Technology Updating Decisions for Improving the Environmental

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Technology updating decisions for improving the environmental performance of an operating supply chain: A multiobjective optimization model for the cement industry Nora Cadavid-Giraldo, Mario Cesar Velez-Gallego, and Gonzalo Guillen-Gosalvez Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.6b01083 • Publication Date (Web): 14 Oct 2016 Downloaded from http://pubs.acs.org on October 18, 2016

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Industrial & Engineering Chemistry Research

Technology updating decisions for improving the environmental performance of an operating supply chain: A multi-objective optimization model for the cement industry Nora Cadavid-Giraldo,∗,† Mario César Vélez-Gallego,† and Gonzalo Guillén-Gosálbez‡ †Departamento de Ingenier´ıa de Producción. Universidad EAFIT. Colombia ‡Centre for Process Systems Engineering. Imperial College London. UK E-mail: [email protected] Abstract The obsolescence and related energy inefficiency of many industrial processes is at present an obstacle in the transition towards a more sustainable manufacturing sector. The aim of this work is to exploit the advantages of technology updating for improving the economic and environmental performance of the cement industry. To this end, we have developed a mathematical approach to optimize technological updating decisions of existing cement supply chains. The approach is based on a multi-objective mixed-integer linear programming formulation that determines whether technological updating projects should be undertaken at a given manufacturing stage such that both

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environmental and financial goals are best met. A test instance based on a cement supply chain is solved to illustrate how significant benefits can be attained by applying our systematic approach.

Introduction In today’s markets, attaining sustainability goals requires implementing significant advances in production techniques. Unfortunately, even implementing the current best available techniques can become a challenge for firms and supply chain managers. By definition, best available technologies (BAT) correspond to the existing “most effective and advanced stage in the development of technologies, activities and methods of operation to reduce emissions and the impact on the environment as a whole”. 1 Despite many regulations and public and private efforts, high obsolescence levels still persist in industrial systems. Along these lines, the International Energy Agency (IEA) indicates that the energy intensity of most industrial processes is far higher than its theoretical minimum. 2 Statistics for different industrial sectors presented in Table 1 highlight the clear need for technological improvements and the potential to boost the productivity level by using BAT. 3–6 Table 1: Energy reduction potential for several common materials. Sector Potential energy use reduction (EJ) Chemicals 5.2 Iron and Steel 4.7 Cement 2.5 Pulp and paper 1.4 – 2.4 Glass 0.6 Aluminum 0.4 Petrochemical n.a n.a not available

Percentage reduction % 15 20 30 n.a 35 – 40 13 10

Technology updating for increasing efficiency in industrial processes has been recently integrated within the concept of supply chain management (SCM). SCM covers material 2


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sourcing, manufacturing, production allocation, the distribution of final products to customers as well as the end-of-life waste management. 7 In environmental engineering, SCM has been investigated in the area of green supply chain (GSCM), paying special attention to the environmental dimension of the problem. In the chemical engineering literature, supply chain management was first formally addressed by, 8 which introduced the concept of “enterprise-wide optimization” (EWO). EWO provides optimization tools and computational advances that expand the boundaries of the production process analysis, thus making it possible to model decisions that simultaneously involve the plant and network levels. The aim of this research is to provide a multi-objective mathematical model to optimize technology updating decisions so as to minimize the cost and environmental impact in the cement industry. A mixed-integer linear programming model is developed to optimize (i) technology updating choices; (ii) fuel selection; (iii) suppliers selection; and (iv) transportation flows, considering updating, operating, transportation and shut-down costs. The capabilities of the model proposed are illustrated through its application to a cement industry. The remainder of this paper is organized as follows. Multi-objective optimization methods for GSCM are reviewed in the next section. In the section that follows, the cement industry is described. The mathematical model proposed to optimize the cement supply chain is presented next. The results obtained for a test instance are then described, discussing the management implications. The conclusions of the work are finally drawn in the last section of the paper.

Literature review Sustainability was introduced in 1987 by the Brundtalnd report 9 as a new concept to deal with high environmental degradation, population, production and consumption growth. In

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the context of enterprise management, “the triple bottom line”, a term coined in 1994 by John Elkington, 10 incorporates the principles of sustainability by covering “people, planet and profit”. Starting in the 1970s, and even more frequently after 1990, mathematical modeling and operations research tools have been widely used in the area of environmental management. Recently, these tools were integrated into SCM giving rise to the area of GSCM. 11–16 GSCM introduces new management concepts related to environmental aspects to be considered in the supply chain’s practical operation. These include, for instance, how to factor environmental protection into supplier selection, how green purchasing could reduce waste production and resource depletion, and how production allocation could lead to a reduction in transportation pollutant emissions. 11,17,18 Recent and comprehensive reviews of works on GSCM are presented in by Srivastava, Seuring and Muller, Sarkis et al., Burgess et al., Eskandarpour et al., and Branderburg et al. 11–16 We focus here on multi-objective formulations for GSCM, specifically in the context of “planet vs profit”. The economic performance of supply chains can be typically easily quantified via metrics such as cost or net present value. In contrast, the environmental performance requires modeling a wide variety of physico-chemical phenomena with different units, magnitudes, improvement strategies and occasionally opposite behaviors. Expressing environmental concerns through quantitative objective functions is challenging and requires finding a middle ground between computational cost and solution quality. This trade-off arises from the fact that adding more environmental objectives leads to more complex models and therefore higher computational times. Several environmental impacts such as air pollution, water eutrophication, global warming potential, human toxicity and resource depletion can be optimized in GSCM, either separately or by using aggregate environmental damage indicators. Several authors recognize the limitations of employing an aggregated metric and prefer to address each environmental 4


