Multicriteria Environmental and Economic Analysis of Municipal

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Multi-criteria Environmental and Economic Analysis of Municipal Solid Waste Incineration Power Plant with Carbon Capture and Separation from the Life Cycle Perspective Yuting Tang, and Fengqi You ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b03283 • Publication Date (Web): 08 Nov 2017 Downloaded from http://pubs.acs.org on November 13, 2017

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ACS Sustainable Chemistry & Engineering

Multi-criteria Environmental and Economic Analysis of Municipal Solid Waste Incineration Power Plant with Carbon Capture and Separation from the Life Cycle Perspective Yuting Tanga,b, Fengqi You *b a

Guangdong Province Key Laboratory of Efficient and Clean Energy Utilization, School of

Electric Power, South China University of technology, Tianhe district, Guangzhou 510640, China b

Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, USA November 5, 2017 Submitted to ACS Sustainable Chemistry & Engineering

Abstract This paper presents multi-criteria environmental and economic analyses of municipal solid waste (MSW) grate incineration power plants without and with CO2 capture and separation (CCS) technologies, including monoethanolamine (MEA) absorption, pressure/vacuum swing adsorption (P/VSA), and oxy-fuel combustion (Oxy). The life cycle analysis (LCA) and techno-economic analysis (TEA) are integrated with Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approaches for systematic environmental and economic analysis. This systematic methodology is applied to investigate the applicability of CCS technologies in MSW incineration power plants from perspectives of local government, enterprise, residents, and “equal” weight. The results show that application of CCS reduces the ecosystem quality and the human health impacts, but increases the resources use and yields an economic penalty of $33.45~$45.98 per ton of CO2 avoidance. From the perspective of residents only, the MSW incineration power plant with CCS performs better than that without CCS. The promotion in energy efficiency and carbon price are the critical for CCS to attract the support from

*

Corresponding Author. Phone: (607) 255-1162; Fax: (607) 255-9166; E-mail: [email protected]

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government and enterprise, respectively. In terms of the three CCS technologies, P/VSA and Oxy have similar comprehensive performance, better than MEA absorption. Keywords: municipal solid waste incineration, CO2 capture and separation, multi-criteria decision making, life cycle assessment, analytical hierarchy process

Introduction The global annual output of municipal solid waste (MSW) is about 1.3 billion tons, and is expected to increase to approximately 2.2 billion tons/yr by 2025. Compared with landfill, the MSW incineration has the following advantages: (1) waste volume reduction by 70%~90%;1 (2) much smaller land occupation; (3) leachate reduction; (4) complete destruction of any living organisms and mineralization of organic substances into harmless end products;2 (5) energy recovery by electricity or heat production. By 2015, there are 1179 MSW incineration plants with power generation around the world, with a total capacity of more than 700,000 tons per day.3 The average generation from MSW incineration is assumed as 280 kW·h per ton of waste,3 the global MSW incineration plants are estimated to produce more than 196,000 MW·h electricity per day. One challenge for MSW incineration is CO2 emission, as it generates a large amount of flue gas.4 Incorporating CO2 capture and separation (CCS) technologies into existing power plant is a promising approach to reduce the greenhouse gas (GHG) emissions and mitigate climate change.56

CCS can be potentially integrated into thermal power production processes using any type of

fuels with carbon sources.7 Considering the rapid growth of waste incineration industry in the foreseeable future, application of CCS technologies in MSW incineration power plants can contribute to the solution of a number of challenges on waste treatment, energy crises, and climate change, simultaneously. There are economic and environmental potentials of taking advantage of fossil fuel substitution and carbon abatement options. Typically, CCS technologies for power plants include post-combustion capture, pre-combustion capture, and oxy-fuel combustion. These technologies have differences in energy consumption, environmental impacts, and economic performance. Therefore, for judicious selection of CCS technologies in MSW incineration power plants, rigorous and systematic analyses of MSW incineration power plant with various CCS technologies need to be addressed. Several publications address the life cycle assessments (LCA) or techno-economic analyses (TEA) of coal and natural gas power plants with different CCS technologies.8-10 Application of various CCS in traditional fossil fuel power plants can decrease the global warming potential 2 ACS Paragon Plus Environment

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(GWP) by 65~75%, but increases other environmental impacts because of the efficiency penalty.1112

LCA studies on bio-energy systems with CCS showed that the chemical absorption post-capture

technology strongly improved the total environmental performance of biomass gasification and co-fired plants, mainly because of sharp reduction of GWP.13-15 These existing LCA results are dependent on the specific CCS technology and power plant type. Compared with coal and natural gas power plants, LCA on non-fossil fuel power plants with CCS technologies, specifically with physical adsorption post-combustion capture or oxyfuel combustion, attract much less attention.14 Moreover, LCA studies do not account for economic performance and most TEA studies on CCS technologies only consider the direct CO2 emissions. Previous TEA studies showed that the CO2 avoidance cost ranged from $26 to $42 per ton of avoided CO2 in a coal power plant with CCS,1618

but the CO2 avoidance cost in a MSW power plant is unknown. There are only limited existing

TEA studies of MSW power plants with CCS.19-20 These studies focus on the limited economic indicators including the flue gas loss, oxygen supply cost and power consumption of CCS, rather than the systematic analysis from the full life cycle perspective.21 To the best of our knowledge, there is no literature addressing the LCA and TEA analyses of MSW grate incinerators with CCS technologies from the whole life cycle perspective. Multi-criteria decision-making (MCDM) is a systematic methodology to identify the best option among a set of feasible alternatives involving multiple criteria.22-24 MCDM yields comprehensive results by combining the cost/benefit information with the stakeholders' perspectives.25 MCDM approaches have been widely applied to the waste management field,26-32 and a multi-objective strategy of MSW management with consideration of multiple stakeholders is proposed.33 Energy, environmental, and economic systems analysis models were proposed to compare the landfill and traditional incineration strategies to optimize MSW treatment.34-36 MCDM approaches also have been used to assess complexity of CCS and reveal the interconnection among complexity factors,37 and to identify and evaluate main non-technical factors affecting the CCS chain.38 To the best of our knowledge, there is no systematic MCDM analysis of CCS options for MSW power plant that account for economic and environmental performance from the life cycle perspective. This paper presents novel multi-criteria environmental and economic analyses of MSW grate incineration power plants without and with three CCS technologies, and the potentials of incorporating CCS in MSW incineration power plants are systematically investigated by an 3 ACS Paragon Plus Environment


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MCDM methodology. This MCDM methodology combines LCA and TEA with Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approaches. The aforementioned CCS technologies, namely, monoethanolamine (MEA) absorption, pressure/vacuum swing adsorption (P/VSA), or oxy-fuel combustion (Oxy) technologies are systematically compared by the MCDM methodology, to identify the most promising CCS technology option. Moreover, the order preference of CCS technologies is analyzed from the perspectives of local government, enterprise, residents, and “equal” weight, respectively. The major novelties of this work are summarized as follows: •

LCA studies of MSW grate incineration power plant with three CCS technologies using comprehensive environmental impact categories.

•

Techno-economic analysis of a MSW grate incineration power plant with three CCS technologies.

•

Comprehensive comparison of CCS technology alternatives for MSW incineration power plants from different stakeholders’ perspectives using MCDM.

The rest of this paper is organized as follows. First, the Process and Systems Description section introduces an existing conventional MSW power plant and retrofitted power plants with CCS units. Next, the Systems Analysis Methodology section describes the LCA, TEA, AHP, and TOPSIS approaches. The proposed methodologies are then applied to MSW grate incineration power plants with three CCS technologies, and the results are compared with the conventional MSW power plant. Conclusions are provided in the end.

Process and Systems Description The LiKeng MSW incineration power plant (Guangzhou, China) under investigation consists of three identical incinerating units with the capacity of 2,000 ton/d. We choose this power plant as our research objective because it adopts a mature industrial incineration technology from Denmark and provides a large amount of available plant data. This conventional MSW power plant without CCS is defined as a baseline (Case 0). The retrofitted MSW incineration power plants with CCS units are shown in Fig. 1. Three cases using different CCS technologies are considered: Case 1: MSW incineration with chemical post-combustion capture via monoethanolamine (MEA) absorption, which presents the highest CO2 carrying capacity by mass among the amines.39


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Case 2: MSW incineration with physical post-combustion capture via

two-stage

pressure/vacuum swing adsorption (P/VSA) using zeolite 13X, which is the most popular zeolite for CO2 capture with available experimental isotherms.40 Case 3: MSW incineration with oxy-fuel combustion that uses an air separation unit to eliminate N2 and recycles part of flue gas to adjust the flame temperature. Pre-combustion capture is not considered in this paper, because it requires a Natural Gas Combined Cycle (NGCC) or Integrated Gasification Combined Cycle (IGCC), which is typically unsuitable for existing MSW grate power plants.

Fig. 1 Schematic diagram of the application of a CCS technology unit to a MSW incineration power plant: a) post-combustion technology; b) oxy-fuel combustion technology 5 ACS Paragon Plus Environment


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The typical composition of feed MSW for this power plant is listed in Fig. 2. The average lower heating value of the inlet MSW is 6,800 kJ/kg.

Fig. 2 Average composition of MSW in Guangzhou (on as received basis) Description of Case 0 As shown in Fig.3, the incinerating unit consists of a furnace, followed by the vertical passes and a convective pass. The furnace is equipped with a horizontal moving grate and an overbed chamber. The moving grate incinerator dominates over other types in the waste incineration industry around the world.3 The combustion air is separated in two groups: primary air and secondary air. Primary air is fed to the bottom of the grate, which converts combustibles in MSW to gaseous products. The left-over ash falls into the ash pit at the end of the grate. Combustible gases flow upward and mix with secondary air from the front and rear walls of the furnace to continue the combustion process, and then it is treated by the selective non-catalytic reduction (SNCR) denitrification (De-NOx) device in the chamber. In the convective pass, the flue gases pass through final superheater (SH3), secondary superheater (SH2), primary superheater (SH1) and economizer for energy recovery. The flue gases then enter the treatment system, which contains a semi-dry desulfurization (De-SOx) tower and a bag filter, to meet standards of pollution control on the MSW incineration.41 The power plant is equipped with a steam turbine electricity generator, and its annual generation amount is 1.59×108 kW·h with annual operation time of 8,000 hours. The operation of a conventional MSW incineration power plant without CCS includes a 6 ACS Paragon Plus Environment

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number of feeds in addition to MSW as fuel. These include limestone, activated carbon, urea, and diesel oil. Limestone is used to neutralize acidic gases in the semi-dry neutralizing column, activated carbon is used to reduce the emissions of heavy metals and dioxin, urea is used to deal with NOx in SNCR, and diesel oil is used to maintain minimum furnace temperature of 850 ℃ when the incinerator is started or shut down.

