucks, rail wagons

35

gas

Ct

,

t

oxy

Ct


op a;t

1 and all operational require-

and

OP u;w;t

, whereas the con-

Ctwater ,

respectively (Eq.


693

Page 36 of 63

incinerated waste by the corresponding cost parameter C (Eq. 76). M

op

a;t

=

t

op

a;t

1

j(

mai a;t

=

t

t

j(

gas

OP Cu;w;t minc u;w;t

2

a;u)

u

mu;t

2

a;u)

X

2 +

X u

C

+m

gas

oxy

t

u;t

C

oxy t

+m

water u;t

U Aa;u

mai a;t 1

X

2 +

t

j(

u

+

U Aa;u ;w

water t

8a; t

(75)

8a; t

C M minc u;w;t

2

a;u)

!

C

!

(76)

U Aa;u ;w

694

The last term of the cash ow expression (Eq. 73), , is obtained as sum of income from

695

heat recovery and waste treatment, minus the expenses for outsourcing residues to other

696

incineration or cement plants as listed in Eq. 77. The revenue from heat recovery can

697

be calculated by multiplying its period-speci c price

698

whereas the income from treatment of external residues are expressed as multiplication

699

between treatment price P

700

for treating their waste but have access to a preferential price

701

treatment, the shipping party has to pay transport expenses in addition to treatment price.

702

Hence, outsourcing costs can be obtained by multiplying the amount of shipped waste with

703

the sum of treatment price and the distance-depending costs associated to the corresponding

704

transportation activity. Such transport costs are described by parameters C

705 706

ni

a;t

T EAB , Ce;w;t

w w;t

heat Pa;t

and the generated heat

rec a;t ,

and the shipped amounts. Internal customers also have to pay site . Pa;w;t

Concerning external

T EI e;i;w;t

,C

T EU e;u;w;t

and

representing shipments to internal tank slots i, incineration units u and outside the

system boundaries, respectively. As already explained in section 2, outsourcing is assumed

36


Page 37 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

707


to only occur from external tanks e. ni a;t

=

t

ni a;t

1

2 +

t

j(

U Aa;u ;w

2

a;u)

j(

j(

2

me;u;w;t

2

a;u) = U Aa;u ;w

Ce;i;w;t

+P

Ce;u;w;t T EU

I Aa;i ;w

w w;t

+P

w w;t

8a; t

T EAB mEAB Ce;w;t e;w;t

+

P site Me;w;t Pa;w;t

EAa;e ;w

T EI

a;i

+

w mtrei Pw;t e;i;w;t

j( )2

2

!

X

2

a;e)

I Aa;i ;w

X

j(

treu

trgi

w mg;i;w;t Pw;t

a;e) = EAa;e ;i

a;i = I Aa;i ;w

EAa;e ;u

a;e)

me;i;w;t

a;i

j(

+

trei

X

2

j(

+

e

X

X e

j( )2

a;e)

j( )2

e w mtreu Pw;t e;u;w;t

EAa;e ;i

j(

e

X

+

U Aa;u ;w

2

a;e)

e

trgu

X

j(

e;u

heat a;t

w mg;u;w;t Pw;t

2

a;u)

P

g;i

X g;u

rec

a;t

(77)

EAa;e ;w

for the cumulative actualized cash ow is enforced by Eq. 78 with the

708

A lower bound

709

aim of distributing investments between multiple periods in order to obtain a clear priority

710

order for them. Similar applications of budget constraining have already been reported in

711

works investigating the capacity planning of power generation in developing countries 27 .

min a

f low

a;t

>

8a; t

min a

(78)

712

Finally, the net present value

713

at the end of the last time period t , as presented in Eq. 79. The total net present value

714

a

is obtained for each plant a from the cumulative cash ow end

tot

is then calculated as sum of the values for single plants a (Eq. 80). a

tot

= =

f low a;t t=t

end

X

8a

a a

37


(79) (80)


715

Page 38 of 63

3.6 Objective functions

716

The complexity of the network and diverging perspectives of the dierent stakeholders makes

717

dicult to express an overarching objective in the form of a single mathematical function.

718

Therefore, it has been decided to de ne a set of dierent objective functions with the aim

719

of assessing the implications of contrasting goals for the major actors of the network, as

720

presented in Table 2. Table 2: Objective functions and associated variables. Objective Variable Description OF1 1 Maximizing the total net present value for incineration sites. OF2 2 Maximizing the total amount of incinerated waste. OF3 3 Minimizing the total amount of transported waste. OF4 4 Maximizing the net present value for the whole network.