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impact separately, even if this leads to more complex models containing three, four or more objectives. Another strategy to simplify the calculations is to group environmental objectives and eliminate redundant indicators. In Oliva et al., Kosting et al., and Brunet et al., 19–21 a comprehensive review of those objective reduction methodologies is presented, including principal component analysis, 22,23 evolutionary algorithms, 24 clustering techniques, 25 and decomposition or reduction algorithms. 26 These authors own approach relies on solving an optimization problem for finding the best clusters of goals, on the basis of which the optimization task is carried out. 19–21 In Jia et al., 27 the environmental objective function is defined using the analytical hierarchy process, which allows grouping nine environmental impact categories into a single indicator. Finally, based on environmental economic principles, other authors translate the environmental damage into financial costs taking into account the externalities. 28–31 It is possible to create a taxonomy of works based on multi-objective formulations, specifically in reference to the planet vs profit context, considering the level of detail in the modeling exercise and the scope of the analysis. According to the former, mathematical models can optimize decisions at the (i) process, (ii) network, and (iii) integrated systems levels. At the process level, the goal is to improve the environmental performance of a manufacturing process by optimizing technology upgrading projects, production capacities and fuels. At the network level, the aim is to establish the optimal supply chain network; whereas at the system level, both types of decisions (i.e. process and network) are tackled simultaneously. Enterprise Wide Optimization (EWO), 8 for instance, deals with type (iii) problems. According to the scope of the analysis, we find works dealing with either a (i) grassroots design or (ii) a retrofit. Type (i) works design new supply chains yet to established, whereas type (ii) consider the costs and emissions related to any possible change with respect to the current state of the system. Tables 2, 3 and 4 present the most relevant works identified at each level, from 1995 to the present. 5



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Table 2: Process level optimization. Scope

Authors

Financial Objective

Environmental Objective

Ashraf et al., 2008 32

Cost

Total weight of NOx, HC, CO, and PM

Gebreslassie et al., 2009

Design

33

Javidan et al., 2012 34 Jia et al., 2006

27

Wu and Chang, 2004

35

Cost

LCA global indicator

Fuel Cost

Power lost and pollutant emissions

Economic potential

Environmental performance

Cost

Pollution charges and conservation fees

Hu et al., 2006

30

Production costs

Net energy costs, External cost of Environmental Pollutant Emissions

Wang et al., 2013

36

Net present value

GWP emissions and LCA global indicator

Madaloni.,2015 37

Improvement

Brunet et al., 2012

21

Profit

Carbon emissions

Net present value

Three damage cathegories of LCA, selected by PCA methods

Ogbeide, 2010 38 Azapagic et al., 1999

39

Costs

Carbon emissions

Cost


Table 3: Network level optimization. Scope

Authors

Financial Objective


Fahimnia et al., 2015 40

Cost

Carbon emissions, energy consumption, and waste generation

Design Absi et al., 2013

41

Duque et al., 2010 42 Quaraguasi et al, 2009

43

Cost

Carbon emissions

Profit


Costs

Cumulative energy demand, waste generation

Ramudhin et al, 2010

Improvement

Wang et al., 2011

28

31

Costs

Carbon emissions costs

Cost including environmental im-

Carbon emissions

provement investments Zhang et al., 2014 44

Cost

Carbon emissions and a third goal namely lead time

Ubeda et al., 2011

45

Travel distances

6


Carbon emissions

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Table 4: EWO. Scope

Design

Authors

Financial Objective


Bojarski et al., 2009 46

Net present value


Khorsand

Cost

Air pollutant emissions added as a

2010

and

Heydari,

47

single objective

Pinto-Varela et al., 2011 48

Profit


Cost

Carbon emissions

Net present value


Mele et al., 2010 50

Net present value

GWP and LCA global indicator

Guillen

Grossman,

Net present value


Hugo and Pistikopoulos,

Net present value

Carcinogenic emissions, global re-

Miret et al., 2015 Guillen

Improvement

and

29

Grossman,

2010 49

and

2009 51

2005 52 This work

sources depletion Cost

CO2 , N Ox and SOx emissions

Here we develop a model tailored to optimize the economic and environmental performance of the cement industry through the implementation of technology updating projects. Several multi-objective formulations were proposed for SCM. However, they typically model the manufacturing process as a single entity instead of focusing on its individual sub-systems. Furthermore, they tend to address the grassroots design of supply chains assuming that no facilities already under operation exist. Finally, environmental concerns are typically neglected, or in the best case modeled via a single indicator. The modeling approach proposed here optimizes the implementation of updating technologies (e.g. retrofit actions) in existing supply chains. These technologies can be implemented at different stages in the manufacturing process. Furthermore, several emissions (i.e., N Ox , SOx , and CO2 ) are optimized simultaneously in order to minimize the environmental impact.

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Problem statement: description of the cement industry Concrete is considered to be the second most consumed substance in the world, being water the first. 53 The amount used in construction nearly doubles that of all other building materials, including wood, steel, plastic and aluminum. 53 In relation to other commonly used materials, cement, which is the main component of concrete, has low energy-intensive and carbon footprint. However, due to the very common use of this material, the cement industry consumes approximately 2-3% of the worldwide primary energy, 53,54 and its absolute CO2 emissions represent around 7% of the total amount 53 . 6 During 2009-2011, the annual worldwide cement production was estimated to be 3 Gt 53 . 6 In 2013 it was reported to be 3.7 Gt. 55 Cement consumption is frequently used as a country development indicator given is importance in any economy. The emissions of cement production plants may differ depending on the production technologies, the heating and electricity sources and the mixture of raw materials. Publicly available data indicate that emissions fall within the range 650 to 950 kg of CO2 per ton of cement, depending on the production technologies, cement compositions and energy sources 56 . 6 The Cement Sustainability Initiative (CSI) estimates that by 2050, the cement industry could reduce its global emissions by 18%, starting with the levels of 2009 and considering the future demand forecast. 6 Global and country-specific studies have been published for The Cement Sustainability Initiative, The World Business Council for Sustainable Develement, The European Cement Research Academy and The Lawrence Berkeley National Laboratory, that provide reference roadmaps for emissions reduction. 6,57–62 All of them consider various strategies, including reducing CO2 emissions, increasing energy efficiency and using alternative cementing materials and carbon sequestration strategies. These efforts to create technology-updating road-maps pose practical management decisions for the firms: How should improvement strategies be selected? How do these technical

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changes in the production plants modify the entire supply chain? What are the related costs and the consequent emissions reductions? Systematic tools based on mathematical programming offer an appealing framework to address these questions. The piece of research presented herein was motivated by a particular case study of a cement production company in Latin America that has recently grown through the acquisition of several small firms. This company owns 10 operating production plants with ages ranging from 15 to 50 years and with diverse technical conditions, production capacities, environmental indicators and operating costs. The aim of the firm is to improve its overall environmental performance, measured as the amount of emissions of CO2 , N Ox and SOx released to the atmosphere. To assist in this task, we have developed a mathematical model that considers the initial technological stage of each process in every production plant and the range of updating options available to improve their performance. The system under study is graphically summarized in 1.

