P.B. Tamarona
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Multi-Objective Optimization of Precooling Stage in Hydrogen Liquefaction
A Process Modelling Approach to Harmonize Technical and Economic Trade-Offs
Hydrogen is getting great attention as a key energy carrier for a cleaner energy future, with demand projections up to 20% of global energy demand by 2050. However, the low volumetric density of hydrogen leads to a challenge for storage and transport purposes, making liquefaction a promising solution that also ensures high purity. However, at the current time, the high energy consumption of liquefaction remains a major obstacle. Within this process, the precooling stage is the second most energy-intensive step, covering the broadest temperature range but offering flexibility in terms of refrigerant choice, cycle configuration, and operating conditions.
However, most of the study of the hydrogen precooling omits economic analysis, refrigerant freeze-out discussion, and employs a portion of a non-environmentally sustainable substance as the refrigerant. This study focused on addressing the gap by conducting multi-objective optimization (MOO) on specific energy consumption (SEC) and levelized precooling cost (LPC) to find out the optimal trade-off between the technical and economic competitiveness of the precooling stage, while ensuring the freeze-out risk in the streams was avoided and using environmentally friendly mixed refrigerant (MR) mixtures.
Two cycles, namely single mixed refrigerant (SMR) and dual mixed refrigerant (DMR), are modeled in Aspen HYSYS V12, where the configurations are defined based on freeze-out consideration. Nine MR mixtures for SMR and DMR are defined based on their thermophysical properties. A Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm was used for MOO through the pymoo library package in Python, which was then coupled with Aspen HYSYS. The decision variables to be optimized include MR composition, MR flow rate, compressor discharge pressure, and JT valve outlet pressure. Several constraints are also introduced, such as vapor fraction at the inlet compressor, minimum internal temperature difference (MITD) of heat exchangers, JT valve temperature difference, and several temperature constraints to ensure thermodynamic behavior is not violated.
In this study, Mixture 8 (for SMR) and Mixture 6 (for DMR) appear to be the top-performing mixtures, achieving specific energy consumptions (SEC) of 1.25 kWh/kgH2 and 1.13 kWh/kgH2, respectively, with levelized precooling costs (LPC) of €0.47/kgH2 and €0.60/kgH2. The study indicates that aligning the boiling points of mixed refrigerant components to enhance temperature glide, combined with tuning of operating conditions, is the key to achieving both energy efficiency and cost competitiveness. Furthermore, the optimized results in DMR also suggest that the intermediate-compression stage in the MR2 cycle could be removed.
From the sensitivity analysis, it was observed that as the precooling temperature target increased, the gap in SEC between the SMR and DMR configurations narrowed. Starting from 95 K, both systems reached similar SEC values, highlighting equal technical performance. However, the LPC further SMR dominant over the DMR. This indicates that beyond this temperature target, DMR was no longer economically competitive. Additionally, variations in pressure drop across heat exchangers and coolers had a stronger impact on the SMR configuration. The percentage increase in SEC and LPC was more severe due to the accumulation of pressure losses within a single-loop cycle. In contrast, the DMR system distributes losses across two separate loops, making it less sensitive to pressure drop effects. ...
However, most of the study of the hydrogen precooling omits economic analysis, refrigerant freeze-out discussion, and employs a portion of a non-environmentally sustainable substance as the refrigerant. This study focused on addressing the gap by conducting multi-objective optimization (MOO) on specific energy consumption (SEC) and levelized precooling cost (LPC) to find out the optimal trade-off between the technical and economic competitiveness of the precooling stage, while ensuring the freeze-out risk in the streams was avoided and using environmentally friendly mixed refrigerant (MR) mixtures.
Two cycles, namely single mixed refrigerant (SMR) and dual mixed refrigerant (DMR), are modeled in Aspen HYSYS V12, where the configurations are defined based on freeze-out consideration. Nine MR mixtures for SMR and DMR are defined based on their thermophysical properties. A Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm was used for MOO through the pymoo library package in Python, which was then coupled with Aspen HYSYS. The decision variables to be optimized include MR composition, MR flow rate, compressor discharge pressure, and JT valve outlet pressure. Several constraints are also introduced, such as vapor fraction at the inlet compressor, minimum internal temperature difference (MITD) of heat exchangers, JT valve temperature difference, and several temperature constraints to ensure thermodynamic behavior is not violated.