721

Unit Swiss Francs [CHF] [kg] [kgkm] Swiss Francs [CHF]

The rst objective function (OF1 ) represents the perspective of incineration sites and sets

722

the goal of maximizing the total net present value

723

(Eq. 81). The interests of waste-generating companies without treatment facilities are not

724

taken into account in this objective function. They are simply considered as waste sources

725

and their preference to ship their residues to a certain site, e.g. for cost reasons, is not

726

considered. 1

727 728

=

tot

for incineration and cement plants

tot

(81)

The second objective function (OF2 ) consists of maximizing the treatment of waste within the system boundaries (Eq. 82), thus fostering a local use of waste as a resource. 2

=

X

minc u;w;t

u;w;t

38


(82)

Page 39 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


729

Although such objective might already reduce the shipment of hazardous residues, the im-

730

plications of a minimization of waste shipments can only be completely assessed with a

731

dedicated objective function. For this reason, a third objective function (OF3 ) minimizing

732

the amount of transported waste, expressed as multiplication of the amount of shipped waste

733

mtr

with the corresponding transport distance D, is de ned as shown in Eq. 83. 3

X

=

trgi

+

T GI mg;i;w;t Dg;i

trgu

T GU mg;u;w;t Dg;u

X

+

g;u;w;t

g;i;w;t

X

X

+

me;i;w;t De;i trei

T EI

X

+

g;w;t

me;u;w;t De;u treu

T EU

e;u;w;t

e;i;w;t

mGAB DgT GAB g;w;t

X

+

mEAB DeT EAB e;w;t

(83)

e;w;t

734

A last objective function (OF4 ) is de ned with the aim of investigating the role of dierent

735

revenue distribution models across the supply chain network. In this sense, the perspective

736

of waste-generating companies without treatment capability is included through variable !

737

(Eq. 84).

g

4

=

X

tot

(84)

!g

g

738

The variable ! represents the net present costs that companies of region g have to bear for

739

shipping their residues as a part of treatment outsourcing. Waste exports also comprise cost

740

for treatment services, since these represent an economic loss for the investigated system as

741

whole, and in order to partially account for additional customs and noti cation expenses.

742

!g

743

actualized costs

g

can be obtained similarly to outsourcing costs for incineration plants with the cumulative reg g;t

!g reg

g;t

for each region g, as presented in Eqs. 85 and 86. = =

g;t t=t reg

t

8g

end

reg

g;t

1

2 +

(85) X

t

trgi

T GI mg;i;w;t Cg;i;w;t

+

g;i;w

X

trgu

T GU mg;u;w;t Cg;u;w;t

+

g;u;w

X

T GAB mGAB Cg;w;t g;w;t

w

39


!

8g; t

(86)


744

4

Results and discussion

745

The developed optimization model has been applied for the ve-year time horizon 2010-2014

746

to the Swiss industrial waste-to-energy network, consisting in ve incineration plants located

747

in integrated chemical complexes, six cement plants and seven regions grouping all chemical

748

sites without any treatment facility (Fig. 3). Every location is connected to each of the

749

other ones by both rail and road. Information about waste composition and properties has

750

been directly retrieved from incineration plants, if available, or estimated with the method

751

proposed by Bolis et al. 17 , otherwise. For modelling purposes, the hazardous liquid residues

752

considered in this study are divided into twelve classes representing waste streams of similar

753

properties and composition, as presented in the supporting information. One of the most

754

signi cant properties of each class is the capacity of self-sustaining its combustion, which

755

depends on both heating value and water content. High-calori c residues are hereinafter

756

de ned as combustibles, whereas the remaining ones, requiring either auxiliary fuels or the

757

simultaneous incineration of additional combustible waste to sustain their incineration, are

758

referred to as mother liquors.

40


Page 40 of 63

Page 41 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 3: Schematic overview of the Swiss industrial waste-to-energy network as considered in this study 21,28,29 , with the seven regions g and incineration sites a, consisting of ve chemical sites with integrated incineration facilities and six cement plants. 759

For validation purposes, the methodology has been rst tested with the separated opti-

760

mization of four single incineration plants for both a weekly and a yearly time resolution,

761

with the aim of checking if they are able to reproduce the historical industrial operation.

762

By comparing the results obtained under these two resolutions with the historical operation,

763

such procedure allows to obtain an insight into trade-os between computational eort and

764

result precision. This is especially important considering the size of the multi-site problem,

765

which is signi cantly larger than single-site ones and presumably requires a certain number

766

of simpli cations. Next, the validated optimization model has been applied to an industrial

767

case study comprising the whole multi-site Swiss network. Both validation and multi-site

768

case studies are formulated as mixed-integer linear problems (MILP) according to section

769

3 in GAMS and solved with CPLEX 12.6.1 on an Intel Core i7-4790 machine (3.60 GHz

770

CPU, 24.0 GB RAM). All results (single- and multi-site) are shown in aggregated form for 41



771

772

Page 42 of 63

con dentiality reasons.