Figure 1: General structure of the problem.

A short description of the cement production process is provided next. Each production technology is described in detail, including possible complements, related costs and emissions

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data. This information has been retrieved from different sources based on a wide review of the technical literature of the cement sector. 6,57–62 The data related to plant locations, current operating technologies, production capacities, demand and costs represent a realistic scenario.

Cement production Cement production uses limestone and other carbonate rocks as feedstock. These rocks are quarried, crushed and put into a calcination kiln. In the kiln, the temperature rises to 1450 ◦ C, thereby inducing a chemical reaction that converts the rocks into carbon dioxide and clinker (CaCO3 → CaO + CO2 ). 63 In a mill the clinker is mixed with other additives to obtain cement. 63 Figure 2 provides a sketch of this process.

Figure 2: Cement production process. In older cement plants, crushed limestone and water are mixed to obtain a homogeneous slurry before entering the wet kiln tube. These old kilns need to be long. Here thermal energy is used for both the calcination process and for evaporating the water contained in the slurry. The first technological improvement towards kilns with higher thermal efficiency led to the so-called long dry kilns, which were designed to avoid mixtures with water. The next improvement was the introduction of a tower-mounted cyclone preheater where the calcina10


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tion process begins. Later on, the calcination process was partly transferred to a precalciner attached to the preheater in a preheater-precalciner kiln line. 64 Preheater-precalciner kiln lines constitute the state of the art for the clinker production technology and can be as short as one-fourth the length of a wet kiln tube of similar capacity. 64 Published technical statistics for cement production show energy efficiency improvements of 30 − 40% between a line with a wet kiln tube and another one with preheater-precalciner. 6,56,64 Concerning the burner inside the kiln, the state of the art technology is based on multi-channel burners. 6 This allows the simultaneous use of different types of fuels. This facilitates the re-circulation of hot air and ultimately improve thermal efficiency. 6 Albeit technology advances in clinker production, many long dry kilns (without preheaters and precalciners) and long wet kilns still operate worldwide. 6 Wet kilns can be improved with a preheater, leading to a so-called “semi-dry” system. Long dry kilns can also be equipped with a preheater or both a preheater and precalciner. In that case, the kiln length may be reduced, thus improving the thermal efficiency and reducing the mechanical stress of the kiln shell due to torsion. The retrofit of an old kiln may become attractive when a new kiln line is too expensive. 6 The capacity of modern kilns with a preheater and precalciner can be improved by adding cyclone preheaters in a number that may go from two to six. These multicyclone systems have higher production capacity and lower marginal energy consumption. 6 This sequential set of technical improvements in kiln technologies can lead to significant environmental and financial benefits. Environmental performance improvements of the kiln process might be attained by changing the type of kiln, but also through other strategies. These include a better thermal isolation of the combustion camera, the reuse of heat with air recirculation, the installation of co-generation or control systems, the use of mineral additives (to reduce the calcination temperature) and the installation of emission filters (which enhance energy efficiency). These strategies are referred to as complements in the remainder of this paper. 11



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To produce cement, the nodules of clinker are mixed with gypsum and ground to powder. Grinding is the activity with the highest power demand in cement production. Modern grinding technologies are claimed to use from 20% to 50% less energy than the older ones. Advances in milling technologies are related to the replacement of traditional ball mills (BM) by high-pressure grinding rolls (HPGR) and vertical roller mills (VRM). 6,56 Between 20% and 50% of the power demand in cement production is related to grinding activities, including the grinding of raw materials before clinker production. 58

Energy consumption and environmental impacts The most critical environmental impact of cement production is given by the CO2 emissions. Data reported by Data reported by Humphreys and Ali, 57,65 shows that approximately 40% of the industry’s emissions are due to kiln thermal consumption, whereas 10% is associated with transportation activities and electricity consumption. The remaining 50% of CO2 emissions originate from the process that converts limestone (CaCO3 ) into calcium oxide (CaO), which is the primary precursor of cement. It is chemically impossible to convert CaCO3 into CaO to produce cement clinker without generating CO2 . Hence, there is a large percentage of emissions that can only be avoided by replacing the clinker by another cementing material, such as fly ash. Other atmospheric pollutants associated with the combustion process in the kilns include SOx , N Ox and particulate matter. The efficiency improvement of the whole process leads to a general reduction of all these pollutants. However, the use of specific fuels may reduce the emissions of one pollutant at the expense of increasing the emissions of others. For instance, the use of cleaner fuels may reduce the emissions of SOx , but due to their lower heat capacity, this could also lead to an increase in the emissions of CO2 . Another example is the case of natural gas, which reduces the emissions of CO2 but increases the emissions of N Ox due to the higher combustion temperature. Those physicochemical phenomena make 12


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it necessary to analyze in detail every possible technical improvement in terms of its costs, environmental impacts and operating conditions. Control technologies can also reduce the emissions of SOx and N Ox (in an independent manner), but they always demand additional power, thereby entailing a potential increase in CO2 emissions. All of the thermal energy consumed in cement production is used in the kiln process, while the average distribution of electricity consumption is as follows: 5% for raw material extraction and blending, 24% for raw material grinding, 6% for raw material homogenization, 22% for clinker production including solid fuels grinding, 38% for cement grinding and 5% for conveying, packing and loading. 6 The exact distribution of the relative percentages of energy consumption depends on the technology used for every process, as illustrated in the next section.

