Renewable-driven Chemical Processes via Power-to-Gas Concepts

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Engineering

Renewable-driven Chemical Processes via

Power-to-Gas Concepts

Hanfei Zhang

Supervisor:

Prof. Umberto Desideri

Co-supervisor: Prof.

François Maréchal

UNIPI

2020

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To my wife Caixia

“A person’s life may burn or rot, I can not rot, I would like to burn up”

Nikolai Ostrovsky

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Acknowledgements

At the end of my PhD journey, I wish to take this opportunity to thank all the people who have helped and supported me during my PhD studies period.

First and foremost, I would like to express my sincere gratitude to my supervisor Prof. Umberto Desideri for giving me the opportunity to pursue my PhD studies under his su-pervision. Umberto, thank you for your encouragement and endless support during my studies. In the past three years, you were patient and tireless to make valuable discussions with me, offered constructive suggestions, and read my manuscripts.

My heartfelt appreciation goes to co-supervisor Prof. François Maréchal at EPFL for host-ing me, patienthost-ing guidance, and sharhost-ing methodology to perform this research. François, after every discussion I can get a lot of inspiration from you. Also, your course made me more interested in the study of energy systems.

I would like to take this opportunity to thank Dr. Ligang Wang at EPFL for his great help in the use of the energy integration platform, the development of the process model and the writing of the paper.

I would like to thank secretary Sylvie and IT Cyrille at EPFL, they always tirelessly give the timeliest help. I would also like to thank Hür, Raluca, Luise, Julia, Stefano, Alberto, Francesco, Francesca, Ivan, Nils, Alessio, Gauthier, Federico, Manuele, Theodoros and Maziar. I would not have enjoyed Sion without you.

Thanks to all of my colleagues in UNIPI: Prof. Antonelli, Prof. Ferrari, Prof. Frigo, Aldo, Andrea, Marco, Gianluca, Guido, Eleonora, Angelica, Gabriele, David, Martin, Arseny, Mikhail, for all your kind help and pleasant discussions. I will certainly remember all of the happy moments with you.

I also would like to thank Prof. Zongming Zheng, Prof. Xianbin Xiao, Prof. Jun Yan, and Mr. Ruizhen Zhang for their support for my studies aboard.

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I must also thank Mr. Binghao Xu, Mr. Fanqian Kong and Mr. Wenkai Xuan, they give me many solid supports about chemical process design.

Many thanks go to Zhuyong, Huahui, Bianjin, Shisong, Xiaolei, Lijun, Wangliang, and Yongsheng, for granting me endless assistance and happiness.

Special thanks to Prof. Daran Li the former Chief Engineer in State Nuclear Electric Power Planning Design & Research Institute (SNPDRI). Thank you for your sincere encourage-ment, support and help all the time. You guided me to become a qualified engineer. With your encouragement, I took the most important step in my life - going abord for further study. I would also like to thank my former colleagues in SNPDRI, Ms. Yuhua, Ms. Huna and Mr. Zhangxian for their support and help.

I am sincerely indebted to my family for all the love, selfless dedication and constant sup-port they are always give me. My deepest thanks are for my lovely wife Caixia, I could not achieve all these without you, I love you.

This research work was funded under the Chinese Scholarship Council and PRA2019 of the University of Pisa.

Hanfei ZHANG Pisa, October 2020

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Abstract

Chemical products play a significant role in the energy system. To reduce fossil-fuel con-sumption and mitigate the impact of climate change from the production of chemicals us-ing natural gas and coal, the investigation of green chemical production processes become essential. Biomass is a promising renewable carbon resource substitute for fossil fuels to produce chemical products. However, state-of-the-art biomass-to-chemical conversion requires an increased hydrogen concentration in the syngas derived from biomass gasifica-tion, which is achieved by water-gas-shift reaction and CO2 removal, resulting in using less

than half of the biomass carbon with the remaining part emitted as CO2. To overcome this

problem, biomass-to-chemical technologies integrated with renewable power-to-hydrogen systems come into being as an alternative concept.

In this thesis, renewable-driven chemical processes using solid-oxide electrolyzer are im-plemented and compared with the state-of-the-art ones for various products (i.e., methane, methanol, dimethyl ether, jet fuel, ammonia, and urea) through innovative conceptual pro-cess design, thermochemical modelling, energy integration, techno-economic evaluation, and multi-objective optimization. Experimental data from the literature and industrial data are adopted to develop and verify thermochemical models, such as entrained-flow gasifica-tion, syngas purification processes, chemical synthesis processes, and others. For the eco-nomic models, capital expenditure and operating expenditure are examined, and the main economic assumptions are proposed on the basis of literature and industrial data. Although the economic information was taken from a variety of sources, it can predict the invest-ment feasibility of the above processes, and provide a reference for policymakers from the industry or government.

Compared with the state-of-the-art chemical process, the solid-oxide electrolyzer-based chemical process achieves higher overall system efficiency because (1)

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biomass-to-chemical processes can fully convert carbon, (2) solid-oxide electrolyzer for steam- or co-electrolysis is highly efficient, (3) high-temperature solid-oxide electrolyzer has the best heat integration opportunity with entrained-flow gasification and chemical synthesis to enhance system-level heat utilization. For instance, solid-oxide electrolyzer based biomass-to-synthetic natural gas process increases the overall system efficiency by more than 15 percentage points compared with state-of-the-art biomass-to-synthetic natural gas. The solid-oxide electrolyzer based power-to-ammonia process enhance the overall system effi-ciency by over 30 percentage points in comparison with biomass-to-ammonia processes. The economic evaluation reveals that solid-oxide electrolyzer based chemical process is hardly economically viable at present. The stack price and lifetime of solid-oxide electro-lyzers are highly sensitive to the investment feasibility of the project, and they reflect the significant impact of commercialization of solid-oxide electrolyzers on economic feasibil-ity. The solid-oxide electrolyzer based processes require a large amount of renewable pow-er to drive the solid-oxide electrolyzpow-er; thus, both the price and available annual hours of renewable electricity have a significant impact on their economic feasibility. A lower price of renewable electricity significantly reduces the levelized cost of products. Biomass-to-chemical with steam electrolysis is hugely affected by available annual hours of renewable electricity. When renewable electricity is not available, the system might need to be shut down due to a lack of large-scale storage of hydrogen or electricity. Nevertheless, the con-cept of state-of-the-art biomass-to-chemical integrated with co-electrolysis allows for addi-tional operaaddi-tional flexibility without renewable electricity, resulting in high annual produc-tion. It is more economically convenient than that with steam electrolysis.

Keywords: biomass-to-chemical, power-to-gas, solid-oxide electrolyzer, entrained flow

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List of Figures

Fig. 0.1. Primary oil (left) and natural gas (right) demand in 2017 by sector. ... 1

Fig. 0.2. Global final energy demand and direct CO2 emissions by sector in 2017. ... 2

Fig. 0.3. Global CO2 emissions from existing energy infrastructure by sub-sector, 2019-70. ... 3

Fig. 0.4. Global direct CO2 emissions in industry by sub-sector and region in the Sustainable Development Scenario, 2019-70. ... 4

Fig. 0.5. The energy density of volume and weight of various chemicals. ... 7

Fig. 0.6. Bioenergy requirements to satisfy all primary chemical demand. ... 9

Fig. 0.7. Schematic diagrams of three gasification processes. ... 11

Fig. 0.8. Schematic of the state-of-the-art biomass-to-chemicals process. ... 13

Fig. 0.9. Schematic of the biomass-to-chemicals process integrated with water electrolysis. ... 14

Fig. 0.10. Conceptual schematic of three electrolysis technologies. ... 14

Fig. 0.11. Comparison of three different electrolysis technologies at stack level for today (solid eclipse) and future (dashed eclipse) permission. ... 15

Fig. 1.1. The employed multi-objective optimization framework... 20

Fig. 1.2. The hot-cold composite curve of heat integration： (a) excess heat recovery is not considered；(b) excess heat is recovered by steam turbine network. ... 23

Fig. 1.3. The composite curve of heat integration： (a)Grand composite curve；(b) Integrated composite curve for steam turbine network. ... 23

Fig. 1.4. Overall energetic schematic of the inputs and outputs of the process... 23