4.1 System-speci c assumptions

773

In addition to the modelling assumptions discussed in section 3.1, it has been necessary to

774

introduce the following ones, speci c to the investigated system:

775

Cement plants are considered as single furnaces without storage facilities, as in reality

776

these are mainly used for buering purposes. Such plants can only accept a xed

777

fraction of CONO combustible residues as substitution fuel, with its total amount as

778

the only constraint. In particular, no limitation is considered for the fumes capacity

779

(imposed with Eq. 27 for each other type of incineration unit). All cement plants

780

are considered as suitable for using such hazardous residues as substitution fuels in

781

their production processes. The modest availability of suitable waste residuals already

782

ensures that the maximum substitution ratio is never exceeded.

783

For con dentiality reasons, it has been agreed with the involved industrial partners to

784

use the same economic parameters for the whole system 23 . Therefore, no plant-speci c

785

costs or prices are considered for this case study. Concerning the economic evaluation,

786

cement plants are then modelled as incineration plants.

787

Since the maximum volumetric fumes throughput is based on average gures, every

788

incineration unit is considered as operational in all time periods. Thus, the value of

789

binary variable bu

u;t

is xed to 1 as listed in Eq. 87. buu;t

790

= 1

8u; t

(87)

4.2 Single-site optimization: validation and rst considerations

791

As previously explained, the methodology has been rst tested with four single-site case

792

studies, representing four Swiss integrated chemical sites and the companies that operate 42


Page 43 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


793

their incineration facilities. Besides residues stemming from their corresponding incineration

794

site, the entire pool of waste streams produced in companies without treatment facilities

795

is considered as available to each single site in order to investigate possible implications of

796

an optimized solution. Non-treated residues are then assumed to be exported outside the

797

system.

798

4.2.1 Comparison between weekly and yearly resolution

799

Concerning validation, the most important data available for comparison is the total amount

800

of treated waste. Other information about waste management is not provided for all incin-

801

eration sites, thus preventing a more rigorous validation of each result obtained with the

802

model. For this reason, considering that this publication focuses on a multi-site perspective,

803

the validation of the proposed methodology is based on the total amount of treated waste.

804

In order to investigate the eect of dierent time resolution on the result precisions, the

805

methodology has been applied to the same ve-year horizon 2010-2014 for both weekly and

806

yearly periods. A model run with capacity expansion (CE) and a second one without allowed

807

investments have been performed for the two time resolutions under dierent objectives, giv-

808

ing the results shown in Fig. 4. This allows to assess the impact of dierent resolutions on

809

both the amount of incinerated waste and the typology of investments predicted by the

810

model.

43



20

Treated waste relative to historical data [%]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Weekly resolution Yearly resolution

15

10

5

0

-5

OF1 with CE

OF1 without CE

OF3 with CE

OF3 without CE

Figure 4: Comparison between weekly and yearly resolution in terms of total amount of treated residues, relative to historical data, in time horizon 2010-2014 under economic and transportation objectives, with allowed and not allowed capacity expansion (CE). Aggregated results for separated single-site optimization of the four aforementioned incineration plants. (OF1 : max incinerators NPV; OF3 : min transportation; more details in Table 2). 811

From this gure, it is possible to observe that, compared to the transportation objective

812

OF3 , the economic objective function OF1 leads to much lower amounts of treated waste.

813

This can be explained by considering the correlation between heating value and the stoi-

814

chiometric amount of air required for a complete waste combustion. In fact, highly calori c

815

residues, mainly composed of waste solvents, require more combustion air due to their larger

816

organic fraction compared to aqueous residues, which have a signi cantly lower energy con-

817

tent. For a given volumetric capacity for the incineration fumes, it is then possible to choose

818

between dierent combinations of waste streams with dierent heating value and air de-

819

mand. Such exibility is further enhanced by a relatively large range of allowed operational

820

temperatures for most incineration units. Therefore, an economic objective will prefer, up

821

to a certain extend, highly energetic waste streams because of their advantage in terms of

822

generated steam, resulting in less treated residues.

823

By considering the cases without allowed investments, it is possible to observe that the

824

obtained results are comparable to the historical operation, spacing from 4 % less to 9 % more 44


Page 44 of 63

Page 45 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


825

incinerated waste for objective function OF1 and OF3 , respectively. The model can then be

826

considered as validated for the scope of this work, since it produces plausible results for both

827

weekly and yearly time resolutions. Moreover, the long-term perspective of this study does

828

not necessarily require the precision of a detailed incineration schedule, which could probably

829

not be achieved as this would signi cantly increase both size and complexity of the problem.

