{"title":"二氧化碳有效直接转化为二甲醚设计指南的敏感性分析和多目标优化","authors":"","doi":"10.1016/j.enconman.2024.119092","DOIUrl":null,"url":null,"abstract":"<div><div>Carbon capture, utilization and storage (CCUS) technologies can play an important role in sustainable development and climate change mitigation. After capture, carbon dioxide can be used to produce valuable chemicals such as dimethyl ether (DME). The direct synthesis of DME from CO<sub>2</sub> involves a complex reaction usually carried out on a CuO-ZnO-Al<sub>2</sub>O<sub>3</sub>/γ-Al<sub>2</sub>O<sub>3</sub> catalyst, which can be operated under various temperature, pressure, and feed conditions. Optimizing DME production is a challenging task and warrants thorough investigation. A pseudo-homogeneous mathematical model was developed to describe the behavior of this reaction in a fixed bed reactor. This model was validated with experimental data from the literature and used to provide valuable insights regarding the effects of operating conditions on the yield and selectivity of DME and methanol, as well as on CO<sub>2</sub> conversion. Given the numerous interdependent variables influencing the process, the task of exploring various operating conditions was accomplished using deep neural network (DNN)-based surrogate modeling, significantly reducing computational efforts. Using the surrogate models, multi-objective optimizations were performed with non-dominated sorting genetic algorithm-II (NSGA-II) to establish design guidelines. Results have shown that DME yield is improved by the presence of CO in the feed, and that the optimal operating temperature varies with the operating pressure. Additionally, the H<sub>2</sub>/CO<sub>2</sub> feed ratio has a minor impact on DME formation, though its selectivity over methanol is increased. Simulations have indicated that water presence hinders DME production. Therefore, the removal of water is worth of further investigation and is likely to improve the process. The optimizations using the NSGA-II algorithm identified that a H<sub>2</sub>/CO<sub>2</sub> ratio of 5.0 yielded optimal conditions with high DME selectivity. At higher ratios, selectivity shifted towards MeOH, indicating increased separation costs. Lower temperatures favored MeOH production over DME.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":null,"pages":null},"PeriodicalIF":9.9000,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sensitivity analysis and multi-objective optimization for design guideline of effective direct conversion of CO2 to DME\",\"authors\":\"\",\"doi\":\"10.1016/j.enconman.2024.119092\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Carbon capture, utilization and storage (CCUS) technologies can play an important role in sustainable development and climate change mitigation. After capture, carbon dioxide can be used to produce valuable chemicals such as dimethyl ether (DME). The direct synthesis of DME from CO<sub>2</sub> involves a complex reaction usually carried out on a CuO-ZnO-Al<sub>2</sub>O<sub>3</sub>/γ-Al<sub>2</sub>O<sub>3</sub> catalyst, which can be operated under various temperature, pressure, and feed conditions. Optimizing DME production is a challenging task and warrants thorough investigation. A pseudo-homogeneous mathematical model was developed to describe the behavior of this reaction in a fixed bed reactor. This model was validated with experimental data from the literature and used to provide valuable insights regarding the effects of operating conditions on the yield and selectivity of DME and methanol, as well as on CO<sub>2</sub> conversion. Given the numerous interdependent variables influencing the process, the task of exploring various operating conditions was accomplished using deep neural network (DNN)-based surrogate modeling, significantly reducing computational efforts. Using the surrogate models, multi-objective optimizations were performed with non-dominated sorting genetic algorithm-II (NSGA-II) to establish design guidelines. Results have shown that DME yield is improved by the presence of CO in the feed, and that the optimal operating temperature varies with the operating pressure. Additionally, the H<sub>2</sub>/CO<sub>2</sub> feed ratio has a minor impact on DME formation, though its selectivity over methanol is increased. Simulations have indicated that water presence hinders DME production. Therefore, the removal of water is worth of further investigation and is likely to improve the process. The optimizations using the NSGA-II algorithm identified that a H<sub>2</sub>/CO<sub>2</sub> ratio of 5.0 yielded optimal conditions with high DME selectivity. At higher ratios, selectivity shifted towards MeOH, indicating increased separation costs. Lower temperatures favored MeOH production over DME.</div></div>\",\"PeriodicalId\":11664,\"journal\":{\"name\":\"Energy Conversion and Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.9000,\"publicationDate\":\"2024-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy Conversion and Management\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0196890424010331\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Conversion and Management","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0196890424010331","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Sensitivity analysis and multi-objective optimization for design guideline of effective direct conversion of CO2 to DME
Carbon capture, utilization and storage (CCUS) technologies can play an important role in sustainable development and climate change mitigation. After capture, carbon dioxide can be used to produce valuable chemicals such as dimethyl ether (DME). The direct synthesis of DME from CO2 involves a complex reaction usually carried out on a CuO-ZnO-Al2O3/γ-Al2O3 catalyst, which can be operated under various temperature, pressure, and feed conditions. Optimizing DME production is a challenging task and warrants thorough investigation. A pseudo-homogeneous mathematical model was developed to describe the behavior of this reaction in a fixed bed reactor. This model was validated with experimental data from the literature and used to provide valuable insights regarding the effects of operating conditions on the yield and selectivity of DME and methanol, as well as on CO2 conversion. Given the numerous interdependent variables influencing the process, the task of exploring various operating conditions was accomplished using deep neural network (DNN)-based surrogate modeling, significantly reducing computational efforts. Using the surrogate models, multi-objective optimizations were performed with non-dominated sorting genetic algorithm-II (NSGA-II) to establish design guidelines. Results have shown that DME yield is improved by the presence of CO in the feed, and that the optimal operating temperature varies with the operating pressure. Additionally, the H2/CO2 feed ratio has a minor impact on DME formation, though its selectivity over methanol is increased. Simulations have indicated that water presence hinders DME production. Therefore, the removal of water is worth of further investigation and is likely to improve the process. The optimizations using the NSGA-II algorithm identified that a H2/CO2 ratio of 5.0 yielded optimal conditions with high DME selectivity. At higher ratios, selectivity shifted towards MeOH, indicating increased separation costs. Lower temperatures favored MeOH production over DME.
期刊介绍:
The journal Energy Conversion and Management provides a forum for publishing original contributions and comprehensive technical review articles of interdisciplinary and original research on all important energy topics.
The topics considered include energy generation, utilization, conversion, storage, transmission, conservation, management and sustainability. These topics typically involve various types of energy such as mechanical, thermal, nuclear, chemical, electromagnetic, magnetic and electric. These energy types cover all known energy resources, including renewable resources (e.g., solar, bio, hydro, wind, geothermal and ocean energy), fossil fuels and nuclear resources.