Model description The main characteristics of our model are now described: • We focus on technology-updating decisions for a manufacturing process. The proposed mathematical model considers the current technological state at each stage of the process and at every plant along with the range of updating options available. The costs related to every possible change are considered. Closing decisions (i.e., shutting down a facility) are also allowed. • The manufacturing process is not modeled as a single stage within the supply chain. Instead, it is decomposed into sub-processes. This approach allows assessing the environmental impacts associated with each stage of the process. This modeling approach also account for the flow of sub-products between the individual components of the process. Hence, technology-updating decisions are implemented in specific plants, and those improvements may imply the redesign of the entire supply chain. 13



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• A set of technology-updating alternatives are defined for each equipment unit. These alternatives are aggregated into individual projects. Each project is defined by (i) the main production technology; (ii) a set of complementary technologies that can be incorporated into the main technology; (iii) the fuel used; and (iv) a range of minimum and maximum production capacities. Then, the capacity is managed as a discrete variable that describes economies of scale. This modeling structure leads to a linear formulation that simultaneously models the effects of the main production technology options, their possible complements, the fuel used and the production capacity. • Pollutant emissions and resources consumption are automatically calculated from the operating variables. • Particular projects are created for each equipment, depending upon its initial state. The “do-nothing” project is the first option in every case. It describes the current state of the equipment and has zero executing cost. • The environmental impact is quantified through the emissions of target pollutants (CO2 , N Ox , and SOx ) at each stage of the process. Individual emissions are associated with technology-updating projects. This strategy models environmental concerns via physical phenomena, avoiding the uncertainty related to their translation into damage categories. • The model accounts for the pollution caused by the transportation from plants to markets. Therefore, our approach considers the trade-off between large-scale production in few production plants and increased transportation costs.

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Mathematical formulation A mixed-integer linear programming (MILP) formulation is proposed in this section to optimize cement supply chains according to four objectives: (i) emissions of CO2 , (ii) emissions of SOx , (iii) emissions of N Ox and (iv) total relevant cost. The latter includes technologyupdating, operating, transportation, fixed, and shut-down costs. To model this problem as an MILP, the following assumptions were made: • The operating cost of a particular project is defined as a per-unit cost. That is, the total operating cost of a project is computed by multiplying the per-unit cost with the total production rate of the equipment in which the project is to be executed. The emissions to the environment are defined in a similar way, so within a range the total emissions attributed to a given process are defined by a linear function of the total production. • The cost of each technological improvement project depends on the project to be executed and the current technology installed for each equipment. • The closing costs depend on the current technology of the equipment being removed from operation. • Emissions of CO2 , SOx and N Ox are directly related to each technology improvement project. Emission values related to each pollutant vary with the type of production technology, the production scale, the use of complements to improve the energy efficiency and the type of fuel. • The model allows for transferring material between facilities in the network. This implies that some manufacturing processes can be omitted in a facility. The proposed MILP formulation is described in detail next. Let K and M be the sets of kilns and mills available in the system, respectively. Let UK and UM be the sets of 15



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technology-updating projects that are available for the kilns and mills. Within each main production technology, a project is a combination of fuel, production capacity and technical complements that can be installed to improve its environmental performance. Particular projects are created for each equipment, depending upon its initial state. The “do-nothing” project is the first project in every case and has zero executing cost. Finally, let C be the set of aggregated customers (or group of customers) in the supply chain.

Nomenclature The notation of the MILP formulation is as follows:

Notation Sets K M C UK UM

Kilns Mills Group of customers technology-updating projects that are available for each kiln technology-updating projects that are available for each mill

Parameters

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Dc αrk βrk γtk δsm ϕsm ψsm ΓK rk M Γsm K Erk M Esm K Ork M Osm K Frk M Fsm SkK M Sm K qrk M qsm QK rk QM sm KM Hkm MC Hmc η θ µ

cement demand of each customer c ∈ C unitary CO2 emissions if project r ∈ UK is executed in kiln k ∈ K unitary N Ox emissions if project r ∈ UK is executed in kiln k ∈ K unitary SOx emissions if project r ∈ UK is executed in kiln k ∈ K unitary CO2 emissions if project s ∈ UM is executed in mill m ∈ M unitary N Ox emissions if project s ∈ UM is executed in mill m ∈ M unitary SOx emissions if project s ∈ UM is executed in mill m ∈ M total cost of executing project r ∈ UK in kiln k ∈ K total cost of executing project s ∈ UM in mill m ∈ M annualized cost of executing project r ∈ UK in kiln k ∈ K annualized cost of executing project s ∈ UM in mill m ∈ M per-unit operating cost if project r ∈ UK is executed in kiln k ∈ K per-unit operating cost if project s ∈ UM is executed in mill m ∈ M fixed cost if project r ∈ UK is executed in kiln k ∈ K fixed cost if project s ∈ UM is executed in mill m ∈ M closing costs for kiln k ∈ K closing costs for mill m ∈ M minimum production capacity if project r ∈ UK is executed in kiln k ∈ K minimum production capacity if project s ∈ UM is executed in mill m ∈ M maximun production capacity if project r ∈ UK is executed in kiln k ∈ K maximun production capacity if project s ∈ UM is executed in mill m ∈ M distance between kiln k ∈ K and mill m ∈ M distance between mill m ∈ M and customer c ∈ C transportation CO2 emissions per production unit and per distance unit transportation costs per production unit and per distance unit clinker percentage in the cement mix

Binary decision variables xK If project r ∈ U K is executed in kiln k ∈ K, and zero otherwise rk = 1 M xM is executed in mill m ∈ M, and zero otherwise sm = 1 If project s ∈ U K ak = 1 If kiln k ∈ K is operating, and zero otherwise aM If mill m ∈ M is operating, and zero otherwise m = 1 Continuous decision variables: AK production volume of kiln k ∈ K associated with project r ∈ U K rk AM production volume of mill m ∈ M associated with project s ∈ U M sm KM Tkm amount of clinker transported from kiln k ∈ K to mill m ∈ M MC Tmc amount of cement transported from mill m ∈ M to to customer c ∈ C 17