In this study, Mixture 8 (for SMR) and Mixture 6 (for DMR) appear to be the top-performing mixtures, achieving specific energy consumptions (SEC) of 1.25 kWh/kgH2 and 1.13 kWh/kgH2, respectively, with levelized precooling costs (LPC) of €0.47/kgH2 and €0.60/kgH2. The study indicates that aligning the boiling points of mixed refrigerant components to enhance temperature glide, combined with tuning of operating conditions, is the key to achieving both energy efficiency and cost competitiveness. Furthermore, the optimized results in DMR also suggest that the intermediate-compression stage in the MR2 cycle could be removed.
From the sensitivity analysis, it was observed that as the precooling temperature target increased, the gap in SEC between the SMR and DMR configurations narrowed. Starting from 95 K, both systems reached similar SEC values, highlighting equal technical performance. However, the LPC further SMR dominant over the DMR. This indicates that beyond this temperature target, DMR was no longer economically competitive. Additionally, variations in pressure drop across heat exchangers and coolers had a stronger impact on the SMR configuration. The percentage increase in SEC and LPC was more severe due to the accumulation of pressure losses within a single-loop cycle. In contrast, the DMR system distributes losses across two separate loops, making it less sensitive to pressure drop effects. ...
Hydrogen is getting great attention as a key energy carrier for a cleaner energy future, with demand projections up to 20% of global energy demand by 2050. However, the low volumetric density of hydrogen leads to a challenge for storage and transport purposes, making liquefaction a promising solution that also ensures high purity. However, at the current time, the high energy consumption of liquefaction remains a major obstacle. Within this process, the precooling stage is the second most energy-intensive step, covering the broadest temperature range but offering flexibility in terms of refrigerant choice, cycle configuration, and operating conditions.
However, most of the study of the hydrogen precooling omits economic analysis, refrigerant freeze-out discussion, and employs a portion of a non-environmentally sustainable substance as the refrigerant. This study focused on addressing the gap by conducting multi-objective optimization (MOO) on specific energy consumption (SEC) and levelized precooling cost (LPC) to find out the optimal trade-off between the technical and economic competitiveness of the precooling stage, while ensuring the freeze-out risk in the streams was avoided and using environmentally friendly mixed refrigerant (MR) mixtures.
Two cycles, namely single mixed refrigerant (SMR) and dual mixed refrigerant (DMR), are modeled in Aspen HYSYS V12, where the configurations are defined based on freeze-out consideration. Nine MR mixtures for SMR and DMR are defined based on their thermophysical properties. A Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm was used for MOO through the pymoo library package in Python, which was then coupled with Aspen HYSYS. The decision variables to be optimized include MR composition, MR flow rate, compressor discharge pressure, and JT valve outlet pressure. Several constraints are also introduced, such as vapor fraction at the inlet compressor, minimum internal temperature difference (MITD) of heat exchangers, JT valve temperature difference, and several temperature constraints to ensure thermodynamic behavior is not violated.
In this study, Mixture 8 (for SMR) and Mixture 6 (for DMR) appear to be the top-performing mixtures, achieving specific energy consumptions (SEC) of 1.25 kWh/kgH2 and 1.13 kWh/kgH2, respectively, with levelized precooling costs (LPC) of €0.47/kgH2 and €0.60/kgH2. The study indicates that aligning the boiling points of mixed refrigerant components to enhance temperature glide, combined with tuning of operating conditions, is the key to achieving both energy efficiency and cost competitiveness. Furthermore, the optimized results in DMR also suggest that the intermediate-compression stage in the MR2 cycle could be removed.
From the sensitivity analysis, it was observed that as the precooling temperature target increased, the gap in SEC between the SMR and DMR configurations narrowed. Starting from 95 K, both systems reached similar SEC values, highlighting equal technical performance. However, the LPC further SMR dominant over the DMR. This indicates that beyond this temperature target, DMR was no longer economically competitive. Additionally, variations in pressure drop across heat exchangers and coolers had a stronger impact on the SMR configuration. The percentage increase in SEC and LPC was more severe due to the accumulation of pressure losses within a single-loop cycle. In contrast, the DMR system distributes losses across two separate loops, making it less sensitive to pressure drop effects.