Fig. 2.1. Schematic of the SoA BtM plant with EFG. ... 31

Fig. 2.2. Schematic of the integration concept of full carbon conversion with SOE.. ... 36

Fig. 2.3. Schematic of the integration concept of zero power export with the power produced internally to drive the SOE. ... 37

Fig. 2.4. Comparison between experimental measurements [69] and simulation results of the syngas composition (operation temperature 1000 - 1400 ℃, fixed O/B weight ratio 0.4). ... 38

Fig. 2.5. Trade-off between the methanol production cost rate and system energy efficiency. ... 45

Fig. 2.6. Carnot grand composite curves of SoA-OP. ... 46

Fig. 2.7. Breakdown of the capital investment cost of the SoA-OP. ... 47

Fig. 2.8. Breakdown of cost contributions with respect to the system energy efficiency. ... 48

Fig. 2.9. Effect of SOE operating variables to the system efficiency. ... 49

Fig. 2.10. The system power consumption state changes with the system energy efficiency. ... 49

Fig. 2.11. The composite curve of system-level heat integration and system performance of FCC. ... 50

Fig. 2.12. Investment distribution of FCC-MCP and SoA-OP. ... 51

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Fig. 2.14. The composite curve of system-level heat integration and system performance for ZPE-MEP. ... 53

Fig. 2.15. Investment distribution of ZPE-MCP and FCC-MCP. ... 54

Fig. 2.16. Operational cost and revenues distribution of ZPE-MCP and FCC-MCP. ... 55

Fig. 2.17. Sensitivity analysis of the main variables in FCC case... 57

Fig. 3.1. Schematic of the state-of-the-art biomass-to-synthetic natural gas system with EFG. ... 63

Fig. 3.2. Schematic of the biomass-to-synthetic natural gas system with steam electrolysis.. ... 65

Fig. 3.3. Schematic of the biomass-to-synthetic natural gas system with co-electrolysis. ... 66

Fig. 3.4. Schematic of the methanol synthesis process. ... 67

Fig. 3.5. Schematic of the DME synthesis process via methanol. ... 68

Fig. 3.6. Schematic of JF synthesis and purification via the FTS process. ... 70

Fig. 3.7. The energy efficiency (LHV) of biomass-to-fuel systems with additional information ... 74

Fig. 3.8. Integrated composite curves of BtSNG with the yellow standing for the steam cycle and the black for the remaining process: (a) SoA-SNG, (b) BSE-SNG, (c) BCE-SNG. ... 76

Fig. 3.9. Integrated composite curves of BtMeOH with the yellow standing for the steam cycle and the black for the remaining process: (a) SoA-MeOH, (b) BSE-MeOH, (c) BCE-MeOH. ... 77

Fig. 3.10. Integrated composite curves of BtDME with the yellow standing for the steam cycle and the black for the remaining process: (a) SoA-DME, (b) BSE-DME, (c) BCE-DME. ... 79

Fig. 3.11. Integrated composite curves of BtJF with the yellow standing for the steam cycle and the black for the remaining process: (a) SoA-JF, (b) BSE-JF, (c) BCE-JF. ... 80

Fig. 3.12. The variation of economic indicators with plant capacity. ... 82

Fig. 3.13. Minimum product selling price varied with plant capacity. ... 83

Fig. 3.14. Impacts of the electricity purchase price (0 - 73 $/MWh) and AOHs (1800 - 7200 h) on the LCOP of SNG. ... 84

Fig. 3.15. Impacts of electricity purchase price (0-73 $/MWh) and AOHs (1800-7200 h) on the MPSP of SNG.85 Fig. 3.16. Annual production with different AAHs. ... 86

Fig. 4.1. Schematic of the state-of-the-art methane-to-ammonia plant. ... 91

Fig. 4.2. Schematic of the biomass-to-ammonia plant with EFG. ... 92

Fig. 4.3. Schematic of the SOE-integrated power-to-ammonia. ... 93

Fig. 4.4. Trade-offs between the levelized cost of ammonia and system energy efficiency. ... 98

Fig. 4.5. The integrated composite curves of the MCPs: (a) MtA, (b) BtA, (c) PtA. ... 101

Fig. 4.6. Investment distribution of MCP for all three cases. ... 103

Fig. 4.7. The distribution of operating costs (positive value) and revenues (negative value) of MCP for all the three cases. ... 103

Fig. 5.1. Schematic of the SoA MtU plant. ... 111

Fig. 5.2. Schematic of the BtU plant with EFG. ... 112

Fig. 5.3. Schematic of the BPtU process. ... 113

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Fig. 5.5. The overall energy and material flows of (a) the MtU, (b) the BtU, and (c) the BPtU. ... 117

Fig. 5.6. The integrated composite curves of the urea processes: (a) MtU, (b) BtU, and (c) BPtU. ... 119

Fig. 5.7. Cost breakdown of all three cases with 50 MW urea production: (a) investment costs, (b) operating costs and revenues. ... 121

Fig. 5.8. The variation of economic indicators with plant capacity. ... 122

Fig. 5.9. Minimum product selling price varied with plant capacity ... 123

Fig. 5.10. The variation of economic indicators of the BPtU case with AAHs and electricity price. ... 124

Fig. 5.11. Impacts of the electricity purchase price (0 - 73 $/MWh) and AAHs (1800 - 7200 h) on the MUSP.124 Fig.A-1. Block diagram of the SoA BtM plant. ... 136

Fig.A-2. Block diagram of the integration concept of full carbon conversion with SOE. ... 136

Fig.A-3. Block diagram of the integration concept of zero power export with the power produced internally to drive the SOE. ... 136

Fig. A-4. Grand composite curve of SoA-OP. ... 137

Fig. A-5. Integrated composite curve for steam turbine utility of SoA-OP. ... 137

Fig.A-6. Grand composite curves of FCC case at MEP and MCP. ... 138

Fig.A-7. The composite curve of system-level heat integration: (a) FCC-MEP; (b) FCC-MCP. ... 138

Fig.A-8. Integrated composite curve for steam turbine utility: (a) FCC-MEP; (b) FCC-MCP. ... 139

Fig.A-9. Different cost contributions with respect to the system energy efficiency. ... 139

Fig.A-10. Effect of SOE operating variables to the system efficiency form ZPE case. ... 140

Fig.A-11. Grand composite curve of ZEP and FCC cases at MEP. ... 140

Fig.A-12. Integrated composite curve for SOE of ZPE case at MEP. ... 141

Fig.A-13. Integrated composite curve for steam turbine utility of ZPE case at MEP. ... 141

Fig. A-14. Sensitivity analysis of the main variables in FCC case. ... 142

Fig. B-1. The integrated composite curves for SOE: (a) BSE-SNG, (b) BCE-SNG. ... 147

Fig. B-2. The integrated composite curves for SOE: (c) BSE-MeOH, (d) BCE-MeOH. ... 148

Fig. B-3. The integrated composite curves for SOE: (e) BSE-DME, (f) BCE-DME. ... 149

Fig. B-4. The integrated composite curves for SOE: (g) BSE-JF and (h) BCE-JF. ... 150

Fig. B-5. Relative electrolysis power varied with plant capacity. ... 150

Fig. C-1. Effect of SOE operating variables to the system efficiency for PtA case. ... 154

Fig. C-2. The grand composite curves of the MCPs: (a) MtA, (b) BtA, (c) PtA. ... 155

Fig. C-3. The integrated composite curve for SOE of PtA case at MCP. ... 156

Fig. D-1. The grand composite curves of the urea processes: (a) MtU, (b) BtU, and (c) BPtU. ... 159

Fig. D-2. The integrated composite curve for SOE in BPtU case. ... 160

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List of Tables

Table 0.1. The main characteristics and performance of gasifiers. ... 12

Table 2.1. Technological features of the BtM plants to be investigated. ... 35

Table 2.2. Ultimate and proximate analysis. ... 38

Table 2.3. Operating parameters and performance indicators of the synthesis reactor. ... 39

Table 2.4. Comparison of the simulated and factory Results. ... 39

Table 2.5. Summary of the modelling of the remaining components. ... 39

Table 2.6. Parameters to estimate the investment of major equipment. ... 41

Table 2.7. Economic evaluation assumptions. ... 42

Table 2.8. The decision variables for MOO... 43

Table 2.9. Fixed technological specifications. ... 43

Table 2.10. Summary of results for different optimal solutions marked in Fig. 2.5. ... 45