830

Such results are also in line with typical goals of the incinerators, which frequently adopt

831

strategies aiming at maximizing both the economic performance and the internal incineration

832

of their own residues, thus minimizing costs and shipments due to treatment outsourcing,

833

and ideally positioning themselves somewhere between OF1 and OF3 .

834

As expected, the amount of incinerated residues presents higher values for the model runs

835

with allowed investments compared to the ones without capacity expansion. This dierence

836

is accentuated when using a transportation objective, since in such cases investments are

837

not driven by their pro tability and have only a positive eect on the objective function. In

838

terms of incinerated waste, these gures indicate a potential improvement due to capacity

839

expansion up to about 8 and 13 % for OF1 and OF3 , respectively, depending on the chosen

840

time resolution. Nonetheless, it has to be remarked that single-site case runs do not present,

841

because of their own nature, any competition for the limited availability of the most valuable

842

waste streams, so the actual improvement potential is presumably lower and shall be further

843

investigated with a full multi-site case study.

844

The dierence between weekly and yearly time resolution is less than 5 % with respect

845

to the historical operation for transportation objective function OF3 without capacity ex-

846

pansion, and much lower in the other cases, indicating that a yearly resolution leads to

847

comparable results to the ones obtained with a more accurate weekly representation. How-

848

ever, it is plausible that possible waste management issues arising from oscillations in waste

849

production are not fully captured by the yearly resolution. In particular, inconstant amounts

850

of generated residues might lead, during certain weeks, to capacity constraints for both tem-

851

porary storage and treatment. In contrast to the transportation objective, usually giving 45



852

all possible investments, the results obtained under OF1 are more likely to be aected by

853

the chosen resolution for what concerns predicted investments. For this reason, both oxy-

854

combustion upgrades and other storage investments have been compared between weekly and

855

yearly resolution as listed in Table 3. The corresponding computational times can instead

856

be found in Table 4. Table 3: Comparison between weekly and yearly resolution in terms of obtained investments, in time horizon 2010-2014 under objective functions OF1 and OF3 (Table 2). Total additional capacity obtained from the separated optimization of the four investigated single-site cases. The maximum possible total number of oxy-combustion upgrades in the investigated singlesite systems is 6. Objective Resolution Oxy-combustion upgrades [-] Weekly 6 OF1 Yearly 6 Weekly 6 OF3 Yearly 6

Additional installed Additional truck and tank capacity [m3 ] wagon capacity [m3 ] 0 0 0 0 1140 210 1200 210

Table 4: Average computational time for the separated optimization of the four single-site cases, in time horizon 2010-2014 under objectives OF1 and OF3 (Table 2). Objective Resolution Computational time [s] Weekly 1049 OF1 with capacity expansion Yearly 2 Weekly 22999 OF3 with capacity expansion Yearly 2 857

Although these results show comparable investments for weekly and yearly resolution

858

over the investigated ve-year horizon, it has to be remarked that a higher resolution still

859

provides a more detailed investment plan as these are allowed to occur more frequently (every

860

year quarter). Additionally, in some cases, their order does not match perfectly between the

861

two resolutions, indicating a slightly dierent prioritization depending on short-term condi-

862

tions, such as a peak generation of certain residues. Nevertheless, such minor improvements

863

require massively longer computational times, which become of special concern for larger op46


Page 46 of 63

Page 47 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


864

timization problems. In fact, this might prevent the application to more complex multi-site

865

case studies, as well as future uses of both multi-objective and uncertainty tools. Therefore,

866

it can be concluded that using a yearly resolution represents a reasonable compromise for

867

the multi-site and long-term perspective considered in this study.

868

4.2.2 Preliminary assessment of dierent objectives

869

In addition to validation purposes, results of model runs with yearly resolution have been

870

further analysed with the aim of obtaining a rst insight into possible implications of di-

871

verging objectives. Even though a direct and rigorous comparison of results, in particular

872

for transportation aspects, between single- and multi-site is not possible because of intrinsic

873

limitations, such as the absence of competition for the former, it might still be interesting

874

to confront some results. In this sense, it is useful to refer to average values per amounts of

875

treated waste for a simpler comparison between dierent case studies or objective functions.

876

Fig. 5 shows selected results obtained under the diverging objectives OF1 , OF2 and OF3 .