However, most of the study of the hydrogen precooling omits economic analysis, refrigerant freeze-out discussion, and employs a portion of a non-environmentally sustainable substance as the refrigerant. This study focused on addressing the gap by conducting multi-objective optimization (MOO) on specific energy consumption (SEC) and levelized precooling cost (LPC) to find out the optimal trade-off between the technical and economic competitiveness of the precooling stage, while ensuring the freeze-out risk in the streams was avoided and using environmentally friendly mixed refrigerant (MR) mixtures.
Two cycles, namely single mixed refrigerant (SMR) and dual mixed refrigerant (DMR), are modeled in Aspen HYSYS V12, where the configurations are defined based on freeze-out consideration. Nine MR mixtures for SMR and DMR are defined based on their thermophysical properties. A Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm was used for MOO through the pymoo library package in Python, which was then coupled with Aspen HYSYS. The decision variables to be optimized include MR composition, MR flow rate, compressor discharge pressure, and JT valve outlet pressure. Several constraints are also introduced, such as vapor fraction at the inlet compressor, minimum internal temperature difference (MITD) of heat exchangers, JT valve temperature difference, and several temperature constraints to ensure thermodynamic behavior is not violated.
In this study, Mixture 8 (for SMR) and Mixture 6 (for DMR) appear to be the top-performing mixtures, achieving specific energy consumptions (SEC) of 1.25 kWh/kgH2 and 1.13 kWh/kgH2, respectively, with levelized precooling costs (LPC) of €0.47/kgH2 and €0.60/kgH2. The study indicates that aligning the boiling points of mixed refrigerant components to enhance temperature glide, combined with tuning of operating conditions, is the key to achieving both energy efficiency and cost competitiveness. Furthermore, the optimized results in DMR also suggest that the intermediate-compression stage in the MR2 cycle could be removed.
From the sensitivity analysis, it was observed that as the precooling temperature target increased, the gap in SEC between the SMR and DMR configurations narrowed. Starting from 95 K, both systems reached similar SEC values, highlighting equal technical performance. However, the LPC further SMR dominant over the DMR. This indicates that beyond this temperature target, DMR was no longer economically competitive. Additionally, variations in pressure drop across heat exchangers and coolers had a stronger impact on the SMR configuration. The percentage increase in SEC and LPC was more severe due to the accumulation of pressure losses within a single-loop cycle. In contrast, the DMR system distributes losses across two separate loops, making it less sensitive to pressure drop effects.
Carbon Nanofibre Purification
Process design, modelling and analysis of carbon nanofibre purification with acid leaching
Catalytic methane pyrolysis (CMP) is a potential method to produce clean hydrogen without direct COx emissions, but is not cost-competitive with current hydrogen production techniques yet. A strategy to increase the cost-competitiveness is to purify and sell the nanocarbon by-product. This paper outlines the process design, modelling and analysis of purifying carbon nanofibre (CNF), produced by CMP, with acid leaching.
CNF produced by CMP with a Ni-SiO2 catalyst was used for this study and initially contains 4700 ppm of nickel. The baseline scenario of the designed process has a production capacity of 20,000 tonnes per year and includes acid leaching with HCl, liquid removal and post-treatment steps. The techno-economic analysis showed a Levelized Cost of Purification (LCOP) of 10.09 $/kg and a Net Present Value (NPV) of 1.48 billion $ for the baseline scenario. The process is very profitable due to the assumed high selling price of 25 $/kg. However, the conversion of nickel is only equal to 5.15 %, leaving 4460 ppm of nickel in the CNF product while the desired nickel content is below 300 ppm. The low conversion indicates that the quality of the CNF product is barely improved and that the assumed selling price is probably too high. The acid leaching kinetics are modelled using literature on acid leaching with HCl of nickel from a Ni-Al2O3 spent catalyst. Acid leaching experiments of nickel from CNF with H2SO4 showed a more positive average nickel conversion of 70.9 % so far. The leaching kinetics still have to be determined for a variety of acids and will be necessary to model the leaching more accurately.