Table 3.1. Product distribution from LTFT jet fuel refinery. ... 71

Table 3.2. Practical stack operating point with the stack inlet temperature of 750 ℃ and 1 bar. ... 71

Table 3.3. Economic evaluation assumptions. ... 72

Table 3.4. Summary of results for the proposed biomass-to-fuel processes. ... 74

Table 4.1. Comparison of the simulation and industry results. ... 94

Table 4.2. Summary of the component models. ... 95

Table 4.3. Assumptions for evaluating the operating costs. ... 96

Table 4.4. Decision variables for different concepts. ... 97

Table 4.5. Summary of results for different optimal solutions marked in Fig. 4.4. ... 99

Table 5.1. Comparison of the simulation and industrial practice. ... 114

Table 5.2. Practical stack operating point at 750 ℃ and 2.2 bar... 115

Table. A-1. Decision variables values for the optimized configuration. ... 135

Table. B-1. Key parameters of the optimal steam turbine network... 151

Table. C-1. Decision variables values for the optimized configuration. ... 154

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Nomenclature

Abbreviations

AAHs annual available hours of renewable electricity AE alkaline electrolyzer

AGR acid gas removal AOH annual operating hours ASU air separation unit ATR autothermal reformer

BCE-FUEL biomass-to-fuel with co-electrolysis BSE-FUEL biomass-to-fuel with steam electrolysis BtA biomass-to-ammonia

BtC biomass-to-chemical

BtDME biomass-to-dimethyl ether BtF biomass-to-fuel

BtJF biomass-to-jet fuel BtL biomass-to-liquid

BtMeOH or BtM biomass-to-methanol BtSNG biomass-to-synthetic natural gas BPtU biomass- and power-to-urea BtU biomass-to-urea

CAPEX capital expenditure CE co-electrolysis

COP coefficient of performance DME dimethyl ether

EFG entrained flow gasifier EES electrical energy storage ESC electricity storage capacity

ETSAP energy technology systems analysis program FCC full carbon conversion

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HTFT high-temperature fischer-tropsch HTSR high-temperature water-gas shift reactor HEN heat exchanger network

HHV higher heating value HVCs high-value chemicals ICE internal combustion engines IEA international energy agency

IRENA international renewable energy agency JF jet fuel

LCI lifecycle indicator LHV lower heating value

LTFT low-temperature fischer-tropsch LTSR low-temperature water-gas shift reactor MCP minimum production cost point

MEA monoethanolamine MeOH methanol

MEP maximum efficiency point MtA methane-to-ammonia MtG methanol-to-gasoline MtU methane-to-urea

MILP mixed integer linear programming MSS methanol synthesis system

MOO multi-objective optimizer OP optimal point

OPEX operational expenditure PEC purchased equipment cost

PEME polymer electrolyte membrane electrolyzer PR primary reformer

PtA power-to-ammonia PtH power-to-hydrogen

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SE steam electrolysis

SMR steam methane reforming SNG synthetic natural gas SoA state-of-the-art

SoA-FUEL State-of-the-art biomass-to-fuel SOE solid-oxide electrolyzer

SR secondary reformer STN steam turbine network WGSR water-gas-shift reaction $ united states dollar € euro Greek Symbols 𝜂𝜂 energy efficiency ε exergy efficiency 𝜏𝜏 payback time Mathematical Symbols 𝐶𝐶𝑑𝑑𝑑𝑑𝑑𝑑 depreciation cost 𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖 investment cost 𝐶𝐶𝑜𝑜𝑑𝑑𝑜𝑜 operational cost 𝐶𝐶𝑟𝑟𝑑𝑑𝑖𝑖𝑏𝑏𝑏𝑏𝑑𝑑 byproduct revenue 𝑖𝑖 annual interest rate

LCOP levelized cost of the product

𝑀𝑀̇product the mass flow of obtained product 𝑀𝑀̇Biomass the mass flow of biomass feed

MPSP minimum product selling price

∆𝐸𝐸̇+_{net electric power input} ∆𝐸𝐸̇−_{net electric power output}

∆𝐻𝐻298K standard enthalpy of formation, kJ mol-1 𝑇𝑇 temperature, K

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Introduction

In modern society, chemicals play an essential role in the energy system. According to the International Energy Agency (IEA) report [1], the Petro-chemical sector is the largest in-dustrial energy consumer, ahead of iron and steel, and cement, accounting for about 10% of total final energy consumption and almost 30% of industrial final energy consumption. It is also the most significant industrial consumer of both oil and gas, accounting for 14% and 8% of total primary demand for each fuel, respectively [1]. The primary oil and natural gas demand in 2017 by sector are shown in Fig. 0.1.

Fig. 0.1. Primary oil (left) and natural gas (right) demand in 2017 by sector (adapted from [1]).

The chemical sector, while playing such a pivotal role, is also putting enormous pressure on the environment. It is contributing to approximately 7% of global greenhouse gas emis-sions (GHG), and 5.5% when only counting CO2 emissions [2]. Today, approximately 18%

or 1.5 GtCO2 of industrial CO2 emissions are from the chemical sector (Fig. 0.2), including

two key sources [1]: (1) energy-related emissions (1.3 GtCO2 or 85%) are released, as in

any other industrial or transportation sector, when fuel is combusted to generate heat or steam; (2) process CO2 emissions (0.2 GtCO2 or 15%) reflect the difference in carbon

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feedstock (75% carbon) are required to make a tonne of ammonia (0% carbon), the process CO2 emissions per tonne of ammonia would be approximately 1.1 tonnes of carbon dioxide.

According to the IEA report [1], primary chemicals account for about 60% of the total CO2

emissions in the chemical sector. Ammonia is the single largest source, contributing 49% of the primary chemicals’ CO2 emissions, followed by high-value chemicals (HVCs) (27%)

and methanol (24%). Moreover, it is estimated that the demand for primary chemicals will increase by around 30% by 2030 and almost 60% by 2050 compared with the current year [1].

Fig. 0.2. Global final energy demand and direct CO2 emissions by sector in 2017 (adapted from [1]).

Based on the Paris Agreement, strengthening the global response to the threat of climate change by keeping a global temperature rise this century well below 2 ℃ above pre-industrial levels will require the stabilization of greenhouse gas concentrations in the at-mosphere at approximately 450 ppm [3]. Global emissions must be reduced, but they will peak well before mid-century, and approach or reach net-zero in the latter half of this cen-tury [3]. According to the IEA report [4], existing energy infrastructure could result in nearly 750 GtCO2 (28 GtCO2 in the chemical sector) of additional emissions by sub-sector

from 2019 to 2070. This would deplete most of the remaining CO2 budget accommodated

to limiting the global temperature rise to “well below 2 ℃” [4], the detailed trends are shown in Fig. 0.3.

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Fig. 0.3. Global CO2 emissions from existing energy infrastructure by sub-sector, 2019-70 (adapted from [4]).

The Fig. 0.4 shows the global direct CO2 emissions from industries by sub-sector and

re-gion in the IEA’s Sustainable Development Scenario (Sustainable Development Scenario: a roadmap for meeting international climate and energy goals - brings the global energy system to net-zero emissions by 2070) from 2019 to 2070 [4]. CO2 emissions in the

chemi-cal sector, aiming to reach near-zero emissions, may only be eliminated worldwide after 2070 under the assumptions of the Sustainable Development Scenario, with residual emis-sions in 2070 to be offset by negative emisemis-sions in the power and other energy transfor-mation sectors [4]. The total chemical sector emissions should be 90% lower than those of 2019, from 1.4 GtCO2 in 2019 to 0.2 GtCO2 in 2070. In the Sustainable Development

Sce-nario, achieving the near-zero emissions goal requires that the feedstock in the chemical sector to be adjusted by increasing the penetration of renewable energies. Finally, those renewable synthetic chemicals can replace traditional fossil fuels to meet the global energy demand in, such as transportation, electricity, industries and so on.

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Fig. 0.4. Global direct CO2 emissions in industry by sub-sector and region in the Sustainable Development

Scenario, 2019-70 (adapted from [4]). ROW: rest of world. Includes direct energy-related and process emis-sions. Other industry includes less energy-intensive industries such as food and beverage, mining and textiles.