877

Objective function OF4 has not been used for single-site runs, since it focuses on purely

878

network aspects. For similar reasons, shipment-related results are only discussed for multi-

879

site optimization. Among the investigated objectives, OF1 leads to the highest economic and

880

energy recovery bene ts per amount of treated waste, while globally resulting in the lower

881

quantity of incinerated residues. This can again be explained with the fact that high-calori c

882

residues, more interesting from an economic perspective, require a higher amount of air for

883

a full combustion, implying that less waste can be treated for a given fumes capacity. A

884

similar eect can be observed by comparing the results of OF2 and OF3 : for similar values

885

of energy recovery per amount of treated waste, objective OF2 , maximizing the amount of

886

incinerated residues, obviously leads to more treated waste but also provides less pro t per

887

incinerated residues, as it prefers aqueous waste streams due to their low air demand.

888

Even though oxy-combustion allows for treating more waste, these additional amount

889

mainly consists of aqueous residues, thus resulting in less pro t and energy recovery per 47



890

amount of incinerated waste for all the investigated objective functions. This is a consequence

891

of temperature constraints, which require to compensate the decreased heat absorption eect

892

of nitrogen. On the one hand, a lower nitrogen amount in the fumes, due to the partial

893

substitution of combustion air with pure oxygen, increases the incineration capacity for

894

a given fumes throughput. On the other hand, a smaller nitrogen quantity imposes that

895

such additional waste streams must release less energy than the one required to heat their

896

resulting fumes up to the allowed operational temperature. Nevertheless, investments in

897

such upgrades are still obtained under economic objective OF1 , implying that they have a

898

bene cial eect in absolute terms. In this sense, it is worth pointing out that such outcome

899

is presumably aected by the duration of the investigated horizon, shorter than the lifetime

900

of the equipment to be purchased, and by the assumption of using the same price, or value,

901

for all waste types.

48


Page 48 of 63

Page 49 of 63


25 with capacity expansion without capacity expansion

20

15

10

5

0

-5

OF1

OF2

OF3

(a) Total amount of treated residues relative to historical operation. 0.40 with capacity expansion without capacity expansion

0.35

Profit / Treated waste [CHF/kg]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.30 0.25 0.20 0.15 0.10 0.05 0.00

OF1

OF2

OF3

(b) Actualized pro t per amount of treated waste.

Figure 5: Comparison between objectives OF1 , OF2 and OF3 for time horizon 2010-2014 with yearly resolution. Single-site optimization of the four aforementioned incineration plants, with allowed and not allowed capacity expansion. (OF1 : max incinerators NPV; OF2 : max treated waste; OF3 : min transportation; more details in Table 2). 902

By comparing results of single-site optimization, the investigated investment opportuni-

903

ties in capacity expansion show an optimization potential of 6-7 % in terms of the amount

904

of treated residues, depending on the selected objective. Among the considered possible

905

purchases, oxy-combustion upgrades to existing incineration units seem the most promising

906

ones. Speci cally, other investments are not obtained under the economic objective OF1 49



20

Energy recovery / Treated waste [MJ/kg]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

with capacity expansion without capacity expansion

15

10

5

0

OF1

OF2

OF3

(c) Energy recovery (steam) per amount of treated waste.

Figure 5: Comparison between objectives OF1 , OF2 and OF3 for time horizon 2010-2014 with yearly resolution. Single-site optimization of the four aforementioned incineration plants, with allowed and not allowed capacity expansion. (OF1 : max incinerators NPV; OF2 : max treated waste; OF3 : min transportation; more details in Table 2). 907

(Table 3), indicating that oxy-combustion represents most of the improvement potential

908

related to capacity expansion. In any case, the waste management problem need to be fur-

909

ther addressed with a network case study, which also allows for a more complete evaluation

910

including transportation aspects.

911

4.3 Case study: Swiss multi-site network with historical data

912

After validation, the developed methodology has been applied to a full-size multi-site case

913

study covering the Swiss industrial waste-to-energy network for the horizon 2010-2014 with

914

yearly resolution. Originally, waste production was provided on a weekly and yearly basis,

915

depending on the information source for the period 2010-2014. In this case study, the model

916

has been run under all the objective functions listed in Table 2. As previously discussed,

917

the main outcome that can be easily compared to historical operation is the total amount

918

of incinerated waste, which is shown in Fig. 6, together with the same results presented in

919

the analysis of single-site runs. The size and computational eort of the problem with and 50


Page 50 of 63

Page 51 of 63

920

without capacity expansion are provided in the Supporting information.



20

15

10

5

0

-5

OF1

OF2

OF3

OF4

(a) Total amount of treated residues relative to historical operation. 0.40 with capacity expansion without capacity expansion

0.35

Profit / Treated waste [CHF/kg]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.30 0.25 0.20 0.15 0.10 0.05 0.00

OF1

OF2

OF3

OF4

(b) Actualized pro t per amount of treated waste.