Fig. 3 Schematic diagram of a conventional MSW grate incineration (Case 0) Description of Case 1 The post-combustion CCS unit is installed after de-NOx device, de-SOx device and a bag filter in both Case 1 and Case 2. Also, the flue gas typically requires cooling before it enters the CCS unit. Direct air emissions and degradation waste from Case 1 are quantified based on the reference sources listed in Table 1. Besides CO2, emissions of SO2 and NOx from the power plant with postcombustion capture differ from those from the power plant without CCS, because these emissions can react with the MEA solvent in the absorber of the CCS unit. In addition, in the wet scrubber, partial particulates and HCl in the flue gas are also removed.10 However, the degradation of MEA solvent promotes the formation of additional NH3,42-43 and MEA emission is directlyreleased



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because part of the MEA solvent leaves the stripper through evaporation or entraining as droplets.14, 44

Table 1. Emissions of various gaseous pollutants and degradation wastes when passing through an MEA stripper Item

Calculation method

Reference

SOx

a further reduction in SO2 content of 99.5%, resulting in a

10, 45

total removal efficiency of 99.995% NOx

10% of NOx in flue gas is NO2 and 25% of the NO2 in the flue gas are expected to react with MEA, so the total NOx

10, 45

reduction rate is assumed to be 2.5%. PM

an additional reduction of all particulates by 50%

10, 45

HCl

an additional 95% reduction in HCl

10, 45

MEA

10 g MEA per ton of captured CO2 leaving the stripper

10, 45

NH3

Additional 194 g produced NH3 per ton of captured CO2

46

Hg

3.95% of Hg is emitted to atmosphere in vapor form

47

reclaimer waste

3.2 kg reclaimer waste per ton of captured CO2

9

0~95% removal, depend on the specific pollution type

9

VOCs, HF, Cd, Pb, As, Cr, Ni, Cu

Formation of heat-stable salts, MEA degradation and vapor losses result in a requirement for make-up MEA, and the required amount of fresh MEA is estimated to be 1.5 kg per ton of captured CO2.10, 45, 9, 48-49 Besides MEA solvent, the CCS process also requires caustic soda (NaOH) to reclaim the amine from the heat stable salt and activated carbon to deal with degradation products.8 The production and transport of MEA solvent, NaOH, and activated carbon should be taken into account as one of the main sources of indirect emissions for post-combustion capture. MEA absorption produces reclaimer waste,14 which can be treated together with the bottom ash from the MSW incinerator. The energy requirements for the MEA chemical absorption process contain heat consumption for regeneration of the MEA solvent, as well as the direct electricity used by pumps, fans and compressors. After MEA absorbs CO2 from the flue gas, the solvent is pumped to the 8 ACS Paragon Plus Environment

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stripper, where stripping is heated by low-pressure steam drawn from the steam cycle of power plants. This steam requirement for the re-boiler in the regeneration column is approximately 1.7 tons per ton of CO2,18 and the heat requirement ranges from 3.89~4.32 GJsteam per ton of CO2.50-54 In this paper, the heat requirement is set to be 4 GJsteam per ton of CO2,55 with the power equivalent factor of 0.2 GJe/GJsteam.56 For the chemical absorption, steam extraction for solvent regeneration is the most energy intensive process,57 and leads to an observed decrease of the efficiency of the power plant. Pumps are used to transport the solvent through CO2 capture unit and to feed cooling water. Fans are used to offset the pressure drop.55 The total electricity requirement of the pumps and fans is 23.6 kW·h per ton of CO2.55 For the three cases with CCS technologies, the enriched CO2 stream is separated and compressed to 110 bars for pipeline transportation. The electricity requirement for the CO2 compression ( Ecompressor , kW·h per kg of CO2) is calculated as follows.58 （γ -1）   ZRT N γ  p2  N γ  -1 W=    M γ − 1  p1   

Ecompressor =

(1)

W

(2)

ηisηmt

where W is specific work (kJ per kg of CO2), Z is compressibility factor, R is the universal gas constant, T is suction temperature, γ is specific heat ratio of cp/cv, M is molar mass, p1 is suction pressure, p2 is discharge pressure, N is number of compressor stages, t is time conversion factor,

ηis is entropic efficiency, and ηm is mechanical efficiency. Description of Case 2 Adsorption processes have been extensively investigated for the CO2 capture from flue gas.5962

P/VSA unit is performed by altering the pressures, and it only requires several adsorption

columns. Its energy consumption is mainly contributed by gas compressors and vacuum pumps. Like chemical absorption, P/VSA also needs regenerative and recyclable adsorbents.63 The energy consumption and economic performance of P/VSA are particularly associated with the specific adsorbent type, process configuration, and operating parameters including feed gas pressure, vacuum pressure, temperature, and purge extents.57 In this paper, P/VSA unit adopts a two-stage Skarstrom cycle, in which the product stream from the first stage is fed into the second stage. The 9 ACS Paragon Plus Environment


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Skarstrom cycle is chosen because of its operational simplicity and its long history of industrial application. This cycle consists of pressurization, adsorption, counter-current depressurization, and light product purge. The bed begins at the low pressure. During the pressurization step, the flue gas is used to raise the pressure of the column from a low pressure to a high pressure. After the bed is pressurized, the valve at the end of the column is opened and the flue gas flows through the bed, with relatively pure N2 leaving the bed. When the bed is almost saturated, the valve at the end of the column is closed, and the bed is depressurized from the front end. Finally, after the column is depressurized, some of the N2 gas from the adsorption step is fed into the end of the column to increase the recovery of CO2.64 Zeolite 13X is used as the adsorbent. The energy consumption is set to be a upper value (275.7 kW·h per ton of CO2) and the CO2 productivity of the bed is 0.27 ton/(m3·day) with 90% recovery from wet flue gas.64 The power consumption requirement for compression, expansion, pumping, and cooling is calculated based on mass and energy balances. Description of Case 3 Oxy-fuel combustion means the combustion under pure oxygen provided by a cryogenic air separation unit (ASU). Between 60 and 80% (70% in this paper) of the flue gas is recycled to the furnace to fill the volume fraction of lost N2.7 Replacement of N2 by the recycled flue gas has effects on the generation of some gaseous products. Compositions of CO2, H2O, SOx, and NOx in oxy-fuel combustion can be calculated by the following equations. mCO2 = Car ⋅ (1- u ) ⋅

mH 2O = H ar ⋅

M CO2

(3)

MC

M H 2O

γ H /H O ⋅ M H

+ War

(4)

2

mSO= α S − SO2 ⋅ Sar ⋅ 2

M SO2

(5)

MS

m= α SO2 − SO3 ⋅ α S − SO2 ⋅ S ar ⋅ SO3 mNO= α N − NOx ⋅ γ NO2 ⋅ N ar ⋅ 2

M SO3

(6)

MS

M NO2

(7)

MN


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= α N − NOx ⋅ γ NO ⋅ N ar ⋅ mNO

M NO MN

(8)

where Car , H ar , Sar , N ar and War are the carbon, hydrogen, sulphur, nitrogen and moisture content, respectively, in the MSW on as-received basis, and u is the rate of unburned carbon. M C , M CO2 , M H , M H 2O , M N , M NO , M NO2 , M S , M SO2 and M SO3 are the molar mass of carbon, CO2, hydrogen,

H2O, nitrogen, NO, NO2, sulphur, SO2, SO3. α S − SO2 , α SO2 − SO3 and α N − NOx are the conversion factors from sulphur to SO2, from SO2 to SO3, and from nitrogen to NOx. γ NO2 and γ NO are ratio of NO2 and NO in NOx, and γ H / H 2O is the molar ratio of H and H2O. There is no consensus on the composition and emission factors of HCl, HF, various heavy metals and volatile organic compounds (VOCs) in the oxy-fuel combustion .83 Therefore, the difference of these emissions between oxy-fuel combustion and traditional air incineration is assumed to be insignificant, and emission of HCl, HF, heavy metals and VOCs under air combustion are used to calculate their emissions under oxy-fuel combustion condition. Different from the power plants with post-combustion capture, de-NOx devices using SNCR are not needed in oxy-combustion plants, since the concentration of NOx in this case is much lower than that in Case 0. Some researchers claimed that a coal oxy-combustion power plant might not need an individual de-SOx device;65-67 but other studies stated that a coal oxy-combustion plant still required a de-SOx device with comparatively higher efficiency and lower cost.16 Since sulphur content in MSW is generally low and circulating fuel gas facilitates SOx removal, semi-dry deSOx devices are not considered in this MSW oxy-combustion plant. The additional units of the MSW oxy-combustion plant are a comprehensive flue gas purification device and a CO2 compressor. The purification device is used to lower the temperature of flue gas and remove acidic gases (93~97% SO2 and 58~78% SO3), moisture (>85%) and particulates (>90%) prior to compression. NOx, SOx, HCl, moisture and heavy metals are further removed as condensate in the following compression.68 Though SNCR de-NOx and semi-dry de-SOx devices are not included, a deep removal of SO2 and NOx emissions is feasible for this oxy-fuel combustion plants.68 The differences in formation of gaseous products and flue gas treatment unit between oxy-fuel combustion and traditional air combustion cause the changes in the emissions of pollution to the environment.



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Oxy-fuel combustion can increase the electricity generation of power plants due to its higher boiler efficiency. Based on the boiler thermodynamic analysis, the boiler efficiency of the targeted MSW incinerator increases from 77.1% to 85.2 %, and annual electricity generation increases from 2.56×108 kW·h to 2.80×108 kW·h. Recycled fuel gas and a smaller excess air factor ( α ) reduce the thermal loss of exhaust gas, and consequently result in the increment of the boiler efficiency.

α in the MSW incinerator is reduced from 1.8 to 1.43,69 due to the injection of pure oxygen and a higher O2 concentration in feed gas in oxy-fuel combustion. The increment of electricity generation is much less than the energy penalty caused by the new equipment. Thus, the total power supply of Case 3 is still lower than that for Case 0. The ASU electricity requirement ( E ASU ) and mass flow of oxygen ( mO2 ) are calculated as follows:70

E ASU = µ1 ⋅ηox + µ2

(9)

⋅ mO2 = ( β + α -1) ⋅ moxy = ( β + α -1)（

Car ⋅ (1- u ) H ar O S + - ar + ar ）⋅ M O2 γ H / O2 ⋅ M H M O2 M S MC

(10)

where µ1 and µ2 are the first and the second empirical constants in ASU energy requirement (0.00172 and 0.1498, respectively), 𝜂𝜂𝑜𝑜𝑜𝑜 is oxygen purity (set to be 95%), and M O2 is molar mass of O2 (32 g/mol). β is air leakage ratio (set to be 2%), and γ H / O2 is molar ratio of H and O2 (equal

to 4). The power requirements of forced draft fan, FGR fan, and cooling water unit in the upgraded boiler for oxy-fuel combustion unit are assumed based to capacity of the steam turbine electrical generator (set to be 1.98 kW/MW).71 The energy consumption for CO2 purification unit is 1.8 kW·h per ton of CO2.72 Although this study only considers three options for CCS, the possibility of applying other CO2 mitigation technologies, such as microalgae biofixation,73-78 ionic liquid-based chemical absorption,79-80 temperature swing adsorption and electric swing adsorption,57,

81-82

to MSW

incineration is worthy of further study.

Systems Analysis Methodology Fig. 4 shows the flowchart of the systems analysis methodology of this work. First, we perform an LCA to calculate the comprehensive resource consumption and environmental impacts of MSW incineration power plants with or without CCS (the four cases described in the previous 12 ACS Paragon Plus Environment

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section). Next, we analyze the economic performance by evaluating the total MSW treatment cost and CO2 avoidance penalty of this MSW incineration power plant with CCS following a life cycle costing approach. The next step is to weight each criterion from different stakeholders’ perspectives using AHP, which models the decision process using a hierarchy, followed by selection of the most suitable CCS technology for the MSW incineration power plant using TOPSIS. The TOPSIS approach chooses an alternative with the shortest distance from the Positive Ideal Solution (PIS) and the longest distance from the Negative Ideal Solution (NIS).

Fig. 4 Flowchart of the proposed systems analysis methodology. AHP: Analytic Hierarchy Process; TOPSIS: Technique for Order Preference by Similarity to Ideal Solution; ALO: agricultural land occupation; ECC: climate change (ecosystems); FET: freshwater ecotoxicity; FE: freshwater eutrophication; MET: marine ecotoxicity; NLT: natural land transformation; TA: terrestrial acidification; terrestrial ecotoxicity (TET); ULO: urban land occupation; HCC: climate change (human health); HT: human toxicity; OR: ionizing radiation; OD: ozone depletion; PMF:



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particulate matter formation; POF: photochemical oxidant formation; FD: fossil depletion; MD: metal depletion; C AI : annual investment cost ; CO , annual total operation cost ; CD : decommissioning cost; PT , total profit; PAC : penalty to avoid the CO2 emission.

Life cycle environmental impact analysis LCA is a widely used method for systematic analysis of the inputs, outputs and the potential environmental impacts of a product system or a technology, across its life cycle, which might start from raw material extraction to energy and material manufacturing, to use, end-of-life treatment and final disposal.79, 83 LCA study is comprised of four phases: goal and scope definition, inventory analysis, impact assessment, and interpretation.84 Goal definition and scope The goal of this LCA study is to determine the life cycle resource consumption and environmental impacts of a MSW incineration power plant with and without CCS. In a product LCA, the functional unit is usually defined in terms of the system's output, i.e. the product. In an LCA for waste management, the functional unit must be defined in terms of system's input, such as the quantity of specific waste, the waste of one household, or the total waste of a defined geographical region in a given time.85 The functional unit is selected as 1,000 kg (1ton) MSW with the composition given in Fig. 2 in this paper. The system boundary contains building materials production, building materials transportation, construction and decommissioning of the power plant, MSW collection, chemicals production, MSW and chemicals transportation, incineration power generation, residues treatment and flue gas treatment. Major unit processes of this LCA are shown in Fig. 5.