Energy-intensive industries, such as steel, chemicals and cement, are indispensable to the modern economy because they provide several key value chains for today’s social and economic development. The decarbonisation and modernisation of this sector is essential. To meet the commitments of the Paris agreement, governments should set out realistic roadmaps to promote green transformation of energy-intensive industries. The European Green Deal [5] is a response to the Paris agreement, the deal should make Europe become the first climate-neutral continent and a European Climate Law to enshrine the 2050 cli-mate-neutrality target into law [6]. Under the European green deal, the EU should in paral-lel ramp-up production and deployment of sustainable alternative transport fuels. By 2025, about 1 million public recharging and refueling stations will be needed for the 13 million zero- and low-emission vehicles expected on European roads [5]. Furthermore, the Euro-pean commission will consider legislative options to boost the production and uptake of sustainable alternative fuels for different transport modes. The commission will also re-view the Alternative Fuels Infrastructure Directive and the trans-European Transport Net-work (TEN-T) Regulation to accelerate the deployment of zero- and low-emission vehicles and vessels [5]. These policy measures will contribute significantly to the technological development of renewable synthetic chemicals or fuels and to the replacement of tradition-al transport fuels.

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Primary chemicals

Among the most studied chemicals we may definitely list methane, methanol, dimethyl ether, ammonia, urea, and fischer-tropsch fuels (i.e., jet fuel and gasoline). These chemi-cals play an important role in the chemical sector and are widely used as raw materials, fuels, and agricultural fertilizers.

Methane (CH4), the main component of natural gas (NG), is one of the major primary

energy sources in the world, accounting for about one-fifth of global energy supply [7]. Methane is widely used [8] (1) to generate electrical power by combined heat and power and fuel cell system, (2) to provide heat, hot water and fuel for cooking in building, (3) to power vehicles instead of diesel oil in the transportation sector, e.g., cars, ships, trucks, and trains, (4) to generate process steam for industrial applications, e.g., steel and paper pro-duction, (5) to synthesize other chemicals as raw material, e.g., methanol and ammonia. Natural gas is the fastest-growing fossil fuel. In 2017, a new high was reached for global demand of natural gas with 3,752 Billion cubic metres, a 49% increase compared to 2000 [9]. The power sector demand accounts for up to 40%, followed by the industry sector (23%), buildings (21%), transport sector (4%), and other sectors (12%).

Methanol (CH3OH) is one of the top chemicals produced in the world [10,11], which can

be (1) used as a fuel in internal combustion engines [12] or direct methanol fuel cell [13] to produce power, (2) reformed to produce hydrogen [10], (3) mixed with gasoline as an additive to provide fuel blends, e.g., the M85 (15% gasoline and 85% methanol) [10,12,14], or (4) used as a raw material to synthesize other fuels and chemicals [10,11,15,16,17], e.g., olefins, gasoline, dimethyl ether, methyl tert-butyl ether, acetic acid, HVCs, plastics, ply-wood, paints, explosives, and textiles. Methanex Corporation, the largest methanol produc-er in the world, reported a total methanol demand of 78 million tonnes (excluding metha-nol produced in integrated coal-to-olefins facilities) in 2017 [18], of which 45% is due to energy-related demand with an annual increase of 8% [18]. The annual global demand for methanol will reach 100 million tonnes by 2025 [18].

Dimethyl ether (CH3OCH3) is the simplest ether, also known as DME. Its physical

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an alternative to LPG. Due to the high cetane number (50-60), DME is used as a replace-ment for diesel engine fuel with no SOX and particulate matter emissions and lower

gen-eration of NOx [20]. Besides, DME has moderate vapor pressure and very low toxicity in the human body. DME is also widely used as a spray agent for aerosol propellants [20]. Moreover, DME is a hydrogen carrier that can be used as a hydrogen source of the fuel cell.

Ammonia (NH3) is one of the most important and widely produced inorganic chemicals in

the world, which can be used (1) to produce agricultural fertilizers like ammonium nitrate, ammonium phosphate, and urea [21], (2) as a capturing agent in acid gas removal process-es [22], (3) for large scale refrigeration and air-conditioning for buildings and industrial processes [23], (4) to manufacture explosive materials, fibers, plastics, polymers, papers, and acids [21], and (5) as a potential fuel for internal combustion engines (ICE) due to a high octane rate of 110-130 [24] and fuel cells (e.g., solid-oxide fuel cells) for power gen-eration with or without reforming [25,26]. Global ammonia production has been continu-ously growing in the last decades, reaching 137 million tonnes in 2012 [23]. By 2018, the global production of ammonia has registered at 140 million tonnes, with China accounting for 31.4%, followed by Russia (10%), US (8.9%), and India (7.8%) [27].

Urea ((NH2)2CO) is one of the top organic chemicals worldwide with its global capacity

growing at a rate of 3 - 4% [28] and reaching 226 million tonnes in 2023 [29]. More than 80% of the urea produced is used to fertilize crops [30], and the rest is used (1) in selective catalytic reduction systems to reduce NOx emissions in fossil-fuel power plants and diesel engines for the truck transport sector [31], (2) as a raw material for important chemicals like plastics and waterproof glue for marine plywood [32,33], (3) in petroleum refining to produce jet aviation fuel and de-wax lubricant oils [33], and (4) as food and animal feed additive [33]. The energy density of urea (11.89 MJ/m3_{) is higher than compressed or}

liq-uid hydrogen (4.93 MJ/m3_{at 700 bar and 8.94 MJ/m}3_{at liquid form). The non-flammable,}

non-toxic, easy-to-store and -transport features enable it as an ideal hydrogen carrier and energy source for fuel cells [34,35]. For example, alkaline membrane electrolyte-based fuel cells can directly convert urea to electricity [35]. Urea, as a fuel in solid oxide fuel cell (SOFC)-based power system, can achieve an overall efficiency of 55% at 800 ℃ and fuel utilization of 0.8 [31]. The direct urea SOFC integrated with the gas turbine power cycle

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can achieve an overall energy system up to 56.8% at 800℃ and fuel utilization of 0.85 [36].

Fischer-Tropsch synthesis (FTS) is a set of catalytic heterogeneous reaction that can be

used to produce hydrocarbon fuels (e.g., gasoline, diesel, and jet fuel), olefins, wax and alcohols from syngas (a mixture of CO and H2), which is commercially available from

natural gas and coal. Compared with hydrocarbons derived from fossil fuels, FTS hydro-carbon fuels are described as clean fuels due to the very low sulfur content [37].

Fig. 0.5. The energy density of volume and weight of various chemicals. The data of gasoline, diesel, and jet fuel is from [38,39,40], others are from Aspen Plus.

The chemicals described above can be used as substitutes for existing transportation and power fuels. The Fig. 0.5 shows the energy density of volume and weight of various chem-icals used as fuel. As a gaseous fuel, hydrogen at 700 bar has a fairly low energy density per volume, only 50% of that of methane at 250bar. Today, it is generally believed that 700 bar of hydrogen, used in fuel-cell cars, has an advantage over battery-powered cars due to hydrogen can be charged within minutes, but this does not seem to be a solution for exist-ing transportation and infrastructure, although it has been considered [41]. Therefore, the demand for high energy density fuel is expected to be huge in fields such as aviation, ship-ping, large vehicles, and so on. Today, it is imperative to develop green, high energy densi-ty fuels instead of conventional fossil fuels, and almost all high energy densidensi-ty fuels are

0 5 10 15 20 25 30 35 40 0 10 20 30 40 50 60 70 80 90 100 110 120 130 En er gy d en si ty , M J/ lit er (L H V) Specific energy, MJ/kg (LHV) H2@700 bar H2Liq. Methane @250 bar Ammonia Liq. Methanol Urea DME Gasoline

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carbon-based (except ammonia). Thus, green carbon sources are just as important as green hydrogen sources. If the feedstock for the synthesis of these chemicals wholly comes from renewable energy sources, as shown in Fig. 0.1, fossil fuel consumptions in chemical, transportation, and power sectors can all be avoided. It is well known that carbon consti-tutes the structure and properties of most primary chemicals as well as specific chemical products [1]. Amongst the alternative renewable energy sources, the only natural, renewa-ble carbon resource and large fraction of substitute for fossil fuels is biomass [42]. Hydro-gen is needed in the production of almost all chemical products, and renewable hydroHydro-gen sources can also be obtained from biomass. Another alternative is hydrogen generated via electrolysis (i.e., water-splitting), powered by renewable electricity. Therefore, biomass gasification and renewable electricity driven water electrolysis technologies should be widely introduced for producing primary chemicals without relying on fossil fuel feedstock.