Figure 6: Comparison between all objectives. Multi-site optimization of the whole Swiss waste-to-energy network for the horizon 2010-2014 divided in yearly periods, with allowed and not allowed capacity expansion. (OF1 : max incinerators NPV; OF2 : max treated waste; OF3 : min transportation; OF4 : max network NPV; more details in Table 2). 921

From this gure, it can be seen that the total amount of incinerated residues does not

922

present signi cant dierences between objective functions, in contrast to single-site results

923

(Fig. 5). This is caused by the competition between the dierent actors of the network,

924

clearly only present in the multi-site case study. More precisely, there is a limited avail51



20

Energy recovery / Treated waste [MJ/kg]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


15

10

5

0

OF1

OF2

OF3

OF4

(c) Energy recovery (steam or released heat for incineration and cement production, respectively) per amount of treated waste.

Figure 6: Comparison between all objectives. Multi-site optimization of the whole Swiss waste-to-energy network for the horizon 2010-2014 divided in yearly periods, with allowed and not allowed capacity expansion. (OF1 : max incinerators NPV; OF2 : max treated waste; OF3 : min transportation; OF4 : max network NPV; more details in Table 2). 925

ability of the most interesting waste streams, whose nature depends on the chosen objective

926

function. As previously observed, economic goals focused on incinerators seem to prefer

927

high-calori c residues, while objectives involving both minimization of shipments and max-

928

imization of waste treatment tend to choose more mother liquors because of their lower air

929

demand. In this sense, as the optimization is performed separately, single-site runs are more

930

free to choose both type and amount of external residues, consequently leading to more dif-

931

ferences between objectives. Similarly, the total amounts of incinerated waste obtained with

932

multi-site optimization is either considerably lower, for OF2 and OF3 , or much higher, when

933

OF1 is used. These gures con rm that the diverging nature of single- and multi-site sys-

934

tems, especially in terms of competition, prevents a full comparison aimed at a quantitative

935

assessment of bene ts arising from a more coordinated network management. Nonetheless,

936

such comparison still allows for a better understanding of the dynamics of the investigated

937

systems, as well as for a qualitative evaluation of possible synergies. Furthermore, the multi-

938

site optimization favors a better utilization of waste since the addition of water and auxiliary 52


Page 52 of 63

Page 53 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


939

fuel is avoided in all solutions for OF1 and OF3 , both in cases without and with capacity

940

expansion (refer to Supporting Information).

941

Concerning the actualized pro t per amount of treated waste, the results show a similar

942

trend between single- and multi-site optimization, with OF1 giving the best value, followed

943

by OF3 and OF2 . In general, the pro t obtained under all objectives is comparable between

944

the two cases, but the multi-site one presents more levelled values. In fact, compared to

945

single-site results, OF1 gives slightly lower values, whereas OF2 leads to a higher pro t per

946

amount of treated waste. OF3 results more or less in the same values obtained with single-

947

site optimization. A lower value for OF1 has indeed been expected from the introduction of

948

competitive dynamics with the multi-enterprise system, but it is somehow remarkable that

949

the limited availability of certain waste classes can lead to such similar pro t for strongly

950

diverging objectives. Such gures highlight the important fact that a more sustainable

951

management aimed at a more local waste treatment does not require particular economic

952

sacri ces, presumably thanks to a consequent reduction of shipment costs. Moreover, the

953

pro t obtained with OF1 is somehow unrealistic since it does not consider the perspective

954

of waste providers. The actual value would be with any probability considerably lower, as

955

suggested by OF4 results that do take such perspective into account.

956

In any case, it would be necessary to develop a strong exchange platform between the

957

network actors, communicating current waste levels and free incineration capacity, to achieve

958

the full potential of a more coordinated management. The poor information ow currently

959

represents the main barrier against the implementation of network synergies potentially

960

leading to economic, environmental and safety bene ts. The need of a continuous commu-

961

nication can already be observed at site level, where imprecise and outdated waste forecasts

962

lead to quite frequent adaptations to the waste treatment plan, resulting in a sub-optimal

963

management.