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Objectives The model must minimize four objectives: emissions of CO2 , N Ox , and SOx ; and the total cost. The first objective, namely the emissions of CO2 , is defined as in equation 1. E(CO2 ) =

X X

X X

αrk · AK rk +

s∈U M m∈M

r∈U K k∈K

XX

δsm · AM sm +

η·

KM Hkm

·

KM Tkm

+

(1)

XX

η·

MC Hmc

·

MC Tmc

m∈M c∈C

k∈K m∈M

The first term computes the direct and indirect emissions associated with the combustion and calcination process that take place inside the kilns. In this first term, AK rk represents the amount of clinker produced with project r in kiln k, and αrk denotes the unitary emissions of CO2 if project r is executed at kiln k. Similarly, the second term computes the emissions due to the grinding process, which only produces indirect emissions. In this second term, AM sm denotes the amount of cement produced with project s in mill m, and δsm denotes the unitary emissions associated to the electricity consumption of project s when executed in mill m. The third and fourth terms compute the emissions produced by the transportation KM MC is the distance between process, where Hkm is the distance between kiln k and mill m, Hmc KM mill m and customer c, Tkm is the amount of clinker transported from kiln k to mill m, MC Tmc is the amount of cement transported from mill m to customer c, and η denotes the

unitary CO2 emissions due to transportation. The emissions of N Ox and SOx are computed via equations 2 and 3, respectively.

E(N Ox ) = E(SOx ) =

X X

βrk · AK rk +

X X

ϕsm · AM sm

(2)

ψsm · AM sm

(3)

r∈U K k∈K

s∈U M m∈M

X X

X X

r∈U K

k∈K

γrk · AK rk +

s∈U M

m∈M

In equation 2, βrk and ϕsm denote, respectively, the unitary emissions of N Ox associated with

18


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M the kiln and mill processes, with AK rk and Asm being again the amounts of clinker produced

with project r in kiln k, and the cement produced with project s in mill m, respectively. Similarly, in equation 3, γrk and ψsm denote the unitary emissions of SOx in the kiln and mill processes. The total cost is computed on a yearly basis as in equation 4, where Ce denotes the annualized cost of executing the technology improvement projects, Cf denotes the fixed costs, Co the operating costs, Cs the cost of shutting down or closing facilities, and Ct is the total transportation cost.

T C = Ce + Cf + Co + Cs + Ct

(4)

The total annual cost of executing the technological improvement projects (i.e. Ce ) is defined K M are the annualized costs of executing projects r and as in equation 5, where Erk and Esm M s in kiln k and mill m, respectively. 66 Furthermore, binary variables xK rk and xsm take the

value of one if the corresponding projects are to be executed and zero otherwise. X X

Ce =

r∈U K

K Erk · xK rk +

k∈K

X X s∈U M

M Esm · xM sm

(5)

m∈M

K M M The annualized costs Erk and Esm are computed as in equations 6 and 7, where ΓK rk and Γsm

denote the total cost of executing the corresponding project, d is the annual discount rate, and n is the amortization period. ΓK rk · d (1 − (1 + d)−n )

(6)

ΓM sm · d = (1 − (1 + d)−n )

(7)

K Erk =

M Esm

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K M The fixed cost is computed as in equation 8, where Frk and Fsm denote the annual fixed

cost of operating the corresponding kiln or mill if projects r or s were executed in the M corresponding equipment; whereas binary variables xK rk and xsm are defined as before.

Cf =

X X

X X

K Frk · xK rk +

M Fsm · xM sm

(8)

s∈U M m∈M

r∈U K k∈K

In a similar way, the annual operational cost is computed as in equation 9. Prameters K M Ork and Osm correspond, respectively, to the unitary operating cost associated with project M r in kiln k, and project s in mill m; whereas AK rk and Asm denote again the amounts of

clinker produced with project r in kiln k, and the cement produced with project s in mill m, respectively.

Co =

X X

K Ork · AK rk +

X X

M Osm · AM sm

(9)

j∈U M m∈M

r∈U K k∈K

The cost of shutting down or closing facilities is computed as in equation 10, where SkK and M Sm denote the cost of shutting down k and mill m, respectively. Binary variables aK k and

aM m take the value of zero when kiln k or mill m are removed from operation.

Cs =

X k∈K

SkK · (1 − aK k )+

X

M Sm · (1 − aM m)

(10)

m∈M

Finally, the transportation cost is computed as in equation 11, where θ represents the cost of transporting one unit of product (i.e. one metric ton) per unit of distance (i.e. one kilometer). KM MC Parameters Hkm and Hmc , respectively, denote the distances between kiln k and mill m, KM MC and between mill m and customer c. Furthermore, Tkm and Tmc are the amount of product

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transported from kiln k to mill m, and from mill m to customer c, respectively.

Ct =

XX

KM KM + · Tkm θ · Hkm

XX

MC MC · Tmc θ · Hmc

(11)

m∈M c∈C

k∈K m∈M

Constraints K Constraint 12 ensures that the production quantity assigned to kiln k (i.e AK rk ) lies between qrk

and QK rk , which correspond, respectively, to the minimum and maximum allowable capacity for project r executed in kiln k. Similarly, constraint 12 ensures that, if project s is executed M and QM in mill m, the production quantity assigned to mill m lies between qsm sm , which

correspond, respectively, to its minimum and maximum capacity.

K K K K qrk · xK rk 6 Ark 6 Qrk · xrk

∀ r ∈ UK, ∀ k ∈ K

(12)

M M M M qsm · xM sm 6 Asm 6 Qsm · xsm

∀ s ∈ UM, ∀ m ∈ M

(13)

Constraints 14–17 model the mass balances. Here constant µ defines the amount of clinker in the cement mix. These constraints also imply that material exchange is allowed not only between processes within a single facility, but between facilities. This is a common condition in cement supply chains, where clinker produced at a given facility can be ground at another.