Biomass gasification technologies and biomass-to-chemical

Biomass, as an alternative source, accounts for 14% of the global renewable energy [43,44]. It must however be noted that such a high share of biomass in the renewable ener-gy conversion is mainly due to low efficiency and residential use of biomass for heating and cooking in poor areas. The use of biomass as a renewable feedstock in the industry is far from that share except in some regions such as notably in northern Europe and parts of North America [44]. Therefore, it is important to increase the share of biomass utilization in industrial sectors where fossil oil and gas are still the main raw materials. Biomass can be used to generate heat, electricity, and chemicals in a carbon-neutral way [45,46,47]. The increased use of biomass allows for the potential of carbon-free production of second-ary energy. Although biomass as an alternative feedstock is not an option for all sectors, many of them do consider the use of bio-based feedstock as one of the key options to re-duce the GHG footprint of feedstock consumption [6].

In IEA’s Sustainable Development Scenario, total biogas and biomethane production worldwide grow from 30 million tonnes of oil equivalent (Mtoe) today to 335 Mtoe in 2040 and 390 Mtoe in 2070. Global average blending shares for biomethane into natural gas networks may reach 8% in 2040 and 16% in 2070 [4]. The liquid biofuels also require

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rapid and large-scale demonstration such as biodiesel and biojet fuel through gasification and Fischer-Tropsch processes, the total production capacity of which would grow at an average rate about 40% faster than in the Sustainable Development Scenario by 2050 [4]. The main challenge of using biomass as the exclusive feedstock for primary chemical pro-duction is the huge demand for biomass. According to the IEA report [1] (Fig. 0.6), the current bioenergy requirements to meet global primary chemical demand is about 1.5 times that of fossil fuels (take 2017 as an example), around 1450 Mtoe (60 EJ) of bioethanol and other types of biomass are required to fully meet the global demand for primary chemical production by 2030. This number is expected to rise to 1620 Mtoe (68 EJ) by 2050. The report predicts that this pathway would require more than half of the world's sustainable biomass for all primary chemical production.

Fig. 0.6. Bioenergy requirements to satisfy all primary chemical demand (adapted from [1]).

Biomass gasification technologies

Gasification is a key process for the thermochemical conversion of biomass to produce syngas or synthesis gas (a mixture of CO, H2, CO2, CH4), which can be used to synthesize

chemicals or biofuels [48]. Biomass gasification technologies mainly include moving-bed gasifier, fluidized-bed gasifier, or entrained-flow gasifier [49,50]. The three types of gasi-fiers are shown in Fig. 0.7. The moving-bed gasifier (~1000 or >1000 ℃, >20 bar [57]) usually results in a smaller hydrogen-to-monoxide ratio of syngas and high tar yield [51,52]. It is limited for scale-up due to the bridging of the biomass, non-uniform bed

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tem-peratures and high tar content [53,54,55]. The fluidized-bed gasifier (950 - 1100 ℃ for coal, 800 - 950 ℃ for biomass [51,53]) allows proper mixing of the gasifying agent [51], high load flexibility, good carbon conversion, and scale-up capability [11,55]; however, it may suffer from equipment abrasion caused by high gas velocity [56] and a certain amount of tar under moderately-high temperatures [51,57]. The entrained-flow gasifier (EFG, 1200 - 1600 ℃, 1 - 80 bar [58]), successfully implemented in 1950 [51] and now widely used in integrated-gasification combined cycles [ 59, 60] and coal-to-methanol/ammonia plants [61,62], employs pure oxygen as the gasification agent and enables remarkably high reac-tion rates and a high biomass-to-syngas conversion with low methane, CO2 and almost no

tar and gaseous hydrocarbons. The EFG achieves a high load throughput during short resi-dence, offering a great advantage for biomass gasification [48,57,63]. High-pressure condi-tions without the dilution of N2 lead to a compact design of downstream equipment and

reduce or even avoid power consumption of syngas compression for chemical synthesis. However, the main drawback is the high oxygen demand [57], which is usually satisfied by expensive air separation units (ASU) (up to 18% to the total plant investment) [64]. The main characteristics and performance of gasifiers are summarized in Table 0.1.

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Fig. 0.7. Schematic diagrams of three gasification processes (adapted from [57,65]).

The EFG is well-proven, commercially-available [66] for large-scale coal gasification, but is still under development for biomass utilization. Lab-scale biomass EFG has been inten-sively studied in [51,57,67,68, 69]. For large-scale applications [70], e.g., the EFGs in Freiburg (Germany) built by CHOREN (198 tonne/day, in 2007) and by Karlsruhe Institute of Technology (12 tonne /day, in 2011), and in Colorado (the USA, 2007) by Range Fuels Inc (125 tonne /day). In this thesis, the EFG gasification technology is employed.

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Table 0.1. The main characteristics and performance of gasifiers [48,51,52,53,54,55,56,57,58,63].

Gasifier type Moving-bed Fluidized-bed Entrained flow

Operating temperature

(℃) ~1000 or >1000 800 - 1100 1200 - 1600

Operating pressure (bar) >20 1 – 35 1 - 85

Syngas outlet

tempera-ture (℃) 400 – 650 900 -1050 1200 - 1600

Cold gas efficiency (%) 20 – 60 <70 30 - 90

Unit capacity (MW) 10 – 350 100 - 700 Up to 700 MW

Advantage Large fuel particles to

ensure good bed permea-bility and efficient heat and mass transfer, less complex fuel preparation

High load flexibility; scale-up capability; high tolerability to diverse biomass feedstock types

Low methane and CO2

content, almost no tar; compact design of down-stream equipment due to without the dilution of N2

Disadvantage High tar content in

syn-gas, limited for scale-up Some tar content in syngas; high ash particles in syngas; high gas ve-locity may cause equip-ment erosion

Requires small particles sizes to promote mass transfer and allow transport; high oxygen demand

Biomass-to-chemical

Biomass can be converted to various chemicals (namely biomass-to-chemical (BtC)) via thermochemical routes based on gasification. The first step in the BtC process is the gasifi-cation of the biomass under high temperature into a syngas. This syngas can then be trans-formed into different types of gaseous or liquid chemical (e.g. bio-SNG, BTL-jet fuel). The state-of-the-art biomass-to-chemical (SoA BtC) process (Fig. 0.8), the synthesis of chemi-cals, requires a higher stoichiometric hydrogen-to-carbon molar ratio (2 - 3) of syngas; however, the syngas derived from biomass gasification is hydrogen deficient with a low hydrogen-to-carbon ratio (below 1). Hence, syngas composition needs to be adjusted by reducing carbon fraction and increasing hydrogen content, which can be achieved by em-ploying the water-gas-shift reactor (WGSR) with the result of an increased CO2

concentra-tion. More than half of biomass carbon ends up into CO2 [71] and needs to be removed via

the acid gas removal (AGR) process, which is usually used in energy-intensive amines-based chemical absorption processes [72]. However, for ammonia synthesis, due to am-monia is a carbon-free chemical, biomass gasification produces syngas passes through two-stage WGSRs to increase the H2 content and then acid-gas removal and methanation

processes to derive high-purity H2. Almost all carbon in biomass is eventually converted to

CO2. Potential bio-chemicals include synthetic natural gas (SNG), methanol (MeOH),

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urea. For instance, there have been many biomass-to-methanol (BtM) plants under opera-tion, e.g., VärmlandsMethanol AB in Sweden (300 t/day [73]), Enerken in Canada (300 ktonne/year [74]) and Delfzijl Plant in the Netherlands (413 ktonne/year [75]). A demon-stration project of biomass-to-SNG called GoBiGas plant was initiated in Gothenburg in 2005 with a production capacity being 20 MW of bioSNG delivered to the natural gas grid in Sweden [76]. The R&D BioTfuel project was built from 2010 to 2019 to produce 200 ktonne/year of liquid products via biomass-to-FT fuels at Total site in Dunkirk [77].

Fig. 0.8. Schematic of the state-of-the-art biomass-to-chemicals process.