964

The average energy recovery per waste amount shows very similar values under all ob-

965

jective functions, in contrast to the ones obtained with single-site runs. The former also 53



966

present signi cantly larger energy recovery results, as expected from the inclusion of ce-

967

ment plants and competition in the multi-site case study. In fact, compared to dedicated

968

incineration plants, these enable a better energy recovery eciency of the residues used as

969

substitution fuel, since the released heat is directly utilized in the kiln for the production of

970

cement instead of being converted into process steam. Moreover, they considerably increase

971

the total treatment capacity for combustible waste, compared to the sole incineration facil-

972

ities in integrated chemical complexes. In this sense, the overall fraction of mother liquors

973

in the incinerated waste is lower than the one resulting from single-site optimization, even

974

though their total amount remains more or less constant. Emission limits, often based on dry

975

composition, signi cantly constrain the increase in incineration capacity for such aqueous

976

residues through oxy-combustion upgrades. In any case, it must be remarked the considered

977

amount of waste suitable for thermal valorization in cement plants is based on the approxi-

978

mation of Bolis et al. 17 . Thus, the actual compatibility of such waste streams as substitution

979

fuels should be further checked to better evaluate the potential contribution of the cement

980

industry.

981

In addition to the results presented in Fig. 6, a multi-site perspective allows for a more

982

complete assessment of transportation aspects. Fig. 7 shows the average transportation

983

distance per amount of waste treated in the system obtained under dierent objective func-

984

tions. The average distance per generated residues, shown in sub- gure (a), can be simply

985

obtained by dividing the value of variable

986

consider the average distance for the waste treated within the system boundaries, presented

987

in sub- gure (b). From both graphs, it can be seen that the objective functions can be di-

988

vided in two distinct groups giving similar results, as especially noticeable for what concerns

989

the average distance of the residues treated in Switzerland. As expected, objectives that are

990

negatively in uenced by all types of shipments result in much lower transport distances than

991

values obtained under OF1 and OF2 . Nonetheless, as previously discussed, the remarkable

992

potential in shipment reduction, obtained by comparing results from the minimization of

3

by the one of 2 , but it is also interesting to

54


Page 54 of 63

Page 55 of 63

993

transportation to OF1 , does not imply particular sacri ces in terms of pro t per amount of

994

treated waste since all functions present similar values.

Transport distance / Treated waste [kg km/kg]


800

600

400

200

0

OF1

OF2

OF3

OF4

(a) Average transportation distance, including exports, per amount of waste treated in Switzerland. 150

Transport distance / Treated waste [kg km/kg]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60



125

100

75

50

25

0

OF1

OF2

OF3

OF4

(b) Average transportation distance (residues treated within the system boundaries) per amount of waste treated in Switzerland.

Figure 7: Comparison of transportation aspects between all objectives. Multi-site optimization of the whole Swiss waste-to-energy network for the horizon 2010-2014 divided in yearly periods, with allowed and not allowed capacity expansion. (OF1 : max incinerators NPV; OF2 : max treated waste; OF3 : min transportation; OF4 : max network NPV; more details in Table 2). 995

Another important comparison term between the dierent objectives is represented by 55



996

the type of investments predicted by the model (Table 5). In general, the obtained in-

997

vestments show a similar trend to single-site results. Whereas the maximum number of

998

oxy-combustion upgrades is obtained under every objective function, purchases of storage

999

equipment are limited to the non-economic objectives, namely OF2 and OF3 . As there is

1000

not a signi cant dierence with the other objective functions (Fig. 6), it can be concluded

1001

that investments in storage capacity only result in limited bene ts in terms of treated waste.

1002

Shipments seem mainly decreased by investing in oxy-combustion, since improvements due

1003

to capacity expansion show comparable results under all objectives, implying that the limited

1004

incineration capacity act as the bottleneck of the waste-to-energy network. Nevertheless, the

1005

increasing focus of the chemical and pharmaceutical industry in life science and biotechnol-

1006

ogy sectors indicates a possible growing importance of process steps performed in aqueous

1007

media. In this sense, it is reasonable to expect a consequent change in the waste composition,

1008

with an increment in the fraction of mother liquor residues. Such variation has already been

1009

observed by some research partners in the last years 23 . On the one hand, an increased share

1010

of mother liquor waste could potentially lead to storage issues arising from technical limita-

1011

tions for the mixing of aqueous residues. On the other hand, their combustion requires less

1012

combustion air, thus potentially enabling to treat more waste with the given incineration ca-

1013

pacity. In such future perspective, storage investments might be more meaningful but their

1014

eectiveness should be further evaluated with additional case studies involving uncertain

1015

conditions. This would also allow a better assessment of the robustness of oxy-combustion

1016

upgrades, which should theoretically play a more relevant role in case of an increased frac-

1017

tion of mother liquor residues. Indeed, as previously explained, an incremented treatment

1018

capacity due to oxy-combustion is mainly limited to aqueous residues because of their lower

1019

energy content. Nevertheless, such upgrades might also be limited in their application by

1020

emission regulations as these are often based on a dry fumes composition.