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In fact, in most cement supply chains, the number of mills exceeds the number of kilns. X

MC = Dc Tmc

∀ c∈C

(14)

∀ k∈K

(15)

∀ m∈M

(16)

∀ m∈M

(17)

m∈M

X r∈U K

X

X

AK rk =

m∈M

X

AM sm = µ ·

s∈U M

X

KM Tkm

KM Tkm

k∈K

AM sm =

X

MC Tmc

c∈C

s∈U M

Finally, constraints 18–19 guarantee that if a technical project is executed on a given equipment, then that particular equipment needs to be in operation. Binary variables aK k and aM m take the value of zero when any operation project is selected for the corresponding kiln or mill. They also ensure the selection of exactly one technical improvement project per piece of equipment. The constraints that define the domain of the decision variables can be deduced from their definition. X

K xK rk = ak ∀

k∈K

(18)

m∈M

(19)

r∈U K

X

M xM sm = am ∀

s∈U M

Case study A case study was solved to illustrate the capabilities of the approach presented. The focus is on two main processes that offer great potential for technology-updating options: (i) the calcination process in the kiln; and (ii) the clinker milling process. The first is the most thermal-energy-demanding process in cement production, and the second is the

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most electrical-energy-demanding. Note that we consider here a simplified supply chain “production-distribution-markets”, where the suppliers of raw materials for the manufacturing plants are assumed to be located next to the plants themselves. Six main technologies were considered for the kiln process. For each main technology, up to three production capacities and up to seven complementary technologies were considered. For both, the main technologies and the complementary technologies, the do-nothing option is always considered as an individual potential project. Additionally, four hypothetical fuels were considered (only one fuel can be selected, except in the multichannel dry kiln, which can use several fuels simultaneously). It is important to note that the complementary technologies associated with one main technology are not mutually exclusive (i.e., several complementary technologies can be installed simultaneously in a kiln). The number of feasible combinations of complementary technologies can be therefore very high. It is hence left to the decision maker to specify the number of them to be analyzed so as to keep the model in a manageable size. The total number of combinations considering six main production technologies, one to three capacity ranges for each one, four fuels and two to five not exclusive technical complements for each kind of kiln is close to 1800 (depending on the initial state of each kiln). To simplify the calculations, we selected representative combinations of those options, creating a set of 78 to 114 updating projects for each kiln, according to its initial state. Note that not all complements and production capacity ranges are compatible with all kiln technologies. Similarly, a total of three main technologies were considered for the milling process. Each one considers five possible production capacity ranges, thereby leading to 15 updating projects in any cement mill. The parameters of the case were based on published route maps for environmental improvement in the cement production 6,53,54,57–62 as described in the problem statement section of this paper. Total investments cost of each project were amortized over a five years period. 23



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Due to the very low indirect emissions of N Ox and SOx associated with electricity generation in the case study, only CO2 emissions were used to describe the milling process. A similar assumption was made for the transportation process, as these emissions are negligible when compared to the emissions of CO2 . In synthesis, the emissions of N Ox and SOx in the case study only depend on the calcination process, while the emissions of CO2 depend on the calcination, milling and transportation processes. The data used in the calculations, which reflect a realistic scenario, are available as supplementary material. The total investment cost of each project was amortized over a five-year period.

Solution procedure The MILP was implemented in Xpress Mosel 3.8 r and solved using Xpress Optimizer 27.01.02 r. The experiments were run on a machine with 16 GB of memory and eight AMD A10-5800B processors running at 3.8 GHz under Windows 7 at 64 bits. To solve the multiobjective MILP, the epsilon-constraint method was used. 67 Furthermore, to narrow down the number of solutions, we used the approach presented in. 68 The test instance includes 11 production plants with 10 kilns, 11 mills and 33 customers. A detailed description of our solution approach follows. First, the four objectives were minimized to obtain a lower bound for each of them. The worst (i.e., highest) value obtained for each objective after minimizing the other three was kept as upper bound (see Table 5). Figure 3 shows the results of these four single objective solutions and the trade-offs among them. Remarkably, the minimum CO2 solution weakly dominates the minimum N Ox one, as it shows the same performance in terms of N Ox and SOx , but lower CO2 and Cost values.

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1 0.8 0.6 0.4 0.2 0 CO2 min CO2 emissions

NOx

SOx

minN Ox emissions

Cost

minSOx emissions

minT otalcost

Figure 3: Solutions for single objective optimization. Vertical scale is normalized.

Table 5: Intervals found for each objective. Emissions units (tons), cost unit (usd). Objective CO2 N Ox SOx Cost

Lower Bound 4.16E + 09 3, 43E + 06 6, 84E + 05 5, 14E + 08

Upper Bound 5, 28E + 09 5, 06E + 06 2, 27E + 06 9.85E + 08

After defining the intervals for each objective, a set of bi-objective models were solved by optimizing the cost against each single environmental objective separately. Each of these bi-objective models was solved via the epsilon-constraint method. Figures 4, 5 and 6 present the Pareto sets for the total cost against the three contaminants optimized (i.e., CO2 , N Ox and SOx ). In these figures, the current situation is depicted in red, while the solutions in blue are those that dominate the current one (i.e., they are better than the initial point simultaneously in both objectives).

25



7

Total Cost [109 $]

Non-dominated solutions Current solution 6.5

6

5.5

5

4

4.5 5 5.5 Emmisions of CO2 [108 Tons]

6

Figure 4: Pareto solutions for Total Costs vs. Emmisions of CO2

6.5 Non-dominated solutions Current solution Total Cost [109 $]

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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6

5.5

5

3

3.5

4 4.5 5 5.5 6 6 Emmisions of N Ox [10 Tons]

6.5

Figure 5: Pareto solutions for Total Costs vs Emmisions of N Ox

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6.5 Non-dominated solutions Current solution Total Cost [109 $]

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


6

5.5

5 0.5

1

1.5 2 2.5 3 3.5 Emmisions of SOx [106 Tons]