To deal with low carbon utilization in the SoA BtC process, injecting green hydrogen into the syngas derived from gasification can be adopted to increase the H/C ratio so that the biomass carbon could be fully converted to chemicals without WGSR and AGR (e.g., Fig. 0.9). Green hydrogen is mainly produced via H2O electrolysis driven by renewable power

with the by-product, i.e., high purity oxygen, used as the gasification agent. In this way, the expensive and energy-intensive cryogenic air separation unit (ASU) can be avoided [16,71]. The significant potential is predicted for such power-to-chemicals processes, and IEA predicts that nearly half of total ammonia and two-thirds of methanol will be produced via electrolysis by 2050 [1].

Gas Cleaning

Raw gas Syngas WGSR Syngas AGR Rich syngas Chemical_Systhesis

ASU Oxygen

Air Nitrogen

Ash & Tar Steam CO2

Upgrating process Methane Methanol Dimethyl ether Jet fuel Gasoline . . . Gasifier Biomass

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Fig. 0.9. Schematic of the biomass-to-chemicals process integrated with water electrolysis.

Electrolysis technologies

Electrolysis can be used to convert renewable electrical energy into chemical via power-to-hydrogen process. The core of green power-to-hydrogen production is electrolysis technology, which utilizes renewable electricity to electrochemically split water into H2 and O2. In general,

three available electrolysis technologies are beneficial to the power-to-hydrogen process [78]: alkaline electrolysis (AE), acidic polymer electrolyte membrane electrolysis (PEME), and solid oxide electrolysis (SOE). The comparison of the three electrolysis technologies is shown in Fig. 0.10 - Fig. 0.11.

Fig. 0.10. Conceptual schematic of three electrolysis technologies (adapted from [79]).

AE technology is the most mature and widely used for commercial large-scale industry applications [78,80]. The main advantages of this technology are [81]: (1) low cost; (2) high reliability and durability and (3) the possibility to operate at elevated pressure. This technology use two electrodes (e.g., mild steel for the cathode and nickel for the anode)

Gas Cleaning

Raw gas Syngas Rich syngas Chemical _Systhesis

Ash & Tar

Hydrogen Mix Water Electrolysis Water Extra oxygen Oxygen Oxygen Upgrating Process Methane Methanol Dimethyl ether Jet fuel Gasoline . . . Gasifier Biomass

M=(H2-CO2)/(CO+CO2)<1 M=(H2-CO2)/(CO+CO2)>=2

D ia ph ra gm An od e Ca th od e OH -KOH KOH e- _e -O2 H2 An od e Ca th od e e- _e -O2 H2O H2 M em br an e H+ An od e Ca th od e e- _e -O2 H2O H2 G as -ti gh t m em br an e O 2-AE PEME SOE

Anode: 2H2O + 2e-→H2+ 2OH- 2H++ 2e-→H2 H2O + 2e-→H2+ O

2-Cathode: 2OH-_→_1/2O

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-immersed in an aqueous alkaline solution (KOH or NaOH) and separated by a diaphragm [78,82]. Due to the alkaline solution (e.g., 20 - 30 wt% of KOH) is highly corrosive, the maintenance costs are very high. The conventional AE usually operates at 60-80 ℃ and 1 - 30 bar with a voltage of 1.8 - 2.4 V and a current density of 0.2 - 0.4 A cm-2_{, which leads}

to low electrical efficiency and power density. Moreover, the dynamic operation of AE (e.g., frequent start-ups and varying power input) is limited and can negatively affect the efficiency and gas purity [80].

PEME technology is based on solid polymer membranes (e.g., Nafion polymer) for water electrolysis, which is commercialized and mostly used for small-scale applications [80]. Compared to AE, PEME instead of liquid electrolytes offers a fast response to the power input and allows a wide range of power input [79], higher electrical efficiency and power density with a voltage of 1.7 - 2.0 V and a current density of over 1.0 A cm-2_{[80,83], safer}

(no corrosive liquid electrolyte is required) and offer the possibility to operate more safely under pressure for onboard storage or offboard removal of the hydrogen by-product [81]. However, due to the high costs of the membrane and noble metals-based electrocatalysts, PEME is more expensive than AE.

Fig. 0.11. Comparison of three different electrolysis technologies at stack level for today (solid eclipse) and future (dashed eclipse) permission (the figure from [83]).

SOE is the latest developed electrolysis technology and laboratory/demonstration-available, which use solid ion-conducting ceramics as the electrolyte to be able to operate at high temperature (650-1000 ℃) [80]. Compared with the AE and PEME technologies, SOE

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provides a much higher electrical efficiency and power density due to it operates under high temperature with a much lower cell voltage (below 1.4 V) and a moderate current density of 0.4 - 1 A cm-2_{[82,83]. SOE also offers an excellent capacity for co-electrolysis}

of steam and CO2 to produce syngas, which can be used to synthesize value-added

chemi-cals. Besides, high-temperature SOE can offer the opportunity of heat integration with the biomass-to-chemicals, because of enhanced system-level heat integration by utilizing waste heat from gasification and chemical synthesis process to generate steam for SOE, thus resolving the bottleneck of enhancing system efficiency of power-to-hydrogen. In this thesis, the SOE technology is employed.

Objectives

Multiple research studies have been carried out in the field of biomass-to-fuel by integrat-ing with power-to-gas concepts. In each of these studies, a large range of technical and economic performance results making a consistent comparison difficult considerably due to differences of technologies and operating conditions considered, the assumptions made, the methodologies applied, thermodynamic models, and software tools used. Therefore, to overcome the difficulties mentioned above, the goal of this thesis is to propose a compre-hensive consistent comparison framework for investigating techno-economically the SOE-based power-to-gas driven renewable chemical production processes using the same meth-odology and uniform assumptions, and the same software tools. The main objectives of this research are:

● To design green chemical synthesis processes with an innovative concept (i.e., methane,

methanol, dimethyl ether, jet fuel, ammonia, urea) and establish the simulation models.

● The thermodynamic performance of various green chemical synthesis processes was

comprehensively investigated through system-level heat integration and steam Rankine cycle for waste heat recovery.

● To establish an economic model for the green chemical synthesis processes, and evaluate

the investment cost and production cost of each process.

● Based on several typical chemical synthesis processes (i.e., methanol and ammonia), the

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efficiency and chemical production cost, and the trade-off of the two performance indica-tors.

Outline

This thesis is organized as follows: Chapter 1, the methodology is introduced with a de-tailed definition of thermodynamic and economic performance. In Chapter 2, two concepts of coupling the SOE of steam with biomass-to-methanol processes are optimized techno-economically and compared with the state-of-the-art biomass-to-methanol plant. In Chap-ter 3, two concepts for full biomass carbon conversion are proposed by integrating SOE: (1) biomass-to-fuels with steam electrolysis and (2) biomass-to-fuels with co-electrolysis. The concepts of SOE-based biomass-to-fuels are techno-economically evaluated and compared with the state-of-the-art biomass-to-fuels for various products, i.e., synthetic natural gas, methanol, dimethyl ether, and jet fuel via Fischer-Tropsch synthesis. In Chapter 4, the two green ammonia production processes are investigated techno-economically with multi-objective optimization and compared with the state-of-the-art methane-to-ammonia plant. Chapter 5, based on Chapter 4, further evaluates the techno-economic feasibility of 100% renewable urea production. Two green urea production processes, called biomass-to-urea and biomass- and power-to-urea, are assessed and compared with the state-of-the-art me-thane-to-urea.

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Methodology

Overview

This chapter describes the methodology of conceptual process design, techno-economic evaluation, and multi-objective optimization that has been adopted throughout this thesis.

1.1 Introduction

In general, conceptual process design is mainly used to evaluate the thermodynamic and economic performance of the process. These two performances will be conflicting since pursuing a higher thermodynamic performance leads to an increase in the production cost, so no single optimum can be found. The multi-objective optimization (MOO) methodology allows to address several conflicting objectives simultaneously such as thermodynamic and economic impacts. The goal of MOO is to understand the trade-offs between conflicting objectives and to find a cluster of high-quality Pareto-optimal solutions.