Sensitivity analyses showed that the impact of the acid waste price on the LCOP was the largest of the economic parameters with ±2.5 $/kg variation, followed by the electricity price. The acid feed price also had a significant impact on the LCOP. The high impact of the acid waste and feed price showed a need for the implementation of an acid recycle. A Monte Carlo analysis indicated a robust process design under economic uncertainties. The mean of the LCOP was equal to 10.12 $/kg and the standard deviation was equal to 0.90 $/kg.
Two improved design cases of the baseline scenario are presented. The first includes changes to the reactor temperature, residence time, acid molarity, ratio of CNF feed to acid feed and the inclusion of an acid recycle. The conversion is improved to 61.04 % with an LCOP of 24.68 $/kg. The second design case builds upon the first and includes further changes to the residence time and ratio of CNF feed to acid feed. Furthermore, the reactor setup is changed to three reactors placed in series for the second design case. The conversion is increased to 93.95 %, leaving only 285.66 ppm of nickel in the CNF product. The LCOP is equal to 21.84 $/kg, but a total of 90 reactors are required. While the process is profitable and the nickel content in the product is below 300 ppm, questions arise whether the second improved design is practical. ...
CNF produced by CMP with a Ni-SiO2 catalyst was used for this study and initially contains 4700 ppm of nickel. The baseline scenario of the designed process has a production capacity of 20,000 tonnes per year and includes acid leaching with HCl, liquid removal and post-treatment steps. The techno-economic analysis showed a Levelized Cost of Purification (LCOP) of 10.09 $/kg and a Net Present Value (NPV) of 1.48 billion $ for the baseline scenario. The process is very profitable due to the assumed high selling price of 25 $/kg. However, the conversion of nickel is only equal to 5.15 %, leaving 4460 ppm of nickel in the CNF product while the desired nickel content is below 300 ppm. The low conversion indicates that the quality of the CNF product is barely improved and that the assumed selling price is probably too high. The acid leaching kinetics are modelled using literature on acid leaching with HCl of nickel from a Ni-Al2O3 spent catalyst. Acid leaching experiments of nickel from CNF with H2SO4 showed a more positive average nickel conversion of 70.9 % so far. The leaching kinetics still have to be determined for a variety of acids and will be necessary to model the leaching more accurately.
Sensitivity analyses showed that the impact of the acid waste price on the LCOP was the largest of the economic parameters with ±2.5 $/kg variation, followed by the electricity price. The acid feed price also had a significant impact on the LCOP. The high impact of the acid waste and feed price showed a need for the implementation of an acid recycle. A Monte Carlo analysis indicated a robust process design under economic uncertainties. The mean of the LCOP was equal to 10.12 $/kg and the standard deviation was equal to 0.90 $/kg.
Two improved design cases of the baseline scenario are presented. The first includes changes to the reactor temperature, residence time, acid molarity, ratio of CNF feed to acid feed and the inclusion of an acid recycle. The conversion is improved to 61.04 % with an LCOP of 24.68 $/kg. The second design case builds upon the first and includes further changes to the residence time and ratio of CNF feed to acid feed. Furthermore, the reactor setup is changed to three reactors placed in series for the second design case. The conversion is increased to 93.95 %, leaving only 285.66 ppm of nickel in the CNF product. The LCOP is equal to 21.84 $/kg, but a total of 90 reactors are required. While the process is profitable and the nickel content in the product is below 300 ppm, questions arise whether the second improved design is practical. ...
Catalytic methane pyrolysis (CMP) is a potential method to produce clean hydrogen without direct COx emissions, but is not cost-competitive with current hydrogen production techniques yet. A strategy to increase the cost-competitiveness is to purify and sell the nanocarbon by-product. This paper outlines the process design, modelling and analysis of purifying carbon nanofibre (CNF), produced by CMP, with acid leaching.