Figure 6: Pareto solutions for Total Costs vs. Emmisions of SOx

Finally, the model was solved considering all the objectives simultaneously. Four sets of solutions were calculated, each corresponding to a different case in which one of the four objectives was defined as main objective, while the remaining ones were transferred to auxiliary constraints. The auxiliary single objective problem was solved 4 ∗ 73 = 1372 times considering seven values for each objective transferred to an epsilon constraint. Non dominated solutions are easy to identify within bi-objective problems, but a higher number of objective functions generates a very large number of solutions, making it difficult to select the solution to be implemented in practice. 68 In this case, a total of 1126 feasible solutions were obtained. Using a Pareto filter developed by Antipova et al. (2015), 68 a set of 26 non-dominated solutions was identified among the total set of solutions obtained. This narrowing procedure is briefly described as follows: (i) the values for each objective function are normalized in order to obtain a common basis to facilitate their comparison; (ii) redundant solutions are

27



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removed considering a given tolerance -a tolerance of 0.01% was used in this case-; and (iii) a reduced pool is built using the concept of dominance (i.e. a feasible solution is said to be non-dominated if no other solution scores at least as well in all objectives, and strictly better in one 69 ). The single objective problem has 2,785 constraints, 1,619 continuous variables and 1,162 binary variables. The running time for each experiment is described in Table 6, in which the first column provides the objective function of the corresponding experiment, and the second column lists the objectives that were used as constraints within the -constraint approach. The third column displays the total running time of the complete cycle (i.e. the exploration of all the values), and the fourth column corresponds to the average time of each run. Table 6: Computational times for each experiment Objective

-constraints

Cycle run time (s)

Cost CO2 N Ox SOx Cost Cost Cost CO2 CO2 SOx Cost CO2 N Ox SOx

CO2 N Ox SOx N Ox SOx N Ox N Ox , SOx , CO2 N Ox , SOx , Cost CO2 , SOx , Cost CO2 , N Ox , Cost

7.88 5.24 9.50 2.50 27.92 1.33 667.92 218.74 408.07 757.72

28


Average run time (s) 1.92 0.69 0.31 0.31 1.20 0.74 1.35 0.35 3.99 0.19 0.64 1.95 1.19 2.20

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Results analyses To analyze results, we focus on the main differences between the extreme solutions and some intermediate ones achieving significant reductions in emissions at a marginal cost. Figure 7 provides the spatial structure of the two extreme solutions. The minimum CO2 emissions solution shown in the left side concentrates clinker production in fewer and larger kilns, while maintaining most of the mills active in the system. The minimum cost solution (in the right) tend to maintain all operating kilns and mills. However, it implements some updating projects that reduce operational costs without incurring in high financial investments.

Kiln and mil process facilities Mill facility Technological improvements or fuel change Increase in production capacity Closed plants Customer

Figure 7: Geographic representation of minimal CO2 and minimal costs solutions

A sample of 13 representative non-dominated solutions is presented in Figure 8. Each solution is represented with an ID number in the horizontal axis. The height of the bars in the figure represents the annual variation of the annual cost and emissions with respect to the current state of the system, computed as a percentage. This analysis shows that reduc29



ing the emissions of CO2 entails a higher cost than reducing emissions of N Ox and SOx . This is because the former are decreased implementing changes in the production capacity, complements for higher energy efficiency and fuel selection. On the contrary, the latter can be reduced in turn by installing control technologies with lower investment and operational costs. It is worth noting that important improvements in the overall environmental performance can be achieved at a low marginal cost, as in solutions 7 to 13. As an example, solution 8 decreases CO2 emissions by 21%, N Ox emissions by 42% and SOx emissions by 77% at the expense of increasing the annual cost by only 4%. These solutions are described in detail in Table 7 and Figure 9. 80 CO2 N Ox SOx Total Cost

60 40 % variations

20 0 −20 −40 −60

Non dominated solutions Figure 8: Annual variation of non dominated solutions with respect to the current state of the system.

For the above mentioned set of solutions (7 to 13), Table 7 describes further details of the kiln process. The first column corresponds to the ID of the solution (the same ID is used in Figure 8). The second column (i.e. F) enumerates the facilities where kilns remain

30


13

12

11

10

9

8

7

6

5

4

3

2

−80 1

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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in operation (facilities are identified with numbers from 1 to 10). Columns 3 and 4 describe the initial state of the system in terms of technology and production capacity of the kilns, while columns 5 and 6 describe the technology and production capacity of the corresponding solution. In the table, the technologies are labeled as wt (wet), sw (semi wet), ld (long dry), ph (pre-heater), and pc (precalciner). All kilns currently operate using fuel 1. The new selected fuel is presented in column 7, where mc represents a multi-channel operation. As an example, in solution 7 only kilns in facilities 5 and 7 remain in operation, while the rest are shut down. The main technology in facility 5 remains the same, but the production capacity was increased from 1.20 to 3.30 metric tons per year. On the other hand, a wet kiln in facility 7 was replaced by a dry kiln with preheater and precalciner, with a production capacity that moved from 0.66 to 2.4 metric tons per year. Solutions at the bottom of the table (i.e the solutions on the right of Figure 8), tend to leave more kilns in operation, which implies relatively lower reductions of pollutant emissions, but also lower investments. A detailed description of all non-dominated solutions is included as supplementary material. Results reveal that the environmental performance of the supply chain can be improved by shutting down most of the current operating equipment, an action that is offset by increasing the capacity in those that remain in operation. Energy efficiency of the new installed equipment contributes to reduce emissions and to achieve lower operational costs. Another strategy to reduce combustion emissions consists of using cleaner fuels or more efficient burners. Optimal potential capacity allocation is a simultaneous strategy to reduce logistic costs and transportation emissions.

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Sol.