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Fig 5. Unit processes boundary of the life cycle analysis.

The electricity generation and emissions data of MSW incineration without CCS use the average values for one ton of MSW under stable operation in 2014, which were directly measured from the LiKeng waste incineration power plant. Currently, there is no MSW plant with CCS is in operation. Thus, a large amount of data related to post-combustion capture, oxy-fuel combustion, and CO2 compression are collected from literature or laboratory data. Table 2 lists the main data references. The remaining life cycle stages, such as transportation, building material and chemicals production, and indirect processes, are obtained from the Ecoinvent 3.3 database. Table 2. Main data resources Unit process

Item

Reference

grate incineration power plant

Building material consumption

86

Furnace ash loader

Energy consumption and gases emissions

87

Electricity and material consumption

88

Electricity and material consumption

88

Building material consumption for infrastructure

55

MSW bottom ash treatment with 60% bottom ash recovery MSW fly ash treatment



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

MEA-based

post-capture Thermal energy and electricity consumption

system V/PSA post-capture system

55, 89

Investment cost per MW

18, 90

Electricity consumption

64

Material consumption for infrastructure

64

Investment cost per MW

63


5

Investment cost per ton of O2

91

Energy and building material consumption

55


5

Investment cost per ton of CO2

18

Air separation unit

CO2 compressor

Page 16 of 53

Life cycle inventory analysis The following assumptions are made in the life cycle inventory analysis: (1) All heat and power consumed in circulating water, traditional flue gas treatment units, oxygen production, CCS unit are provided by this power generation system itself. In this MSW power plant without CCS (Case 0), the power supply for various pumps, fans, and traditional flue gas treatment units (except the CCS units) is calculated by 22% of the generating capacity, according to the actual data from the targeted waste incineration power plant. The remaining electricity is sent to the electric power grid. (2) The operation lifetime of all capital equipment except for the adsorbents is designed to be 30 years, whereas the lifetime of the adsorbent (zeolite 13X) in P/VSA system is assumed to be 5 years. 64 (3) The CO2 capture efficiency of MEA absorption, P/VSA and oxy-fuel combustion capture technologies are assumed to be 95%, 90%,64 and 95%,86 respectively. (4) Based on the location of this MSW incineration power plant, the average energy consumption for collection from households or roadsides is 0.2 L petrol/ton, caused by frequent starts and stops.92 The collected MSW is transported to the power plant by lorry with Euro III emission standards, and the average transportation distance to the plant is 35 km. All building materials are transported by train (electricity) with the distance of 100 km.


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(5) The bottom ash and fly ash are handled separately. The bottom ash is collected using the ash loader, and then 60% of the bottom ash is reused for metal and inert material recovery. The remaining bottom ash is transported to a landfill plant. No fly ash is reused, and all fly ash is transported to a hazardous waste disposal plant after solidification. (6) The system boundary about fuel starts from MSW collection. MSW production and upstream are excluded, so the photosynthesis during growth of biogenic fraction is ignored. The emission during collection, transportation and operation process of all composition in MSW is calculated. The energy, materials and emissions inventory to deal with 1 ton of MSW for the four cases is shown in Table 3. Compared with the power plant without CCS, power plants with CCS have lower electricity generation and less direct gaseous emissions. Table 3. Energy, materials and emissions inventory to process 1 ton of MSW

Case 0: Item

Unit

without CCS

Case 1: with MEA absorption

Case 2: with P/VSA

Case 3: with oxyfuel combustion

Electricity

kW·h

−272.51

−72.99

−126.99

−111.01

Fuel oil

MJ

0

0.01

0.01

0.01

Gasoline

kg

0.33

0.33

0.34

0.33

Diesel

kg

0.45

0.45

0.45

0.45

MSW

kg

1000

1000

1000

1000

Concrete

kg

2.48

2.49

2.48

2.52

Building

Steel

kg

4.76

4.77

4.76

4.79

materials

Polyethylene

kg

0

0

0

1.26E-4

Aluminum

kg

0.41

0.41

0.41

0.41

Raw

Copper

kg

0

4.19E-05

0

4.19E-05

materials

Gravel

kg

90.13

90.94

90.13

90.13

Zeolite

kg

0

0

1.21

0

Fresh water

kg

3132.5

3601.67

3132.5

3691.22

Activated

kg

0.88

0.92

0.88

0.88

Energy

Fuel



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carbon Chemicals

Direct gas emission

Direct solid emission

NaOH

kg

0

0.07

0

0

Limestone

kg

7.71

7.71

7.71

0

Urea

kg

4.60

4.60

4.60

0

MEA

kg

0

0.84

0

0

CO2

kg

586.46

29.32

58.659

29.32

MEA

kg

0

0.01

0

0

SOx

kg

0.04

1.87E-04

0.04

0

NOx

kg

0.31

0.30

0.31

0.33

HCl

kg

0.19

0.01

0.19

0

PM2.5

kg

0.20

0.10

0.20

0.20

NH3

kg

0.02

0.13

0.02

0.02

Hg

kg

5.16E-6

2.45E-7

5.16E-6

0

CO

kg

0.11

0.11

0.11

0

Cd

kg

1.36E-5

1.09E-5

1.36E-5

1.36E-5

Pb

kg

1.31E-4

1.04E-4

1.31E-4

1.31E-4

Dioxin

kg

As

kg

Cr

kg

3.54E-8

2.84E-8

3.54E-8

3.54E-8

Ni

kg

2.58E-5

2.06E-5

2.58E-5

2.58E-5

Al

kg

2.6E-4

2.6E-4

2.6E-4

2.6E-4

Cu

kg

2.3E-5

1.83E-5

2.3E-5

2.3E-5

PAH

kg

2.67E-4

2.67E-4

2.67E-4

2.67E-4

PCDD/DFs

kg

3.2E-5

2.55E-5

3.2 E-5

3.20E-5

VOCs

kg

0.01

0.002

0.01

0.01

HF

kg

9.3E-4

9.3E-4

9.3E-4

9.3E-4

Bottom ash

kg

198.92

200.70

198.92

198.92

Fly ash

kg

36.01

36.01

36.01

36.01

Gypsum

kg

9.96

9.96

9.96

0

1.40E13 3.54E10

1.40E-13

2.83E-10


1.40E13 3.54E10

1.40E-13

3.54E-10

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Life cycle impact assessment Based on the life cycle inventory analysis results, ReCiPe methodology is used to conduct life cycle impact assessment. Resource consumption and emissions, including the related upstream and down-stream activities, are calculated and systematically analyzed. Three damage (endpoint) categories, namely, ecosystem quality, human health, and resources, are taken into account, together with 17 impact (midpoint) categories: agricultural land occupation (ALO), ecosystems climate change (ECC), freshwater ecotoxicity (FET), freshwater eutrophication (FE), marine ecotoxicity (MET), natural land transformation (NLT), terrestrial acidification (TA), terrestrial ecotoxicity (TET), urban land occupation (ULO), human health climate change (HCC), human toxicity (HT), ionizing radiation (OR), ozone depletion (OD), particulate matter formation (PMF), photochemical oxidant formation (POF), fossil depletion (FD) and metal depletion (MD). The first nine midpoint categories are for ecosystem quality, the last two midpoint categories belong to resources, and the other six midpoint categories relate to human health. Results interpretation Based on the life cycle inventory analysis and life cycle impact assessment, the last step of LCA summarizes the results of life cycle impact indicators, identifies significant issues, and provide suggestions for improvement of environmental sustainability of MSW incineration with and without CCS.84 Interpretation is the most versatile phase in LCA and it reflects the identified limitations and the goal of the study. More insightful suggestions are given by sensitivity analysis. Techno-economic analysis Techno-economic analysis aims to determine the economic performance of a MSW incineration power plant with or without CCS, and to identify the most economically viable CCS technology. To simplify the calculation process, the following assumptions are made in this techno-economic analysis: (1) All currencies other than USD$ are converted using the average exchange rate from the year of the cost data,93 and all the capital cost are then updated to values in 2014 using the Chemical Engineering Plant Cost Index (CEPCI).94 (2) The discounting rate is set at 5%, and the inflation rate is 1.13%.16 The total annual cost ( C AT ) is expressed as:

C AT = C AI + CO + CD

(11) 19 ACS Paragon Plus Environment


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where C AI , CO , and CD are the annual investment cost, annual total operation cost, and decommissioning cost, respectively. Capital investment Total capital investment ( CI ) is the sum of equipment cost, site preparation cost, buildings cost, and direct and indirect installation costs (engineering, contractor fees, start-up, etc.). The investment cost of an MSW incineration without CCS is collected directly from the existing power plant, whereas the techno-economic data related to MEA absorption, P/VSA and oxy-fuel combustion capture are collected from literature.18, 63, 90-91 The boiler in the oxy-fuel combustion system should be upgraded and added into the flue gas recycle unit and steel frame item, so there is an extra investment cost of 7%.16, 95 Based on the actual data of the target MSW incineration power plants, the construction period of power plants is set to be two years. The proportion of bank loan and owned capital/cash is set to be 70% and 30% in investment, respectively. The interest rate is set at 5.58%. The calculation of loan interest ( I j ) is as follow:

I j = （Pj -1 + Aj）⋅ s

(12)

where I j and Aj are loan interest and loan amount in the jth year, respectively, Pj -1 is the sum of capital and interest in the (j-1)th year, and s is interest rate. The annual investment cost (CAI ) is calculated by:96

C AI= f ⋅ CI

(13)

where f is obtained from literature:97   q（k + p）− 1 （q p − 1）    f  = −   q -1）⋅ q（k + p） （  q -1）⋅ q p   （ 

−1

(14)

q =（1 + i）（ ⋅ 1 + r）

(15)

where p is the construction period; k is the amortization period; i is the discounting rate; r is the inflation rate. Total operation cost Total operation cost ( CO ) includes operation and maintenance (O&M) cost, fuel cost, miscellaneous chemicals cost, as well as residual treatment cost. O&M costs include the fixed operating costs, variable operating costs, overhaul and spare parts costs, etc. Fixed operating costs 20 ACS Paragon Plus Environment

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include staff salaries, maintenance cost, sewage charges, management fees, and insurance. Variable operating costs include consumable materials (excluding fuel), such as water, oil materials and air filter media. Overhaul and spare parts costs contain the expenses of labor, materials and thermal components.5 According to Andersson et al., O&M costs are generally calculated based on investment cost and power supply.17 Fuel costs include MSW collection, MSW transport and subordinate fuel cost. The unit cost of MSW collection and transportation is $10.45/ton and $7.04/ton, respectively.98 The unit prices of diesel, limestone, urea, activated carbon, NaOH, MEA, zeolite 13X are set to be $560/ton, $35.5/ton, $371.2/ton,99 $807.0/ton, $161.4/ton, $0.97/kg, and $1/kg, respectively.100 The treatment costs of bottom ash and fly ash are set to be $3.2/ton and $106.2/ton, respectively. Decomissioning cost Decomissioning cost ( CD ) is the cost paid for the cleanup and destruction at the ending of product's life cycle. The decommissioning of some equipment can yield a certain amount of income, and their decommissioning cost should be negative. However, some equipment unit require human, material, and financial resources rather than generating any residual income, so their decommissioning cost is positive. The MSW incineration power plant’s demolition cost at the end of the life cycle is assumed to be equal to its salvage value, so the CD is 0. Revenues The disposal fees (also called tipping fees), subsidies, and general government budgets are main revenues of MSW treatment plants. These revenues (RT) of MSW-to-energy are $25.6/ton.101 Revenue from sale of produced electricity (RE) is another source of income. The online electrovalency of MSW incineration power project is set as $0.105/kW·h.102 The total profit (PT) is expressed as:

PT = RT + RE −

( C AI − CO − CD )

(16)

mMSW

where mMSW is annual MSW treatment (ton/yr). Economic penalty of CO2 avoidance (PAC) The CO2 avoidance cost (CAC) is an economic indicator that is widely used to measure the increment in generation cost for avoiding the emission of CO2 in the field of techno-economic evaluation of fossil fuel power plants with CCS.5, 16 MSW incineration power plants aim to treat 21 ACS Paragon Plus Environment


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MSW and generate power at the same time and MSW treatment brings about revenue. Thus, total profit (PT) is more typical and more complete than total cost (CT) to reflect the economic performance for one ton of treated MSW. Therefore, we proposed a new economic indicator (PAC) for techno-economic analysis of MSW power plants with CCS, to better reflect the penalty of avoiding CO2 emission ($ per ton of CO2 avoidance). PAC can be calculated by: PAC =

(P (m

T − capture c − air

− PT − air )

(17)

− mc −capture )

where mc means the CO2 emission amount per ton of MSW (ton), the subscript ‘capture’ and ‘air’ means the power plants with carbon capture units and conventional power plant without CO2 capture, respectively.