Systematic methodologies for conceptual process design, techno-economic evaluation, and MOO are carried out using the in-house platform OSMOSE developed by the Group of Industrial Process and Energy Systems Engineering (IPESE) at École Polytechnique Fédérale de Lausanne (EPFL), which can readily integrate the flow-sheeting software (i.e., Aspen plus, Belsim Vali) and perform plant-wise process and heat integration to close the energy balance of the overall system. Once the process and heat integration are completely defined the performance evaluation can be carried out according to the thermodynamic and economic (i.e. techno-economic) criteria. The results can then be used in the frame of a MOO to obtain, given the decision variables, the optimal solutions in terms of conflictive objectives [84]. The MOO problem is solved using a Queueing Multi-Objective Optimiza-tion (QMOO) technique developed by Leyland [85], which is a robust evoluOptimiza-tionary algo-rithm designed at EPFL to find global optimum together with many local sub-optimal solu-tions [86]. This platform has been employed to investigate the design of many complex

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systems, e.g. [84,87,88,89]. The architecture of the platform is schematically given in Fig. 1.1, and briefly described below:

Fig. 1.1. The employed multi-objective optimization framework (adapted from [83]). HEN - heat exchanger network; Decision variables: EFG: operating pressure; SOE: operating pressure, steam utilization, steam and sweep feed flowrate; Steam turbine network: live steam pressure, super-heating temperature; ammonia synthe-sis: reactor operating pressure.

(1) With specific values of decision variables and fixed technology specifications related to the conceptual process design, the process models are simulated to obtain the mass and energy flows of the whole plant.

(2) Heat and mass integration, formulated in a mixed-integer linear programming problem (MILP), is performed mathematically using heat cascade calculation with the selection and sizing of hot and cold utilities to realize maximum heat recovery and close the energy bal-ance.

(3) Classical hot-cold and grand composite curves are obtained for the interpretation of heat integration. The minimum numbers, area, and cost of the heat exchangers are estimat-ed by the vertical heat transfer [90].

Technology selection/ Conceptual process designs

Energy-flow model

(Flowsheeting software: Aspen plus, Belsim Vali, etc.)

Model inputs

Energy integration model

(MILP: AMPL/CPLEX) Multi-scenario optimal selection, sizing of

utility with heat cascade calculation Mass & energy flows

HEN estimation/design

 Minimum number of heat exchangers  Area/cost estimation Graphical display/post-compute  Thermodynamic evaluation  Economic evaluation  Objective calculation MOO Multi-objective optimization algorithm Objectives Decision variables thermo-dynamic targets

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(4) The values of the objective functions or evaluation indicators are then evaluated with the investment of major equipment and main economic assumptions.

(5) MOO is carried out to vary the variables and to evaluate the process designs with the objective values. The steps (1) - (4) are repeated until a cluster of high-quality Pareto-optimal solutions is found, which reveal potential trade-offs between different objective functions.

1.2 Thermodynamic performance evaluation

The thermodynamic performance evaluation mainly includes conceptual process design and modeling, process integration, and performance indicator calculation.

1.2.1 Conceptual process design and modeling

It is necessary to carry out a conceptual process design to evaluate the thermodynamic performance of a novel process. Based on literature and industrial surveys, the raw materi-als, products and by-products, main process units, and feasible operating conditions of the process to be designed should be determined first. Then, for the different process units, such as biomass gasification, water electrolysis, chemical synthesis and purification, are reasonably integrated and designed into a process.

Process models are developed by a commercial flow-sheeting software Aspen plus in this thesis. The model parameters and operating conditions are obtained from the literature and industry data. The outputs of the models, such as mass flows, energy flows, material, pres-sure, and temperature, are used as inputs for energy integration and the techno-economic performance evaluation in the post-compute.

1.2.2 Energy integration

Energy integration is a process design approach based on mass and energy balance. Its purpose is to improve the efficiency of the existing process and design a more efficient process, thereby saving raw materials and energy, reducing operating and investment costs, and environmental impacts.

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can be obtained, and then the energy integration can be performed. The core of the energy integration is the pinch analysis (PA), which is a powerful technique to identify the possi-ble energy recovery by counter-current heat exchange between hot (heat source) and cold (heat sink) streams in a process. The PA is based on the definition of the minimum temper-ature approach (∆𝑇𝑇𝑚𝑚𝑖𝑖𝑖𝑖) that assures a feasible heat exchange between the hot and cold streams, and represents the energy-capital trade-off between the energy savings and heat exchangers investment. The minimum temperature differences are assumed: 10 ℃ for liquid streams, 20 ℃ for low-temperature gas streams, and 30 ℃ for high-temperature gas streams. The aims of PA are to design an optimal heat exchange network (HEN) between hot and cold streams, to achieve maximum heat recovery and optimal use of utilities. To achieve this, three golden rules have to be respected [91]:

1) Do not transfer heat across the pinch. 2) Do not use cold utilities above the pinch. 3) Do not use hot utilities below the pinch.

In the OSMOSE platform, a mathematical programming approach is used to solve the en-ergy integration problem, which is formulated as a mixed-integer linear programming (MILP) problem. The platform offers optimal settings in terms of the selection and sizes of utilities while computing the mass balances and the heat cascade of the PA. The detailed formulation has been explained in [84,87,92,93], so the thesis only presents a brief intro-duction. If energy balance still exists excess heat after energy recovery, a steam turbine network can be employed and integrated into the process to recover the excess heat. When the process can not realize heat self-sufficiency, a hot utility should be employed to close the energy balance of the whole process. The hot utility can be provided by the combustion of the selected process streams (e.g., syngas) or feedstock (e.g., natural gas), or electrical heating. The cold utility is provided by the cooling water from rivers or lakes.

After the heat cascade calculation, the hot and cold streams can be represented graphically by classical hot-cold and grand composite curves, as in the example reported in Fig. 1.2-Fig. 1.3.

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Fig. 1.2. The hot-cold composite curve of heat integration： (a) excess heat recovery is not considered；(b) excess heat is recovered by steam turbine network.

Fig. 1.3. The composite curve of heat integration： (a)Grand composite curve；(b) Integrated composite curve for steam turbine network.

1.2.3 Thermodynamic indicators

The thermodynamic performance depends on the one hand on the efficiency of raw materi-al conversion into fuel/chemicmateri-al defined by the process and operating conditions choice, final product type, and on the other hand on the quality of the process and heat integration that depends on the utility technologies, the excess heat recovery, and the combined heat and power production. The overall energetic performance is schematically described in Fig. 1.4.

Fig. 1.4. Overall energetic schematic of the inputs and outputs of the process.

To evaluate the energetic performance of the integrated process, the overall energy effi-ciency is defined in Eq. 1.1 based on the first principle of thermodynamic, and considers

0 200 400 600 800 1000 1200 1400 0 400 800 1200 1600 2000 Te m pe ra tu re (℃ )

Heat Load (kW/MWBiomass) Hot streams Cold streams (b) 0 200 400 600 800 1000 1200 1400 0 400 800 1200 1600 2000 Te m pe ra tu re (℃ )

Heat Load (kW/MWBiomass)

Hot streams Cold streams (a) 0 200 400 600 800 1000 1200 1400 0 100 200 300 400 500 600 700 Te m pe ra tu re (℃ )

Heat Load (kW/MWBiomass) Other process Steam turbine network

(b) 0 200 400 600 800 1000 1200 1400 0 100 200 300 400 500 Te m pe ra tu re (℃ )

Heat Load (kW/MWBiomass)

(a)

Process

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chemical and mechanical energy as being equivalent. The overall exergy efficiency is de-fined in Eq. 1.2. 𝜂𝜂 = ∑ 𝑀𝑀̇𝑖𝑖∙𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖+∆𝐸𝐸̇− ∑ 𝑀𝑀̇𝑗𝑗∙𝐿𝐿𝐿𝐿𝐿𝐿𝑗𝑗+∆𝐸𝐸̇+∙ 100 (1.1) 𝜀𝜀 = ∑ 𝑀𝑀̇𝑖𝑖∙𝐸𝐸𝑖𝑖+∆𝐸𝐸̇− ∑ 𝑀𝑀̇𝑗𝑗∙𝐸𝐸𝑗𝑗+∆𝐸𝐸̇+∙ 100 (1.2)

In these definitions, 𝑀𝑀̇ and 𝐸𝐸 designate the mass flowrate and exergy rate of products i and raw materials j. LHV is the lower heating value of products i and raw materials j. The superscripts - and + refer, respectively, to the net produced (output to the power grid) and consumed (input from the power grid) services, and ∆𝐸𝐸̇ = |𝐸𝐸̇−_{− 𝐸𝐸̇}+_{|, either the net} elec-tric power input (∆𝐸𝐸̇+_{) or output (∆𝐸𝐸̇}+_{) of the process is considered since only the overall} balance is of interest [94].