CNF produced by CMP with a Ni-SiO2 catalyst was used for this study and initially contains 4700 ppm of nickel. The baseline scenario of the designed process has a production capacity of 20,000 tonnes per year and includes acid leaching with HCl, liquid removal and post-treatment steps. The techno-economic analysis showed a Levelized Cost of Purification (LCOP) of 10.09 $/kg and a Net Present Value (NPV) of 1.48 billion $ for the baseline scenario. The process is very profitable due to the assumed high selling price of 25 $/kg. However, the conversion of nickel is only equal to 5.15 %, leaving 4460 ppm of nickel in the CNF product while the desired nickel content is below 300 ppm. The low conversion indicates that the quality of the CNF product is barely improved and that the assumed selling price is probably too high. The acid leaching kinetics are modelled using literature on acid leaching with HCl of nickel from a Ni-Al2O3 spent catalyst. Acid leaching experiments of nickel from CNF with H2SO4 showed a more positive average nickel conversion of 70.9 % so far. The leaching kinetics still have to be determined for a variety of acids and will be necessary to model the leaching more accurately.
Sensitivity analyses showed that the impact of the acid waste price on the LCOP was the largest of the economic parameters with ±2.5 $/kg variation, followed by the electricity price. The acid feed price also had a significant impact on the LCOP. The high impact of the acid waste and feed price showed a need for the implementation of an acid recycle. A Monte Carlo analysis indicated a robust process design under economic uncertainties. The mean of the LCOP was equal to 10.12 $/kg and the standard deviation was equal to 0.90 $/kg.
Two improved design cases of the baseline scenario are presented. The first includes changes to the reactor temperature, residence time, acid molarity, ratio of CNF feed to acid feed and the inclusion of an acid recycle. The conversion is improved to 61.04 % with an LCOP of 24.68 $/kg. The second design case builds upon the first and includes further changes to the residence time and ratio of CNF feed to acid feed. Furthermore, the reactor setup is changed to three reactors placed in series for the second design case. The conversion is increased to 93.95 %, leaving only 285.66 ppm of nickel in the CNF product. The LCOP is equal to 21.84 $/kg, but a total of 90 reactors are required. While the process is profitable and the nickel content in the product is below 300 ppm, questions arise whether the second improved design is practical.
CNF produced by CMP with a Ni-SiO2 catalyst was used for this study and initially contains 4700 ppm of nickel. The baseline scenario of the designed process has a production capacity of 20,000 tonnes per year and includes acid leaching with HCl, liquid removal and post-treatment steps. The techno-economic analysis showed a Levelized Cost of Purification (LCOP) of 10.09 $/kg and a Net Present Value (NPV) of 1.48 billion $ for the baseline scenario. The process is very profitable due to the assumed high selling price of 25 $/kg. However, the conversion of nickel is only equal to 5.15 %, leaving 4460 ppm of nickel in the CNF product while the desired nickel content is below 300 ppm. The low conversion indicates that the quality of the CNF product is barely improved and that the assumed selling price is probably too high. The acid leaching kinetics are modelled using literature on acid leaching with HCl of nickel from a Ni-Al2O3 spent catalyst. Acid leaching experiments of nickel from CNF with H2SO4 showed a more positive average nickel conversion of 70.9 % so far. The leaching kinetics still have to be determined for a variety of acids and will be necessary to model the leaching more accurately.
Sensitivity analyses showed that the impact of the acid waste price on the LCOP was the largest of the economic parameters with ±2.5 $/kg variation, followed by the electricity price. The acid feed price also had a significant impact on the LCOP. The high impact of the acid waste and feed price showed a need for the implementation of an acid recycle. A Monte Carlo analysis indicated a robust process design under economic uncertainties. The mean of the LCOP was equal to 10.12 $/kg and the standard deviation was equal to 0.90 $/kg.
Two improved design cases of the baseline scenario are presented. The first includes changes to the reactor temperature, residence time, acid molarity, ratio of CNF feed to acid feed and the inclusion of an acid recycle. The conversion is improved to 61.04 % with an LCOP of 24.68 $/kg. The second design case builds upon the first and includes further changes to the residence time and ratio of CNF feed to acid feed. Furthermore, the reactor setup is changed to three reactors placed in series for the second design case. The conversion is increased to 93.95 %, leaving only 285.66 ppm of nickel in the CNF product. The LCOP is equal to 21.84 $/kg, but a total of 90 reactors are required. While the process is profitable and the nickel content in the product is below 300 ppm, questions arise whether the second improved design is practical.