TOPSIS calculation We employ a MCDA approach, in order to integrate LCA and techno-economic analysis results and to systematically compare the environmental impacts and economic performance of the technology/process alternatives of MSW power plants with CCS. There are a large number of MCDA methods.103 TOPSIS method is considered as one of the most popular approaches.104 The TOPSIS approach simultaneously considers the distances to both positive and negative ideal solutions and requires fewer user inputs.105-106 Thus, TOPSIS is considered to be an MCDA approach better than others, such as Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) and Viekriterijumsko Kompromisno Rangiranje (VIKOR).107 The complexity of TOPSIS is lower and simpler than the Elimination and Choice Translating Priority III (ELECTRE III) and Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE).108 Moreover, TOPSIS has the same number of steps regardless of the number of attributes.109 The key steps involved in TOPSIS methodology are: Step 1: Calculate the normalized decision matrix: = r ij

fij , i 1,= 2,..., m; j 1, 2,...n =

(18)

m

∑f j =1

2 ij

where r ij is the normalized value for each criterion, f ij is original value for each criterion, m is the number of alternatives (m = 4), and n is the number of criteria (n = 3). 22 ACS Paragon Plus Environment

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Step 2: Calculate the weighted normalized decision matrix:

1, 2,..., m; j = 1, 2,...n v ij =× rij w j , i =

(19)

where v ij is the weighted normalized value for each criterion and w j is criterion weight from the matrix in AHP. Step 3: Calculate the positive ( A+ ) and negative ( A− ) ideal solutions:

  +  −  A+ = {v1+ , v2+ ,...vn+ } =  max j ∈ I  ,  min j ∈ I   1≤ j ≤ m 1≤ j ≤ m     

(20)

  +  −  A− = {v1− , v2− ,...vn− } =  min j ∈ I  ,  max j ∈ I   1≤ j ≤ m 1≤ j ≤ m     

(21)

where I + and I − are benefit criteria and cost criteria, respectively. Step 4: Calculate the separation measures of each alternative: = Di+ = Di−

n

∑ (v j =1

ij

n

∑ (v j =1

ij

− v +j ) 2

(22)

− v −j ) 2

(23)

where Di+ and Di− are the separation from positive ideal and negative ideal solutions, respectively Step 5: Calculate the relative closeness coefficient ( Ci ) for each alternative: Ci =

Di− Di+ + Di−

(24)

Step 6: Rank alternatives based on their Ci values An alternative with a higher Ci value is considered as a better one. The alternative with the highest Ci is considered as an overall priority. In this study, TOPSIS is used to rank the MSW incineration power plants without CCS, with MEA absorption, with P/VSA and with Oxy-fuel combustion by calculating their corresponding relative closeness coefficient.



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AHP calculation The subjective preferences of decision makers are not fully considered by TOPSIS. AHP method can be combined with TOPSIS to overcome this issue.110 The AHP method allows multistakeholder inclusion. AHP is the most popular MCDA method in waste management.111 112 AHP simplifies complicated and ill-structured problems by arranging the decision attributes and alternatives in a hierarchical structure with the help of a series of pairwise comparisons.113 It is scalable, and can easily adjust the size to accommodate decision-making problems due to its hierarchical structure.108 The AHP method has been proven to be effective and feasible to aggregate the economic, resource, and environmental objectives.114-115 The key steps in AHP are given below:110 Step 1: Stratify the decision-making process in a hierarchy of levels with goal at the top level, followed by criteria, sub-criteria, and alternatives at the bottom level. Step 2: Saaty’s 1–9 scale is used to judge the relative importance of each criterion in Pairwise comparisons.110 A larger value in pair-wise comparison means larger differences between criteria levels. Step 3: For each comparison matrix, maximum eigenvalue, consistency index ( CI ), consistency ratio ( CR ), and normalized eigenvector are calculated to obtain priority weights for each criteria/alternative.

CI =

(λ max −n) (n − 1)

(25)

where λ max is obtained by calculating the scalar product of the principal eigenvector and the vector of column sums of the matrix.116 CR =

CI RI

(26)

where RI is the Random Consistency Index. Step 4: In order to overcome the unreasonable subjectivity in pair-wise comparison, a consistency test is necessary to evaluate judgments. Once a failure ( CR >10%) appears, pair-wise comparisons must be reset. The inconsistency with the CR value lower than 10% is acceptable. In this study, AHP is used to weight resource consumption, environment impact and economic criteria for the power plant with or without CCS from the perspectives of local


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government, enterprise, residents, and “equal” weight, respectively, in order to reflect different priorities for MSW management from various stakeholders’ perspectives.

Results and Discussion Resource use and environmental impacts Fig. 6 presents the midpoint impacts caused by direct emissions for the four cases considered in this work. Compared with power generation without CCS, power plants with MEA absorption, P/VSA, and oxy-fuel combustion reduce the total impact of direct emissions by 91.36%, 85.14%, and 90.06%, respectively. This is mainly due to sharp decreases in ECC and HCC. Since P/VSA technology has the lowest capture efficiency, the impact values caused by direct emissions for the power plant using P/VSA are larger than those using the other two capture technologies.

Fig. 6 Various ReCiPe midpoint impacts caused by direct emissions As waste incineration produces electricity, the avoided inputs and outputs from the traditional production of the Guangdong electricity mix are considered as indirect negative emissions. Indirect emissions result from production and transportation of additives including limestone, activated carbon, MEA solvent and urea, and NaOH for flue gas treatment systems. Fig.7 presents the midpoint impacts in the operation process considering direct emissions and indirect emissions. 25 ACS Paragon Plus Environment


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Thanks to electricity production and flue gas treatment, impacts on ecosystem quality, human health and resources during operations of the MSW incineration power plants with CCS are negative (see Fig.8), indicating that an advanced MSW incineration can benefit the environment. During the whole operation process, the order of decreasing ecosystem quality performance and human health performance are: Case 3 (with oxy-fuel combustion), Case 2 (with P/VSA), Case 1 (with MEA absorption) and Case 0 (without CCS); whereas the order of decreasing resources performance is Case 0 (without CCS), Case 2 (with P/VSA), Case 3 (with oxy-fuel combustion) and Case 1 (with MEA absorption). Although the direct emissions are the smallest, the MEA absorption case requires the most energy and results in worse environmental and resources performances than the other two cases using CCS technologies.

Fig.7 Various ReCiPe midpoint impacts caused by direct and indirect emissions during operation process of MSW power plants


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Fig.8 Total ecosystem quality, human health and resources impacts in the operation process and in the full life cycle



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Fig.9 Various ReCiPe midpoint impacts in the full life cycle of MSW power plants with and without CCS Application of CCS to MSW incineration power plants alleviates pollution but increases resource consumption. Compared with Case 0 (without CCS), the total ecosystem quality impact values of Case 1 (with MEA absorption), Case 2 (with P/VSA), and Case 3 (with oxy-fuel combustion) at the life cycle level reduce from 4.35 to −0.70, −1.39, and −1.64, respectively, as shown in Fig. 8. The total human health impact values of Case 1 (with MEA absorption), Case 2 (with P/VSA), and Case 3 (with oxy-fuel combustion) reduce from 4.77 to −0.70, −2.09, and −2.34, respectively. The total resources impact values of Case 1 (with MEA absorption), Case 2 (with P/VSA), and Case 3 (with oxy-fuel combustion) increase from −7.34 to −0.91, −2.57, and −2.26, respectively. The ranking of the environmental impacts across full life cycle and at the operation stage only are identical for four cases. The reason is that the operation stage plays a predominant role in the whole life cycle, and other upstream processes have minor life-cycle environmental impacts. Fig. 9 illustrates various life-cycle midpoint impacts. For the full-life cycle, the most important impact categories are FD, HCC and ECC, followed by PMF and MD, with HT, ALO and ULO ranked behind. MSW incineration power plants have no obvious effect on other impact categories except the above eight impact categories. For all cases, the OZD value is positive but its value is much smaller than other impact categories, so it is not a main concern. 28 ACS Paragon Plus Environment

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Techno-economic performance The major items of investment cost of a MSW incineration power plant with or without CCS are listed in Table 4. The investment costs of base plant do not account for the investment of emission control units, such as de-NOx device, de-SOx device, bag filter, CCS device, and leachate treatment. A comparison of investment costs of Case 1, Case 2 and Case 3 shows that the capital cost of oxy-fuel combustion unit is higher than the two post-combustion units. McIlveenWright et al.117 and Al-Qayim et al.118 also found the costs of the ASU, flue gas recycling equipment, and piping in oxy-fuel combustion system are approximately 1.5 times of the cost of MEA absorption system. However, de-NOx and de-SOx devices in the post-combustion plants cost more than those in the oxy-fuel combustion plant, due to a smaller flue gas flow and a different removal process of NOx and SOx after gas recycling.119 Compared with the MEA absorption process, the investment cost of the P/VSA equipment is higher with a larger area mainly because of a much lower unit productivity of the column packed with zeolite 13X.60 The total investment cost of Case 3 (with oxy-fuel combustion) is larger than that of Case 1 (with MEA absorption), but smaller than that of Case 2 (with P/VSA). MM$ in Table 4 and Table 5 means millions of $ (1 MM$=1,000,000 $). Annual O&M costs and total costs of MSW incineration power plant with and without CCS units are shown in Table 5 and Table 6, respectively. The CT values of the three Cases with CCS units are in the range of $49.9~$52.8/ton, greater than CT values of the conventional MSW incineration combustion plant ($48.1/ton). For all cases, more than 40% of CT is the investment cost, indicating that a promotion in the investment saving is the most effective path to reduce the total cost. As shown in Table 6, MSW collecting and transportation costs account for a large portion of CT, indicating that a promotion in the MSW management level is effective to reduce CT.