56


Page 56 of 63

Page 57 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 5: Comparison of obtained investments, in time horizon 2010-2014, under dierent objective functions (Table 2). Total additional capacity obtained from the optimization of the whole Swiss industrial waste-to-energy network with yearly resolution. The maximum possible number of oxy-combustion upgrades in the investigated system is 6. (OF1 : max incinerators NPV; OF2 : max treated waste; OF3 : min transportation; OF4 : max network NPV; more details in Table 2). Objective Oxy-combustion upgrades [-] OF1 6 6 OF2 OF3 6 6 OF4

Additional installed Additional truck and tank capacity [m3 ] wagon capacity [m3 ] 0 0 1020 350 1140 360 0 0

1021

The strong dierence in terms of predicted shipments, together with the fact that in-

1022

vestments in additional storage capacity are as likely to be good as bad depending on the

1023

chosen objective function, indicate that multi-objective tools could be useful for identifying

1024

possible trade-o solutions between economic and transportation objectives. This is of par-

1025

ticular importance considering the increasing safety concerns about shipments of hazardous

1026

waste 18 , whose associated risk is intrinsically linked with both amount and distance of the

1027

transported residues.

1028

5

Conclusions

1029

This work proposes a novel methodology for the optimization of industrial waste-to-energy

1030

networks, combining logistic, site management and process elements into a single mathe-

1031

matical formulation. The model is formulated as multi-period mixed-integer linear problem

1032

(MILP) with discrete time representation and includes the possibility of investing in addi-

1033

tional storage means, as well as in oxy-combustion upgrades. After validation with real data

1034

stemming from industrial and institutional partners, the developed model has been applied

1035

to a complex multi-site case study consisting of the whole Swiss industrial waste-to-energy

1036

network. This covers the ve-year horizon 2010-2014, using historical data and a yearly 57



1037

resolution. A set of objective functions allows for investigating the implications of diverging

1038

goals in terms of investments, shipments and energy recovery.

1039

Results highlight the importance of both investments in oxy-combustion and the role of

1040

the cement industry, which increase the treatment capacity for mother liquor and combustible

1041

residues, respectively. Speci cally, a systematic planning for the distribution of residues

1042

between incineration and cement plants seems to be the key factor for increasing both the

1043

amount of incinerated waste and the consequent energy recovery, as well as to diminish

1044

transport distances. In contrast to oxy-combustion, investments in additional storage means

1045

are only obtained under certain objectives, indicating that the limited bene ts associated

1046

to them are presumably situational and arising from an increasing exibility concerning

1047

waste mixing. Nevertheless, the importance of a more exible temporary waste storage

1048

and oxy-combustion might be considerably aected by possible future changes in waste

1049

amounts and composition. Therefore, further studies should consider uncertain conditions

1050

for a more complete evaluation of the dierent investment opportunities. In this sense,

1051

longer time horizons would also allow to better assess the long-term eects of investments.

1052

Furthermore, purchases of storage equipment are only obtained with half of the objective

1053

functions, suggesting that multi-objective tools could be applied to investigate possible trade-

1054

o solutions.

1055

The comparison between the solutions obtained under dierent goals shows a signi cant

1056

improvement potential in terms of shipped waste, which would result in important environ-

1057

mental and safety bene ts, together with a decrease of transport expenses. Nonetheless, it

1058

is worthy to remark that the predicted improvements should be considered as upper bounds,

1059

since they assume a perfect information ow between all actors in the network. A common

1060

platform for information exchange would thus be required to achieve most of the bene ts

1061

arising from a synergistic management of the network.

58


Page 58 of 63

Page 59 of 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1062


Acknowledgement

1063

The authors thank the companies Cimo Compagnie industrielle de Monthey SA, Dottikon

1064

Exclusive Synthesis AG, Holcim Schweiz AG, Infrapark Baselland AG, Lonza AG and the

1065

Swiss Federal Oce for the Environment for supplying data and their support in technical

1066

questions. The members of the advisory board of the project 23 are acknowledged for their

1067

feedback on both assumptions and results of the optimization model. Sincere gratitude is

1068

also dedicated to Christoph Hugi, Corinna Baumgartner, Cynthia Muller and Paolo Ferrara

1069

of the Fachhochschule Nordwestschweiz for their collaboration on the economic evaluation.

1070

This research project is part of the National Research Programme \Energy Turnaround"

1071

(NRP 70) of the Swiss National Science Foundation (SNSF). Further information on the

1072

National Research Programme can be found at www.nrp70.ch.

1073

1074 1075 1076

1077 1078 1079

1080 1081

1082 1083

1084

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For Table of Contents Only.

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Optimal Design and Management of Industrial Waste-to-Energy Systems

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