Kiln

Operating kilns Initial state Selected operational project Q Q Tech. Tech. fuel (M.tons/year)

7 8 9 10

11

12

13

5 7 5 7 5 7 5 6 7 3 6 7 10 3 6 7 8 1 2 3 6 7 10

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ph, pc wet ph, pc wet ph, pc wet ph, pc ph and pc wet ph, pc ph, pc wet long dry ph, pc ph, pc wet ph wet ph, pc ph, pc ph, pc wet long dry

1.2 1 1.2 1 1.2 1 1.2 1.2 0.6 1.2 1.2 0.6 0.6 1.2 4 0.6 0.75 0.6 1.2 1.2 1.2 0.6 0.75

(M.tons/year)

ph, pc ph, pc ph, pc ph, pc ph,pc ph,pc ph, pc ph, pc ph, pc ph, pc ph, pc ph, pc long dry ph, pc ph, pc ph, pc ph semi wet ph, pc ph, pc ph, pc wet long dry

3.3 2.4 3.3 2.4 3.3 2.4 1.2 2.4 3.3 1.2 1.2 3.3 0.75 1.2 1.2 2.4 0.75 0.75 1.2 1.2 1.2 0.6 0.75

multich. multich. 1 4 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1

Table 7: Operating projects in a sample of non dominated solutions. For the same set of solutions (i.e. 7 to 13), the total investment and closing costs, and the annual operational savings with respect to the initial state of the system are (respectively) represented in blue and green bars in Figure 9. Emissions reduction and short-term financial savings related to technology updating decisions are evident. In this figure, the investment cost is expressed as a total value (not annualized). A detailed financial analysis should include the cost of the capital, assets depreciation and the annual cash flows during the depreciation period. Such a detailed analysis would allow to determine the payback period 32


Page 33 of 47

of the proposed investment. Due to the static character of this model, we restrict the analysis to the information presented in Figure 9, where the total investment (not annualized) can be contrasted against the annual operational savings. As an example, the total investment in solution 12 is equal to 398 USD billion, while annual savings with respect to the initial state of the system are equivalent to 129.8 USD billion. This implies that -in addition to the environmental advantages- within a very short period of time, the annual savings compensate the investment in technology improvement. 800

600 usd. millions

400

200

13

12

11

10

9

8

0 7

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


Non dominated solutions Total investments and closing cost

anual savings in operational cost

Figure 9: Total investments Vs. operational anual savings in for detailed solutions in Table 8

Management implications The question of why some current industrial systems still operate using obsolete or inefficient technologies is highly relevant. The reason behind this suboptimal operation is related to the lack of information, financial bottlenecks as well as poor expertise on the use of systematic 33



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tools to assess updating decisions and their implications in terms of environmental benefits and related costs. Despite the greater availability of more efficient production technologies, obsolete ones are still operating due to the lack of knowledge of the technological options and uncertainty about its effects and plant-specific operating implications. 61 Although some energy-efficient technologies have short payback periods, the high initial capital cost of the project often prevents its adoption and installation. 61 Hence, the proposed mathematical model aims to give a clear answer to this challenge by: (i) identifying optimal improvement strategies; (ii) providing the decision maker with all relevant information related to the technological and logistic operating conditions; and (iii) assessing the corresponding emissions levels and cost of each alternative. Furthermore, the proposed decision making model becomes an appropriate tool for environmental management initiatives as Ecoprofit, 70 as it allows to clearly recognize the financial benefits of environmental improvement strategies. Reducing the pollution of a production process is always desirable. However, besides technical and logistical issues, it is obvious that breaking the “inertia” of a system is in turn a financial challenge because of the costs involved in shutting down an operating process and disassembling, rebuilding and relocating facilities. One of the key aspects of this research problem was to study the trade-offs between the costs and the polluting emissions. Regarding the classical question “does it pay to be green?”, our results clearly show a positive answer. Dismantling older technologies and installing new ones entail high financial investments. In the case of operating supply chains with obsolete components, however, the benefits are clear as technological updates can lead to simultaneous environmental and financial improvements. For this case study, it is therefore clear that the “business as usual strategy” is sub-optimal and that win-win strategies can be identified. Of course, it is necessary to mention a tacit subject in every planet vs. profit trade off analysis: environmental damages always cause externalities. These financial costs that are technically out of the financial balance constitute an important key issue to consider when 34


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favoring more efficient and cleaner production technologies.

Conclusions To the best of the authors’ knowledge, we have addressed for the first time the problem of improving the environmental performance of an operating supply chain by means of technology-updating projects. The problem was addressed using a multi-objective mathematical model with four objective functions: emissions of CO2 , N Ox and SOx and the total cost. A test instance was built using publicly available technical information of the cement industry. A set of non-dominated solutions was obtained by applying the epsilon-constraint method combined with Pareto filters. The results clearly demonstrate the effectiveness of using a multi-objective mathematical programming approach to design strategies towards a more sustainable supply chain. The high practical applicability of this problem invites to keep working on its multiple possible extensions. These include more complex supply chains, the use of additional key performance indicators and the inclusion of multiple periods in the planning model. Another topic not included in this work is the commonly discussed public cost of private environmental damage. Environmental taxes seem to be the nearest available quantitative instrument to consider this subject. Fixing the value of those taxes is under the purview of local environmental authorities. Other potential lines of future research could be the inclusion of the different uncertainty sources that affect the calculations. The benefits of high-scale production and the consequent higher transportation intensity remain a strategic subject in the management of sustainable systems. Large-scale production advantages depend on the particular characteristics of each product and production system. Considering the cement supply chain, this case study demonstrates that better solutions in

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terms of emissions reduction are related to higher production scales. That effect is stronger in the case of the kiln, thus emphasizing the importance of the modeling of as many stages as possible in the supply chain. The case study justifies the use of advanced decision making tools to optimize a wide variety of variables in the integral planning of production process. The challenge for the firm is to optimize all these decisions simultaneously and to assemble the required information to develop an accurate model of the system. This requires gathering cost and environmental data from the supply chain echelons and identifying possible improvements for every system’s component.

Supporting information Detailed information on the computational experiment is provided as supporting information in the following files:

• Model parameters.xls: includes the parameters of the test instance, with a description of updating projects for each process at each production plant.

• Detailed results.xls: includes detailed description of the strategic and tactical decisions for every non-dominated solution is provided in the file.

This information is available free of charge via the Internet at http://pubs.acs.org/

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Acknowledgments Nora Cadavid-Giraldo expresses her gratitude for the financial support received from Colombian Administrative Department for Science, Technology and Innovation, through the National Program of Researches Formation.

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Figure 10: TOC.

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