Table 4. Investment costs of MSW incineration power plant with and without CCS units



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Item

Unit

Case 0: without CCS

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Case 1:

Case 2:

with MEA

with

absorption

P/VSA

Case 3: with oxyfuel combustion

Base plant

MM$

12,750

12,750

ASU/post-capture devices

MM$

0

1,644

2,142

1,666

Boiler upgrades

MM$

0

0

0

893

MM$

0

618

618

681

MM$

0

2,262

2,760

3,240

MM$

3,664

5,926

6,424

6,064

Total static investment

MM$

16,414

18,676

19,174

18,814

Interest in the first year

MM$

256

292

300

294

Interest in the second year

MM$

591

673

691

678

Total dynamic investment

MM$

17,262

19,641

20,165

19,786

CO2 cleaning and compressor Total CCS system Environmental protection units

12,750

12,750

Table 5. Annual O&M costs of MSW incineration power plant with and without CCS units

Item

Plantvariable Plant-fixed

CCS devices Other fuel gas treatment Total

Unit

MM$/ yr MM$/ yr MM$/ yr MM$/ yr MM$/ yr

Case 0:

Case 1: with

Case 2:

Case 3: with

without

MEA

with

oxy-fuel

CCS

absorption

P/VSA

combustion $1.3/MW

33.22

33.22

33.22

36.43

191.25

191.25

191.25

204.64

1.50%17

0.00

65.75

85.68

66.65

4%

33.58

43.83

43.83

12.75

4%

258.05

334.04

353.97

320.47

4%


h17

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Table 6. Costs and profit of the MSW incineration with and without CCS

Item

Unit

Case 0:

Case 1:

Case 2:

Case 3: with

without

with MEA

with

oxy-fuel

CCS

absorption

P/VSA

combustion

Investment cost

$/ton

19.758

22.523

23.132

22.685

O&M cost

$/ton

3.535

4.592

4.869

4.404

MSW collecting cost

$/ton

10.447

10.447

10.447

10.447

MSW transport cost

$/ton

7.035

7.035

7.035

7.035

Desulfurizer cost

$/ton

0.274

0.274

0.274

0.000

Denitrification agent cost

$/ton

1.708

1.708

1.708

0.000

MEA solvent cost

$/ton

0.000

0.811

0.000

0.000

other chemicals cost

$/ton

0.707

0.753

0.707

0.707

Ash treatment cost

$/ton

4.466

4.472

4.466

4.466

$/ton

48.101

52.786

52.810

49.916

MSW treatment revenue

$/ton

26.953

26.953

26.953

26.953

Electricity sale revenue

$/ton

28.639

7.709

13.374

11.698

Profit

$/ton

7.539

-18.075

-12.264

-11.095

——

32.443

23.236

19.914

——

45.975

37.520

33.446

Total MSW treatment cost

Balance CO2 sale revenue

Economic penalty of CO2 avoidance

$ of 1 ton CO2 avoidance $ of 1 ton CO2 avoidance

The revenues and the profits of different cases are also shown in Table 6. Due to the increment of total cost and the reduction of electricity revenue, profits of three Cases with CCS units are negative, indicating that the current CCS technologies are still economically immature to be applied to a MSW incineration power plant. The comparison of total economic performances of 31 ACS Paragon Plus Environment


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the three CCS technologies shows that oxy-fuel combustion has the best performance, followed by P/VSA and MEA absorption. The economic penalty of CO2 avoidance of MSW incineration power plants with CCS units are also shown in Table 6. In the MSW incineration power plant, the PAC values for Case 1 (with MEA absorption), Case 2 (with P/VSA technology) and Case 3 (with oxy-fuel combustion) are $45.98, $37.52 and $33.45 per ton of CO2 avoidance, respectively. Previous studies showed that the CO2 avoidance cost ranged from $26 to $42 per ton of CO2 avoidance in a coal power plant with CCS.16-18 There is no obvious difference in economic penalty caused by CCS units between the MSW incineration power plants and coal power plants. In order to balance between financial revenue and expenditure (PT= $0/ton), the revenues of MSW-to-energy for the Case 1 (with MEA absorption), Case 2 (with P/VSA technology) and Case 3 (with oxy-fuel combustion) need to increase from $25.6/ton to $45.0/ton, $39.2/ton and $38.0/ton, respectively. If the revenues of MSW-to-energy remain $25.6 per ton of MSW and the market price of industrially CO2 avoidance reaches $32.4, $23.2, and $19.9 per ton of CO2, respectively, Case 1, Case 2 and Case 3 might become profitable. AHP-TOPSIS result Table 7 lists the pairwise comparison and weighting factors in AHP based on the view of local government who gives priority to sustainable development. A criterion on the left is compared with another indicated at the top and the numerical values basically express how many times or how strongly more important a criterion is than the other criteria in comparison. The values ‘3’ and ‘5’ mean that the local government considers that resource consumption indicator is moderately and strongly more important than environmental indicator and economy indicator, respectively. For the matrix A related to the pair-wise comparison, the diagonal values of the matrix are always‘1’, and all judgments below the diagonal are the reciprocal of those above.

3 5  1   A =  0.333 1 3  . The weight factors are calculated by Eigenvalue Method, and the vetor  0.2 0.333 1    T w containing the weight factors is the eigenvector of the matrix A. w =（0.633, 0.260, 0.106） .

λ max is its maximal eigenvalue ( λ max =3.04 ). Aw = λ w . According to Equations (25) and (26), CI and CR are 0.02 and 0.034. CR is smaller than 0.1, showing that the consistency of this 32 ACS Paragon Plus Environment

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pairwise comparisons is acceptable. Local government gives the top priority to the resource consumption, and might have minimum concern on the economy. Therefore, the weight factors of resource, environment and economy progressively decrease. Table 7. Pair-wise comparison and weight factors for AHP based on the view of local government35 Resource

Environment

Economy

Weight factor

Resource

1

3

5

0.633

Environment

0.333

1

3

0.260

Economy

0.2

0.333

1

0.106

Table 8. TOPSIS result for a MSW power plant with or without CCS unit based on the view of local government Item

TOPSIS result Resource consumption Environment impact

Economy

Matrix Case 0

−7.327

1.782

7.539

Case 1

−0.905

−2.305

−18.075

Case 2

−2.561

−6.045

−12.264

Case 3

−2.250

−6.230

−11.095

Normalized decision matrix Case 0

−0.901

0.195

0.294

Case 1

−0.111

−0.252

−0.705

Case 2

−0.315

−0.660

−0.478

Case 3

−0.277

−0.680

−0.433

Weighted normalized decision matrix Case 0

−0.571

0.051

0.031

Case 1

−0.071

−0.066

−0.075

Case 2

−0.199

−0.172

−0.051

Case 3

−0.175

−0.177

−0.046

Ideal solution positive value

−0.571

−0.177

0.031

negative value

−0.071

0.051

−0.075



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D+

D−

Ci

Case 0

0.228

0.511

0.692

Case 1

0.523

0.116

0.182

Case 2

0.380

0.258

0.405

Case 3

0.403

0.253

0.385

The TOPSIS results from the local government’s perspective are summarize in Table 8. As MSW incineration produces electricity, the consumption of fossil fuels from the traditional production of the Guangdong electricity mix are avoided. The indicator corresponding to resource consumption is negative for four cases in matrix. The emissions from the traditional generation of the Guangdong electricity mix are avoided but MSW incineration brings in other emissions. Overall, case 0 without CCS has bad effect to environment but cases with CCS has good effect to environment, the indicator corresponding to environmental impacts is positive for case 0, but negative for Case 1, Case 2 and Case 3. For case 0 without CCS, income is greater than the cost, and its economic indicator is positive. Due to the increment of total cost and the reduction of electricity revenue, profits of three Cases with CCS units are negative, and their economic indicators are negative in matrix. The normalized decision matrix is obtained using formula (18). The weighted normalized decision matrix is obtained by multiplying normalized decision matrix with the weight factors from AHP calculation. The economic indicator reflecting commercial profitability belongs to benefit criteria (the greater, the better), and thus its positive and negative ideal solution are maximum and minimum values. Different from the economic indicator, resource consumption and environment impact are cost criteria, so their positive and negative ideal solutions take minimum and maximum values, respectively. The separation measures of each alternative from positive ideal and negative ideal solutions ( D + and D − ) are calculated by Equations (22) and (23). The Ci values calculated by Equation (24) simultaneously reflect the distances to both positive and negative ideal solutions, and the alternative with the largest Ci value is chosen as the optimum. The comparison of Ci values in Table 8 shows that the MSW incineration power plant without CCS outranks the power plants with CCS from local government’s standpoint. Different stakeholder groups put different weights on each individual criterion. Enterprise gives the top priority to the economic indicator, and might have minimum concern on the 34 ACS Paragon Plus Environment

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environment. Residents give preference to the environment with energy consumption as the least concerned one. Fig. 10 summarizes the final ranking of the AHP-TOPSIS calculation from the perspective of local government, enterprise, residents and an ‘‘equal’’ weight (0.33 for each criterion).

Fig. 10 Relative closeness coefficients of the AHP-TOPSIS calculation from the perspectives of local government, enterprise, residents, and ‘‘equal’’ weight

From the perspective of local government or enterprise, CCS has an unfavorable effect on the performance of the MSW incineration power plant, no matter which CCS technologies is adopted. From the perspectives of residents only, Case 3 (with oxy-fuel combustion) is the best choice, followed by Case 2 (with P/VSA technology) and Case 1 (with MEA absorption), with Case 0 (without CCS) being the worst. Residents consider the MSW power plants with CCS technologies as a better option than a traditional MSW incineration power plant. Among the three cases containing CCS, the P/VSA technology case and oxy-fuel combustion case have similar relative closeness coefficients ( Ci ), always much larger than the MEA absorption case. The disparity in comprehensive performance between four cases from the equal perspective is the smallest.



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However, their Ci differs greatly from the enterprise’s perspective with the highest economy weight. In LCA and TEA, the results on the resource consumption, environmental burden, and financial cost are reported separately. Among the three CCS options, MEA-based absorption has the advantages of the smallest direct emissions and the lowest capital investment, P/VSA postcapture has the advantages of the smallest resources consumption, and oxy-fuel combustion has the advantages of the best ecosystem quality and human health performance and the smallest economic penalty. The above separated LCA and TEA results only provide qualitative comparison based on a specific criterion. Instead, the weighted data by implementing AHP-TOPSIS provides a comprehensive indicator quantitatively. The P/VSA technology and oxy-fuel combustion have similar Ci values, always much larger than MEA-based absorption. Although government and companies give many policy support and financial investment for MSW incineration power plant projects, the development of waste incineration power plants still cannot meet the demand of growing MSW production mainly due to public opposition. The public acceptance is considered most critical for the effectiveness of any integrated MSW management scheme.120 From the residents’ perspective, MSW power plants with CCS technologies perform better than traditional MSW incineration power plants. This means the application of CCS technologies to the MSW treatment can reduce public opposition and give support the growth of MSW incineration industry. Sensitivity analysis In order to better understand the impacts of altering parameters on the economic performance of the MSW incineration power plant with CCS, a sensitivity analysis is conducted. For illustration, Fig. 11 presents the sensitivity analysis result of economic penalty of CO2 avoidance (PAC) based on changes in energy consumption of CCS systems, investment of CCS systems, online electrovalency, unit MSW treatment revenue, and MSW treatment capacity. Although online electrovalency has no influence on environmental performance of power plants, it always has the same great impact on PAC as energy consumption of CCS systems, and online electrovalency and energy consumption of CCS systems are the most critical factors influencing PAC for MEA absorption and P/VSA cases. The unit MSW treatment revenue has no impact on PAC. The effect of MSW treatment capacity is opposite to the other factors, and larger MSW treatment capacity 36 ACS Paragon Plus Environment

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results in lower PAC. The PAC for oxy-fuel combustion case is much more sensitive to MSW treatment capacity than those for MEA absorption and P/VSA cases, because MEA absorption and P/VSA belong to post-combustion capture which has no direct relationship with furnace combustion, whereas the oxy-fuel combustion has a direct relationship with the furnace efficiency and power generation. When the MSW treatment capacity reaches a certain value, the increment of electricity generation is larger than the energy penalty caused by the oxy-fuel combustion equipment, indicating that oxy-fuel combustion is available for larger-size MSW incineration power plants even under the current techno-economic level of CCS.

Fig. 11 Sensitivity analysis result of economic penalty of CO2 avoidance (PAC): (a) MEA absorption; (b) P/VSA; (c) Oxy-fuel combustion For the target MSW incineration power plant with the capacity of 2000 ton per day, CCS still needs further techno-economic promotion to attract the support from government and enterprise. With the booming development of CCS technology, energy consumption and investment of CCS systems gradually decrease, so it is important to evaluate its application future in MSW-to-energy field. How the decrease in energy consumption and investment of CCS systems influence the total profit, PAC and ReCiPe endpoint scores with respect to MSW power plants is also investigated in this subsection. As shown in Fig. 12, the economic and environmental performance of power plants is much more sensitive to energy consumption of CCS systems than investment of CCS systems. 37 ACS Paragon Plus Environment


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This result is resulted from the decrease of the energy consumption of CCS systems that can dramatically increase the electric power supply and revenue from electricity sale which is critical to the profitability of power plants. Moreover, the decrease of energy consumption of CCS dramatically increases the avoided resource inputs and emissions from the traditional production of the Guangdong electricity mix which are critical factors influencing ReCiPe endpoint scores. The variation of the investment of CCS systems causes the changes in Pt and PAC, due to the change in cost. From the standpoint of environmental performance, since the investment of CCS systems changes the production and transportation of the partial construction materials of CCS process, which is a small share in the whole life cycle, the influence of investment of CCS systems on ReCiPe endpoint scores is negligible. Compared with the investment of CCS systems, their energy efficiency deserves more attention to be effectively improved in future.