1.3 Economic performance evaluation

For a new process, in addition to the thermodynamic performance evaluation, a compre-hensive evaluation of its economic feasibility is also of great importance. With economic evaluation, several leading economic indicators of the process can be obtained to (1) pre-dict the investment feasibility of the process, and (2) provide a reference for policy-makers in the industry or governments. The economic performance evaluation is performed by capital expenditure (CAPEX) and operational expenditure (OPEX) estimates. The econom-ic modelling in this thesis is carried out on the basis of previous work of IPESE group at EPFL and an extensive literature survey.

1.3.1 The capital investment

The estimated methodology of capital expenditure (CAPEX) is based on [90,95]. To esti-mate the total investment cost of the equipment, the first step is to estiesti-mate the purchase cost, 𝐶𝐶p, of the equipments with the following procedure.

The purchase cost of the equipment, operating at reference pressure and made of carbon steel, 𝐶𝐶𝑑𝑑0, can be expressed in the following equation Eq. (1.3).

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log10𝐶𝐶p0= 𝐾𝐾1+ 𝐾𝐾2∙ log10𝐴𝐴 + 𝐾𝐾3∙ (log10𝐴𝐴)2 (1.3) where A is the capacity or sizing parameter (for example the power of a turbine or the sur-face area for a heat exchanger), while 𝐾𝐾1, 𝐾𝐾2, and 𝐾𝐾3 are the parameters fitted from market studies.

As the cost of equipment increases with operating pressure and material grades increase, Eq. (1.3) is no longer applicable. The additional pressure and material-factors will be em-ployed to account for the changes in operating pressure and the materials used. The revised equipment price is defined as “Bare Module Cost”, as given in Eq. (1.4).

𝐶𝐶BM= 𝐶𝐶p0𝐹𝐹BM= 𝐶𝐶p0(𝐵𝐵1+ 𝐵𝐵2𝐹𝐹M𝐹𝐹P) (1.4) where 𝐶𝐶BM is the bare module cost of equipment, 𝐹𝐹BM is the bare module cost factor, 𝐵𝐵1, 𝐵𝐵2 are factors depending on the type of equipment. The 𝐹𝐹M and 𝐹𝐹P are pressure and mate-rial factors.

The best estimate of the purchase cost of major equipment is to use the reference cost data from previously purchased equipment of the same type. The bare module cost of the equipment can be estimated by Eq. (1.5).

𝐶𝐶BM= 𝐶𝐶p,ref0 ∙ (_𝐴𝐴𝐴𝐴_ref0 )𝑚𝑚 (1.5)

where 𝐶𝐶p,ref0 and 𝐴𝐴0ref refer to the base cost and reference size or capacity of equipment from literature. The cost exponent 𝑚𝑚 vary between 0.30 to 0.84 and usually referenced from the literature. If no further information is available, 𝑚𝑚 is assumed to be 0.65 in this thesis.

For the compressor purchase cost, the pressure- and material-factored method is only ap-plicable when the power of the compressor is less than 75 kW; when the power of the compressor is more than 75 kW, another method is employed by [96], as shown in Eq. (1.6).

𝐶𝐶BM= 𝑎𝑎 + 𝑏𝑏𝑆𝑆𝑖𝑖 (1.6)

where a, b are the cost constants. 𝑆𝑆 is the sizing parameter (driver power of compressor). When the equipment cost data comes from records or literature for price information, it should be converted into the present time by Eq. (1.7).

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𝐶𝐶pt = 𝐶𝐶p0∙ (_𝐼𝐼_{ref_index}𝐼𝐼index ) (1.7) where the C is purchased cost, subscript t refers to the time when the cost is desired, and subscript 0 refers to the reference time when the cost is known. 𝐼𝐼index and 𝐼𝐼ref_index are cost indexes of the desired and reference time, e.g., the Marshall and Swift Equipment cost index.

Total module cost 𝐶𝐶TM refers to the cost of making small to moderate expansions or altera-tions to an existing facility. The 𝐶𝐶TM is composed of 𝐶𝐶BM and “contingency and fee costs”. The latter is included in the cost in the evaluation of the cost as a protection against over-sights and faulty information. Unless otherwise stated, values of 15% and 3% of the bare module cost are assumed for contingency costs and fees, respectively. The 𝐶𝐶TM can be evaluated by Eq. (1.8).

𝐶𝐶TM= 1.18 ∙ 𝐶𝐶BM (1.8)

A completely-new facility to be constructed on essentially undeveloped land is defined as Grassroots 𝐶𝐶GR. The 𝐶𝐶GR is composed of 𝐶𝐶TM and auxiliary facilities costs. The auxiliary facilities costs include costs for site development, auxiliary buildings, and off-sites and utilities. Unless otherwise stated, the auxiliary facilities costs are assumed to be equal to 50% of the 𝐶𝐶BM for the base case conditions. The 𝐶𝐶GR can be evaluated by Eq. (1.9).

𝐶𝐶GR= 𝐶𝐶TM+ 0.5 ∙ 𝐶𝐶BM (1.9)

1.3.2 The operating cost

The operating expenditure (OPEX) includes depreciation cost of the total investment, op-erational cost (i.e., labour, raw materials, catalysts, and imported electricity), and revenue (product and by-product). The OPEX is calculated based on a series of economic assump-tions, such as the plant lifetime, interest rate, annual operating hours, raw materials price, product and by-product price, and so on, as given in the following Chapters.

The estimated methodology for the cost of labor is based on [90]. The operating labor re-quirement for chemical processing plants can be evaluated by the equation Eq. (1.10) and (1.11).

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𝑁𝑁OL= (6.29 + 31.7 ∙ 𝑃𝑃2+ 0.23 ∙ 𝑁𝑁np)0.5 (1.10) where 𝑁𝑁OL is the number of operators per shift, 𝑃𝑃 is the number of processing steps involv-ing the handlinvolv-ing of particulate solids, such as transportation and distribution, particulate size control, and particulate removal. The handling of particulate solids is not considered in this study, and the value of 𝑃𝑃 was assumed as zero, because we considered this as an ex-ternal service that is quite sensitive to the location of the plant and the availability of bio-mass. 𝑁𝑁np is the number of equipment, such as compressors, towers, heater exchangers. The value of 𝑁𝑁OL in Eq. (1.10) is the number of operators required to run the process unit per shift. The total number of operators can be estimated according to the following equa-tion Eq. (1.11).

Operating labor = 4.5 × 𝑁𝑁OL (1.11) The cost of operating labor can be calculated by operating labor multiplied by operator salary. The salary per operator is assumed to be 52900 $/year in this study.

With the calculated investment cost, the depreciation cost can be computed by Eq. (1.12) [97]:

𝐶𝐶dep= 𝐶𝐶inv×𝑖𝑖×(𝑖𝑖+1)

𝑛𝑛

(𝑖𝑖+1)𝑛𝑛₋₁ (1.12)

where 𝐶𝐶inv is total investment cost, 𝑖𝑖 is annual interest rate and n is plant lifetime (year). The levelized cost of product LCOP ($/GJ or $/tonne) can be estimated by Eq. (1.13) [97]:

𝐿𝐿𝐶𝐶𝐿𝐿𝑃𝑃 =𝐶𝐶opt+𝐶𝐶dep−𝐶𝐶revbyp

𝑃𝑃product (1.13)

where 𝑃𝑃product is the capacity of fuel production (GJ/year or tonne/year), 𝐶𝐶revbyp is by-product revenues ($/year), 𝐶𝐶dep is the depreciation cost ($/year).

The payback time τ is estimated as the total investment cost divided by the annual profit of plant by Eq. (1.14):

𝜏𝜏 = 𝐶𝐶inv

𝐶𝐶rev+𝐶𝐶revbyp−𝐶𝐶opt (1.14)

Renewable-driven Chemical Processes via Power-to-Gas Concepts