Fig. 12 Effects of the energy consumption and investment of CCS systems on the profit, PAC and ReCiPe endpoint scores from different standpoint


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Fig. 13 Effects of the energy consumption and investment of CCS systems on the relative closeness coefficients from different standpoints Fig 13 shows the decrease in energy consumption and investment of CCS systems on relative closeness coefficients ( Ci ) from different standpoints. As shown in Fig. 13, Case 2 using P/VSA technology and Case 3 using oxy-fuel combustion still have similar Ci values, if the investment of CCS systems decreases by 25% or 50%. However, if the energy consumption of MEA, P/VSA and oxy-fuel combustion systems all decreases by 25% or 50%, the Ci of oxy-fuel combustion will become obviously larger than those of P/VSA technology and MEA absorption cases, indicating that the oxy-fuel combustion is the likeliest CCS candidate to be applied in MSW power plants if the energy efficiency is effectively improved in the future. The fluctuations in the energy consumption and investment of CCS systems influence the Ci values, but the changes are not large enough to change the ranking result from the perspective of residents and enterprise. Only when the energy consumption of CCS systems decreases by 50%, the oxy-fuel combustion case will become the highest-ranking technology from the local government’s standpoint. This phenomenon indicates that the promotion in energy efficiency of oxy-fuel combustion system helps to attract the support for application of CCS to MSW incineration field from government. However, the decrease in the energy consumption and 39 ACS Paragon Plus Environment


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investment of CCS systems cannot change the preference of enterprise, because the resource and economic performances of power plants with CCS are still worse than conventional power plants without CCS. If the price of industrially CO2 avoidance is considered, the preference of enterprise will change because the carbon price has a strong impact on the profitability of power plants. Fig. 14 shows the minimum carbon price to make the targeted MSW power plant with oxy-fuel combustion ranking beyond the conventional power plant without CCS under different conditions from the enterprise’s standpoint. For example, the oxy-fuel combustion case will rank first no matter from the perspectives of local government, enterprise, residents, or “equal” weight, if the carbon price reaches $13.57 per ton CO2 avoidance and the energy consumption of CCS systems decreases by 50%. The decrease in investment of CCS systems can decrease the minimum carbon price to make the MSW incineration power plant with oxy-fuel combustion as the most preferred technology.

Fig. 14

Minimum carbon price to make the MSW power plant with oxy-fuel combustion outrank the conventional power plant from the enterprise’s standpoint

The accurate absolute results of environmental impact and costs cannot be expected, since no MSW plants with capture units are currently in operation and some techno-economic assumptions 40 ACS Paragon Plus Environment

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involve in the analysis. Uncertainty of variation of each subsystem in the life cycle with the continuous social development, and absence of local data on CCS equipment and related technological parameters are the error sources of assessment. This paper presents an analysis for a specific case study and the results aim to enable quick assessment of the MSW incineration power plant in Guangzhou. However, the methodology applied in this work is not case specific, and it also can be adopted for other places and other capture technologies. The result of this MSW incineration power with CCS can contribute to the scientific knowledge of MSW-to-energy plants, provide a comparison with other MSW-to-energy plants, and help to improve waste management.

Conclusion In this work, we conducted multi-criteria environmental and economic analyses of MSW grate incineration power plants with CCS. At the full life cycle, the application of CCS to a MSW power plant reduced the ecosystem quality impact value from 4.35 to −0.70~ −1.64, reduced the human health impact value from 4.77 to −0.70 ~ −2.34 and increased the resources impact value from −7.34 to −0.91~−2.57. The most important impact categories were FD, HCC and ECC, followed by PMF and MD, with HT, ALO and ULO ranking behind. Among the three CCS options for MSW power plants, oxy fuel combustion had the best economic performance, followed by P/VSA; with MEA absorption as the worst choice. Their total cost and economic penalty of CO2 avoidance values were in the range of $49.9~$52.8/ton, and $33.45~$45.98/ton, respectively. AHP-TOPSIS results showed that no matter from the perspective of equal weight, government weight, enterprise weight or resident weight, the P/VSA technology and oxy-fuel combustion had similar comprehensive performance, always much better than MEA-based absorption. From the perspectives of residents only, MSW power plants with CCS technologies performed better than a traditional MSW incineration power plant, indicating P/VSA and oxy-fuel combustion technologies can diminish residents’ opposition of MSW incineration projects. However, they still needed the promotion in energy efficiency of CCS and carbon price to attract more support from government and enterprise. The increment of the MSW treatment capacity was useful to improve the economic feasibility of CCS for MSW power plants, especially oxy-fuel combustion technology. The methodology in this paper is aimed to permit quick comprehensive approximate assessment for decision makers, and they also can be adopted for other capture technologies, such as microalgae biofixation, ionic liquid-based chemical absorption, temperature swing adsorption and electric swing adsorption. 41 ACS Paragon Plus Environment


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Acknowledgments The authors acknowledge financial support from National Science Foundation (NSF) CAREER Award (CBET-1643244) and Natural Science Foundation of China (51606071).

Nomenclature A = matrix related to pair-wise comparison

Aj = loan amount in the jth year

C AI = annual investment cost [$/yr] C AT = total annual cost [$/yr] CI = total capital investment [$] Car = carbon content in the MSW on as-received basis [set to be 18.44wt%] Ci = relative closeness coefficient CO = annual total operation cost [$/yr] CD = decommissioning cost [$/yr] CT = total cost [$/ton] CI = consistency index CR = consistency ratio Di+ = separation from positive ideal solution Di− = separation from negative ideal solution Ecompressor = electricity requirement of 1 kg CO2 for compressors [kW·h/ kg ]

E ASU = electricity requirement per m3 of O2 for ASU [kW/ m3 ] f = annual capital recovery factor f ij = original value for each criterion

H ar = hydrogen content in the MSW on as-received basis [27.05wt%] i = discounting rate [set to be 5%];

I j = loan interest in the jth year；


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I + = benefit criteria I − = cost criteria.

k = amortization period [set to be 30 years];

m = number of alternatives [set to be 4] mc − air = CO2 emission amount for 1 ton of MSW from a plant without CO2 capture [ton]; mc −capture = CO2 emission amount for 1 ton of MSW from a plant with CO2 capture [ton]; mO2 = mass flow of oxygen for 1 ton of MSW [ton] mCO2 = mass of CO2 for 1 ton of MSW [ton] mH 2O = mass of water vapor for 1 ton of MSW [ton] mNO = mass of NO for 1 ton of MSW [ton]

mNO2 = mass of NO2 for 1 ton of MSW [ton] mSO2 = mass of SO2 for 1 ton of MSW [ton] mSO3 = mass of SO3 for 1 ton of MSW [ton]

mMSW = annual MSW treatment [ton/yr] M C = molar mass of carbon [12 g/mol]

M CO2 = molar mass of CO2 [44 g/mol] M H = molar mass of hydrogen [1 g/mol]

M H 2O = molar mass of H2O [18 g/mol] M N = molar mass of nitrogen [14 g/mol] M NO = molar mass of NO [30 g/mol]

M NO2 = molar mass of NO2 [46 g/mol] M O2 = molar mass of O2 [32 g/mol] M S = molar mass of sulphur [32 g/mol]

M SO2 = molar mass of SO2 [64 g/mol] M SO3 = molar mass of SO3 [80 g/mol]

n = number of criteria [set to be 3] 43 ACS Paragon Plus Environment


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N = number of compressor stages [set to be 4]

N ar = nitrogen content in the MSW on as-received basis [0.075wt%] p1 = suction pressure [set to be 0.101325 MPa] p2

= discharge

pressure [set to be 11 MPa]

P = the construction period [set to be 2 year] PAC = penalty to avoid the CO2 emission [$ per ton of CO2 avoidance] Pj -1 = sum of capital and interest in the (j-1)th year；

PT = unit profit [$/ton] PT −air = unit profit of a plant without CO2 capture [$/ton] PT −capture = unit profit of a plant with CO2 capture [$/ton] r = rate of inflation [ r =1.13%]

r ij = normalized value for each criterion R = universal gas constant [8.3145 J/(mole K)] RE = revenue from sale of produced electricity [$/ton] RT = revenues of disposal fees [$/ton] RI = random consistency index s = band loan rate

Sar = sulphur content in the MSW on as-received basis [0.15wt%] t= time conversion factor [3600] T = suction temperature [313.15 K] u = rate of unburned carbon [set to be 0.386%]

v ij = weighted normalized value for each criterion w=vetor containing weight factors

w j = criterion weight from the matrix in AHP W = specific work [kJ per kg of CO2]

War = moisture content in the MSW on as-received basis [50wt%] Z = compressibility factor [0.9942] 44 ACS Paragon Plus Environment

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Greek Symbols

α = excess air factor α S − SO = conversion factor from sulphur to SO2 [0.6] 2

α SO − SO = conversion ratio from SO2 to SO3[0.01] 2

3

α N − NO = conversion ratio from N to NOx [0.15] x

λ max = maximum egenvalue of comparison matrix γ = specific heat ratio, equal to cp/cv [1.293759] γ NO = ratio of NO2 in NOx [0.05] 2

γ NO = ratio of NO in NOx [0.95] γ H / O = molar ratio of H and O2 [4] 2

γ H / H O = molar ratio of H and H2O [2] 2

µ1 = first empirical constant in ASU energy requirement [0.00172]

µ2 =second empirical constant in ASU energy requirement [0.1498]

ηis = entropic efficiency [set to be 80%];

ηm = mechanical efficiency [set to be 99%]. 𝜂𝜂𝑜𝑜𝑜𝑜 = oxygen purity [set to be 95%];

β = air leakage ratio [set to be 2%].

References (1) Kumar, A.; Samadder, S. R., A review on technological options of waste to energy for effective management of municipal solid waste. Waste Manage 2017. (2) Brunner, P. H.; Rechberger, H., Waste to energy - key element for sustainable waste management. Waste Manage 2015, 37, 3-12. (3) Lu, J.-W.; Zhang, S.; Hai, J.; Lei, M., Status and perspectives of municipal solid waste incineration in China: A comparison with developed regions. Waste Manage 2017. (4) Raclavská, H.; Corsaro, A.; Hlavsová, A.; Juchelková, D.; Zajonc, O., The effect of moisture on the release and enrichment of heavy metals during pyrolysis of municipal solid waste. Waste Management & Research the Journal of the International Solid Wastes & Public Cleansing Association Iswa 2015, 33 (3), 267-74. (5) Wang, Y.; Zhao, Y. C.; Zhang, J. Y.; Zheng, C. G., Technical-economic evaluation of O2/CO2 recycle combustion power plant based on life-cycle. Sci China Tech Sci, 2010, 53, 3284-3293.



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(6) Gong, J.; You, F., Sustainable design and synthesis of energy systems. Current Opinion in Chemical Engineering 2015, 10, 77-86. (7) Toftegaard, M. B.; Brix, J.; Jensen, P. A.; Glarborg, P.; Jensen, A. D., Oxy-fuel combustion of solid fuels. Prog Energ Combust 2010, 36 (5), 581-625. (8) Singh, B.; Stromman, A. H.; Hertwich, E., Life cycle assessment of natural gas combined cycle power plant with post-combustion carbon capture, transport and storage. Int J Greenh Gas Con 2011, 5 (3), 457-466. (9) Korre, A.; Nie, Z.; Durucan, S., Life cycle modelling of fossil fuel power generation with postcombustion CO 2 capture. Int J Greenh Gas Con 2010, 4 (2), 289-300. (10) Pehnt, M.; Henkel, J., Life cycle assessment of carbon dioxide capture and storage from lignite power plants. Int J Greenh Gas Con 2009, 3 (1), 49-66. (11) Corsten, M.; Ramírez, A.; Shen, L.; Koornneef, J.; Faaij, A., Environmental impact assessment of CCS chains – Lessons learned and limitations from LCA literature. Int J Greenh Gas Con 2013, 13 (13), 59-71. (12) Singh, B.; Strømman, A. H.; Hertwich, E., Life cycle assessment of natural gas combined cycle power plant with post-combustion carbon capture, transport and storage. Int J Greenh Gas Con 2011, 5 (3), 457-466. (13) Carpentieri, M.; Corti, A.; Lombardi, L., Life cycle assessment (LCA) of an integrated biomass gasification combined cycle (IBGCC) with CO 2 removal. Energy Conversion & Management 2005, 46 (11–12), 1790-1808. (14) Schakel, W.; Meerman, H.; Talaei, A.; Ramírez, A.; Faaij, A., Comparative life cycle assessment of biomass co-firing plants with carbon capture and storage. Applied Energy 2014, 131 (9), 441–467. (15) Yue, D.; You, F.; Snyder, S. W., Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges. Computers & Chemical Engineering 2014, 66, 36-56. (16) Xiong, J.; Zhao, H. B.; Zheng, C. G.; Liu, Z. H.; Zeng, L. D.; Liu, H.; Qiu, J. R., An economic feasibility study of O2/CO2 recycle combustion technology based on existing coal-fired power plants in China. Fuel 2009, 88 (1135–1142). (17) Klas Andersson, F. J., Process evaluation of an 865 MWe lignite fired O2/CO2 power plant. Energy Conversion and Management 2006, 47, 3487–3498. (18) Singh, D.; Croiset, E.; Douglas, P. L.; Douglas, M. A., Techno-economic study of CO2 capture from an existing coal-fired power plant: MEA scrubbing vs. O-2/CO2 recycle combustion. Energy Conversion and Management 2003, 44 (19), 3073-3091. (19) Z, F.; S, Z.; X, L.; J, S.; K, W.; H, C., MSW oxy-enriched incineration technology applied in China: combustion temperature, flue gas loss and economic considerations. Waste Manag 2015, 38 (12), 149-156. (20) Yan, W.; Xuehong, H. E.; Yin, S., Technical and economic analysis on a waste oxygenenriched incineration power plant with flue gas near-zero emissions. Thermal Power Generation 2013. (21) Tang, Y. T.; Ma, X. Q.; Lai, Z. Y.; Chen, Y., Energy analysis and environmental impacts of a MSW oxy-fuel incineration power plant in China. Energy Policy 2013, 60 (5), 132-141. (22) Kucukvar, M.; Gumus, S.; Egilmez, G.; Tatari, O., Ranking the sustainability performance of pavements: An intuitionistic fuzzy decision making method. Automation in Construction 2014, 40, 33-43.


Page 47 of 53

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


(23) Kucukvar, M.; Noori, M.; Egilmez, G.; Tatari, O., Stochastic decision modeling for sustainable pavement designs. International Journal of Life Cycle Assessment 2014, 19 (6), 11851199. (24) You, F.; Tao, L.; Graziano, D. J.; Snyder, S. W., Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and inputoutput analysis. AIChE Journal 2012, 58 (4), 1157-1180. (25) Mardani, A.; Zavadskas, E. K.; Khalifah, Z.; Zakuan, N.; Jusoh, A.; Nor, K. M.; Khoshnoudi, M., A review of multi-criteria decision-making applications to solve energy management problems: Two decades from 1995 to 2015. Renewable & Sustainable Energy Reviews 2017, 71, 216-256. (26) Cavallaro, F., Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems. Applied Energy 2010, 87 (2), 496-503. (27) Diakoulaki, D.; Karangelis, F., Multi-criteria decision analysis and cost-benefit analysis of alternative scenarios for the power generation sector in Greece. Renewable & Sustainable Energy Reviews 2007, 11 (4), 716-727. (28) Elghali, L.; Clift, R.; Sinclair, P.; Panoutsou, C.; Bauen, A., Developing a sustainability framework for the assessment of bioenergy systems. Energy Policy 2007, 35 (12), 6075-6083. (29) Heo, E.; Kim, J.; Boo, K. J., Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renewable & Sustainable Energy Reviews 2010, 14 (8), 2214-2220. (30) Jovanovic, M.; Afgan, N.; Radovanovic, P.; Stevanovic, V., Sustainable development of the Belgrade energy system. Energy 2009, 34 (5), 532-539. (31) Papadopoulos, A.; Karagiannidis, A., Application of the multi-criteria analysis method Electre III for the optimisation of decentralised energy systems. Omega-International Journal of Management Science 2008, 36 (5), 766-776. (32) Pohekar, S. D.; Ramachandran, M., Application of multi-criteria decision making to sustainable energy planning - A review. Renewable & Sustainable Energy Reviews 2004, 8 (4), 365-381. (33) Alicia Danae Diaz-Barriga-Fernandez, J. E. S.-A., Neyara Radwan, Fabricio Napoles-Rivera, Mahmoud M El-Halwagi, José María Ponce-Ortega, Strategic Planning for Managing Municipal Solid Wastes with Consideration of Multiple Stakeholders ACS Sustainable Chemistry & Engineering 2017, In press. (34) Tan, S. T.; Ho, W. S.; Hashim, H.; Lee, C. T.; Taib, M. R.; Ho, C. S., Energy, economic and environmental (3E) analysis of waste-to-energy (WTE) strategies for municipal solid waste (MSW) management in Malaysia ☆. Energy Conversion & Management 2015, 102, 111-120. (35) Dong, J.; Chi, Y.; Zou, D.; Fu, C.; Huang, Q. X.; Ni, M. J., Energy-environment-economy assessment of waste management systems from a life cycle perspective: Model development and case study. Applied Energy 2014, 114, 400-408. (36) Solano, E.; Ranjithan, S. R.; Barlaz, M. A.; Brill, E. D., Life-cycle-based solid waste management. I: Model development. Journal of Environmental Engineering-Asce 2002, 128 (10), 981-992. (37) Sara, J.; Stikkelman, R. M.; Herder, P. M. In Assessing complexity of carbon capture and storage using multi-criteria decision-making methods, Systems Conference, 2015; pp 145-152. (38) Jakobsen, J. P.; Roussanaly, S.; Mølnvik, M. J.; Tangen, G., A standardized Approach to Multi-criteria Assessment of CCS Chains. Energy Procedia 2013, 37, 2765-2774.



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

Page 48 of 53

(39) Zhao, M.; Minett, A. I.; Harris, A. T., A review of techno-economic models for the retrofitting of conventional pulverised-coal power plants for post-combustion capture (PCC) of CO 2. Energ Environ Sci 2012, 6 (1), 25-40. (40) Hasan, M. M. F.; First, E. L.; Floudas, C. A., Cost-effective CO2 capture based on in silico screening of zeolites and process optimization. Physical Chemistry Chemical Physics 2013, 15 (40), 17601-17618. (41) Protection, M. o. E. Standard for Pollution Control on the Municipal Solid Waste Incineration. (42) Strazisar, B. R.; Richard R. Anderson; White, C. M., Degradation Pathways for Monoethanolamine in a CO2 Capture Facility. Energy & Fuels 2003, 17 (4), 1034-1039. (43) Knudsen JN, J. J., Vilhelmsen P-J, Biede O, Experience with CO2capture from coal flue gas in pilot-scale: testing of different amine solvents. In In: 9th International Conference on Greenhouse Gas Control Technologies (GHGT-9). Washington DC, USA, 2008. (44) Thitakamol, B.; Veawab, A.; Aroonwilas, A., Environmental impacts of absorption-based CO 2 capture unit for post-combustion treatment of flue gas from coal-fired power plant. Int J Greenh Gas Con 2007, 1 (3), 318-342. (45) Rao, A. B.; Rubin, E. S., A technical, economic, and environmental assessment of aminebased CO2 capture technology for power plant greenhouse gas control. Environmental Science & Technology 2002, 36 (20), 4467-75. (46) IPCC Special Report on Carbon Dioxide Capture and Storage. Intergovernmental Panel on Climate Change; New York, 2005. (47) Nie, Z. G.; Korre A.; S., D., Life cycle modelling and comparative assessment of the environmental impacts of oxy-fuel and post-combustion CO2 capture, transport and injection processes. Energy Procedia 2011, 4, 2510-2517. (48) Chapel, D. G., Mariz, C.L., Ernest, J., Recovery of CO2from flue gases: commercial trends. . In In: Proceedings of the Canadian Society of Chemical Engineers Annual Meeting, Saskatoon, Saskatchewan, Canada., 1999. (49) White, D. C. M.; Strazisar, B. R.; Granite, E. J.; Hoffman, J. S.; Pennline, H. W., Separation and Capture of CO2 from Large Stationary Sources and Sequestration in Geological Formations— Coalbeds and Deep Saline Aquifers. Journal of the Air & Waste Management Association 2003, 53 (6), 643. (50) Dan, G. C.; Mariz, C. L.; Ernest, J., Recovery of CO2 from Flue Gases: Commercial Trends. Society of Chemical Engineers Annual Meeting 1999. (51) Alie, C.; Backham, L.; Croiset, E.; Douglas, P. L., Simulation of CO 2 capture using MEA scrubbing: a flowsheet decomposition method. Energy Conversion & Management 2005, 46 (3), 475-487. (52) Knudsen, J. N.; Vilhelmsen, P. J.; Biede, O.; Jensen, J. N., CASTOR 1 t/h CO2 absorption pilot plant at the Elsam Kraft A/S Esbjerg power plant - First year operation experience. 2006. (53) Rao, A. B.; Rubin, E. S.; Keith, D. W.; Morgan, M. G., Evaluation of potential cost reductions from improved amine-based CO 2 capture systems. Energy Policy 2006, 34 (18), 3765-3772. (54) Abu-Zahra, M. R. M.; Schneiders, L. H. J.; Niederer, J. P. M.; Feron, P. H. M.; Versteeg, G. F., CO 2 capture from power plants : Part I. A parametric study of the technical performance based on monoethanolamine. Int J Greenh Gas Con 2007, 1 (1), 37-46. (55) Koornneef, J.; van Keulen, T.; Faaij, A.; Turkenburg, W., Life cycle assessment of a pulverized coal power plant with post-combustion capture, transport and storage of CO2. Int J Greenh Gas Con 2008, 2 (4), 448-467.


Page 49 of 53

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


(56) Peeters, A. N. M.; Faaij, A. P. C.; Turkenburg, W. C., Techno-economic analysis of natural gas combined cycles with post-combustion CO 2 absorption, including a detailed evaluation of the development potential. Int J Greenh Gas Con 2007, 1 (4), 396-417. (57) Yu, C.-H.; Huang, C.-H.; Tan, C.-S., A review of CO2 capture by absorption and adsorption. Aerosol Air Qual. Res 2012, 12 (5), 745-769. (58) Damen, K. Reforming fossil fuels use—the merits, costs and risks of carbon capture and storage. Utrecht University, Utrecht, 2007. (59) And, H. H. K.; Tan, R. B. H., Life Cycle Investigation of CO2 Recovery and Sequestration. Environmental Science & Technology 2006, 40 (12), 4016-24. (60) Liu, Z.; Wang, L.; Kong, X.; Li, P.; Yu, J.; Rodrigues, A. E., Onsite CO2 Capture from Flue Gas by an Adsorption Process in a Coal-Fired Power Plant. Industrial & Engineering Chemistry Research 2012, 51 (21), 7355–7363. (61) Krishnamurthy, S.; Haghpanah, R.; Rajendran, A.; Farooq, S., Simulation and Optimization of a Dual-Adsorbent, Two-Bed Vacuum Swing Adsorption Process for CO2 Capture from Wet Flue Gas. Industrial & Engineering Chemistry Research 2014, 53 (37), 14462-14473. (62) Gibson, J. A. A.; Mangano, E.; Shiko, E.; Greenaway, A. G.; Gromov, A. V.; Lozinska, M. M.; Friedrich, D.; Campbell, E. E. B.; Wright, P. A.; Brandani, S., Adsorption Materials and Processes for Carbon Capture from Gas-Fired Power Plants: AMPGas. Industrial & Engineering Chemistry Research 2016, 55 (13). (63) Ho, M. T.; Allinson, G. W.; Wiley, D. E., Reducing the Cost of CO2 Capture from Flue Gases Using Pressure Swing Adsorption. Industrial & Engineering Chemistry Research 2008, 47 (14), 1562-1568. (64) Leperi, K.; Snurr, R. Q.; You, F., Optimization of Two-Stage Pressure/Vacuum Swing Adsorption with Variable Dehydration Level for Post-Combustion Carbon Capture. Ind. Eng. Chem. Res., 2016, 55 (12), pp 2015, 55 (12), 3338–3350. (65) Croiset E, T. K., NOx and SO2 emissions from O2/CO2 recycle coal combustion. Fuel 2001, 80, 2117-2121. (66) MacDowell, N.; Florin, N.; Buchard, A.; Hallett, J.; Galindo, A.; Jackson, G.; Adjiman, C. S.; Williams, C. K.; Shah, N.; Fennell, P., An overview of CO2 capture technologies. Energ Environ Sci 2010, 3 (11), 1645-1669. (67) Scheffknecht, G.; Al-Makhadmeh, L.; Schnell, U.; Maier, J., Oxy-fuel coal combustion-A review of the current state-of-the-art. Int J Greenh Gas Con 2011, 5, S16-S35. (68) Yan J, F. R., Jacoby J, Anheden M, Giering R, Schmidt T, et al., Flue-gas cleaning processes for CO2capture from oxyfuel combustion - experience of FGD and FGC at 30 MWth oxyfuel combustion plant. mendeley 2009. (69) Gohlke, O.; Busch, M., Reduction of combustion by-products in WTE plants: O2 enrichment of underfire air in the MARTIN SYNCOM process. Chemosphere 2001, 42 (5–7), 545-550. (70) Rubin, E. S.; Rao, A. B.; Berkenpas, M. B., Development and Application of Optimal Design Capability for Coal Gasification Systems. Carbon 2007. (71) Nie, Z. Life cycle modeling of carbon dioxide capture and geological storage in energy production. 2009. (72) Koiwanit, J.; Manuilova, A.; Chan, C.; Wilson, M.; Tontiwachwuthikul, P., A life cycle assessment study of a hypothetical Canadian oxy-fuel combustion carbon dioxide capture process. Int J Greenh Gas Con 2014, 28 (2), 257–274.



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

Page 50 of 53

(73) Gebreslassie, B. H.; Waymire, R.; You, F., Sustainable Design and Synthesis of Algae-Based Biorefinery for Simultaneous Hydrocarbon Biofuel Production and Carbon Sequestration. AIChE Journal 2013, 59 (5), 1599-1621. (74) Gong, J.; You, F., Global Optimization for Sustainable Design and Synthesis of Algae Processing Network for CO2 Mitigation and Biofuel Production Using Life Cycle Optimization. AIChE Journal 2014, 60 (9), 3195-3210. (75) Gong, J.; You, F., Value-Added Chemicals from Microalgae: Greener, More Economical, or Both? ACS Sustainable Chemistry & Engineering 2015, 3 (1), 82-96. (76) Yue, D.; Gong, J.; You, F., Synergies between Geological Sequestration and Microalgae Biofixation for Greenhouse Gas Abatement: Life Cycle Design of Carbon Capture, Utilization, and Storage Supply Chains. ACS Sustainable Chemistry & Engineering 2015, 3 (5), 841-861. (77) Gong, J.; You, F., Optimal Design and Synthesis of Algal Biorefinery Processes for Biological Carbon Sequestration and Utilization with Zero Direct Greenhouse Gas Emissions: MINLP Model and Global Optimization Algorithm. Industrial & Engineering Chemistry Research 2014, 53 (4), 1563-1579. (78) Gong, J.; You, F., Consequential Life Cycle Optimization: General Conceptual Framework and Application to Algal Renewable Diesel Production. ACS Sustainable Chemistry & Engineering 2017, 5 (7), 5887-5911. (79) Hoornweg, D.; Bhada-Tata, P. What A Waste A Global Review of Solid Waste Management; https://siteresources.worldbank.org/INTURBANDEVELOPMENT/Resources/3363871334852610766/What_a_Waste2012_Final.pdf, 2012; pp 8-12. (80) Hasib-ur-Rahman, M.; Siaj, M.; Larachi, F., Ionic liquids for CO 2 capture—development and progress. Chemical Engineering and Processing: Process Intensification 2010, 49 (4), 313322. (81) Chung, Y. G.; Gomez-Gualdron, D. A.; Li, P.; Leperi, K. T.; Deria, P.; Zhang, H.; Vermeulen, N. A.; Stoddart, J. F.; You, F.; Hupp, J. T.; Farha, O. K.; Snurr, R. Q., In silico discovery of metalorganic frameworks for precombustion CO2 capture using a genetic algorithm. Science Advances 2016, 2 (10). (82) Ebner, A. D.; Ritter, J. A., State-of-the-art Adsorption and Membrane Separation Processes for Carbon Dioxide Production from Carbon Dioxide Emitting Industries. Separation Science and Technology 2009, 44 (6), 1273-1421. (83) Ofori-Boateng, C.; Lee, K. T.; Mensah, M., The prospects of electricity generation from municipal solid waste (MSW) in Ghana: A better waste management option. Fuel Process Technol 2013, 110 (110), 94-102. (84) ISO, Environmental Management – Life Cycle Assessment –Principles and Framework. International Standards Organisation, Geneva. In ISO 14040, 2006. (85) Cherubini, F.; Bargigli, S.; Ulgiati, S., Life cycle assessment (LCA) of waste management strategies: Landfilling, sorting plant and incineration. Energy 2009, 34 (12), 2116-2123. (86) Consonni, S.; Giugliano, M.; Grosso, M., Alternative strategies for energy recovery from municipal solid waste Part B: Emission and cost estimates. Waste Manage 2005, 25 (2), 137-48. (87) Li, W. j.; Sun, N.; Zhang, L. G., Life Cycle Assessment of Domestic Waste Treatment in Dalian. Environmental Sanitation Engineering 2009, 17 (6), 55-57(in Chinese). (88) Arena, U.; Ardolino, F.; Di, G. F., A life cycle assessment of environmental performances of two combustion- and gasification-based waste-to-energy technologies. Waste Manage 2015, 41, 60-74.


Page 51 of 53

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


(89) Amann, J. M.; Kanniche, M.; Bouallou, C., Natural gas combined cycle power plant modified into an O(2)/CO(2) cycle for CO(2) capture. Energy Conversion and Management 2009, 50 (3), 510-521. (90) CL, M., Carbon dioxide recovery: large scale design trends. J Can Pet Technol 1998, 37 (7), 42-47. (91) Xiong, J., TECHNO-ECONOMIC EVALUATION OF O2/CO2 燫 ECYCLE COMBUSTION SYSTEM AND MEA SCRUBBING SYSTEM BASED ON THERMOECONOMICS. Journal of Engineering Thermophysics 2008, 29 (10), 1625-1629. (92) Zhao, Y.; Xing, W.; Lu, W.; Zhang, X.; Christensen, T. H., Environmental impact assessment of the incineration of municipal solid waste with auxiliary coal in China. Waste Manage 2012, 32 (10), 1989-1998. (93) Dong, C. Q.; Yang, Y. P.; Zhang, J. J.; Lu, X. F., Gaseous emissions from the combustion of a waste mixture containing a high concentration of N(2)O. Waste Management 2009, 29 (1), 272276. (94) ENGINEERING, C. WWW.CHEMENGONLINE.COM. (95) R, S. D., CO2 mitigation economics for existing coal-fired power plants. In In: First National Conference on Carbon Sequestration, Washington DC, 2001. (96) Valero A., L. M. A., Serra L., et al., CGAM problem: Definition and conventional solution. Energy Policy 1994, 19, 279–286. (97) Silveira, J. L.; Tuna, C. E., Thermoeconomic analysis method for optimization of combined heat and power systems. Part I. Prog Energ Combust 2003, 29 (6), 479-485. (98) firm, H. Z. Y. a. Report of investigation, audit and accounting on treatment cost of domestic waste. (99) Liang, Z. Y.; Ma, X. Q.; Lin, H.; Tang, Y. T., The energy consumption and environmental impacts of SCR technology in China. Applied Energy 2011, 88 (4), 1120-1129. (100) Alibaba http://www.alibaba.com/product-detail/molecularsieve-zeolite-13xprice_60196376745.html (accessed May 29, 2015). (101) Zhao, W.; van der Voet, E.; Zhang, Y. F.; Huppes, G., Life cycle assessment of municipal solid waste management with regard to greenhouse gas emissions: Case study of Tianjin, China. Sci Total Environ 2009, 407 (5), 1517-1526. (102) Committee, N. D. a. I. Notice on propagating of electrovalency policy of MSW incineration power plant. (103) Hwang, C. L. Y., K, Multiple Attribute Decision Making: Methods and Applications. 1981. (104) Zavadskas, E. K.; Mardani, A.; Turskis, Z.; Jusoh, A.; Nor, K. M., Development of TOPSIS method to solve complicated decision-making problems: An overview on developments from 2000 to 2015. International Journal of Information Technology & Decision Making 2016, 15 (03), 645682. (105) Ramos, M. A.; Boix, M.; Montastruc, L.; Domenech, S., Multiobjective Optimization Using Goal Programming for Industrial Water Network Design. Industrial & Engineering Chemistry Research 2014, 53 (45), 17722–17735. (106) Yue, Z., A method for group decision-making based on determining weights of decision makers using TOPSIS. Applied Mathematical Modelling 2011, 35 (4), 1926-1936. (107) Wang;, Z.; Rangaiah, G. P., Application and Analysis of Methods for Selecting an Optimal Solution from the Pareto-Optimal Front obtained by Multiobjective Optimization. Ind. Eng. Chem. Res. 2017, 56 (2), 560-574.



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

Page 52 of 53

(108) Evren Can Özcan, S. Ü., Tamer Eren, A combined goal programming–AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants. Renewable and Sustainable Energy Reviews 2017, 78, 1410–1423. (109) Tansel, Y., An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies. Pergamon Press, Inc.: 2012; p 245-256. (110) TL, S., The analytic hierarchy process: planning, priority setting. resource allocation. New York, NY: McGraw-Hill; 437 1980. (111) Soltani, A.; Hewage, K.; Reza, B.; Sadiq, R., Multiple stakeholders in multi-criteria decision-making in the context of municipal solid waste management: a review. Waste Manage 2015, 35, 318-328. (112) Souza, R. G. D.; Clímaco, J. C. N.; Sant’Anna, A. P.; Rocha, T. B.; Quelhas, O. L. G., Sustainability assessment and prioritisation of e-waste management options in Brazil. Waste Manage 2016, 57, 46-56. (113) Tavana, M.; Hatami-Marbini, A., A group AHP-TOPSIS framework for human spaceflight mission planning at NASA. Expert Systems with Applications 2011, 38 (11), 13588-13603. (114) Chen, H.; Wen, Y.; Waters, M. D.; Shonnard, D. R., Design guidance for chemical processes using environmental and economic assessments. Industrial & engineering chemistry research 2002, 41 (18), 4503-4513. (115) Chen, H.; Shonnard, D. R., Systematic framework for environmentally conscious chemical process design: early and detailed design stages. Industrial & engineering chemistry research 2004, 43 (2), 535-552. (116) Saaty, T. L., Time dependent decision-making; dynamic priorities in the AHP/ANP: Generalizing from points to functions and from real to complex variables. Mathematical and Computer Modelling 2007, 46 (7-8), 860-891. (117) Mcilveen-Wright, D. R.; Huang, Y.; Rezvani, S.; Mondol, J. D.; Redpath, D.; Anderson, M.; Hewitt, N. J.; Williams, B. C., A Techno-economic assessment of the reduction of carbon dioxide emissions through the use of biomass co-combustion ☆. Fuel 2011, 90 (1), 11-18. (118) Alqayim, K.; Nimmo, W.; Pourkashanian, M., Comparative techno-economic assessment of biomass and coal with CCS technologies in a pulverized combustion power plant in the United Kingdom. Int J Greenh Gas Con 2015, 43 (1), 82-92. (119) Catalanotti, E.; Hughes, K. J.; Porter, R. T. J.; Price, J.; Pourkashanian, M., Evaluation of performance and cost of combustion ‐ based power plants with CO2 capture in the United Kingdom. Environmental Progress & Sustainable Energy 2015, 33 (4), 1425-1431. (120) Achillas, C.; Vlachokostas, C.; Moussiopoulos, N.; Banias, G.; Kafetzopoulos, G.; Karagiannidis, A., Social acceptance for the development of a waste-to-energy plant in an urban area. Resources Conservation & Recycling 2011, 55 (9–10), 857-863.


Page 53 of 53

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TOC Graphic:

Multi-criteria Environmental and Economic Analysis of Municipal Solid Waste Incineration Power Plant with Carbon Capture and Separation from the Life Cycle Perspective is presented.


Multicriteria Environmental and Economic Analysis of Municipal

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