{"title":"用于发电、淡水和甲醇生产的气体-太阳能-生物废料多联产系统的最佳 4E 设计和创新 R 曲线方法","authors":"","doi":"10.1016/j.psep.2024.09.042","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Cogeneration power plants traditionally rely on fossil fuels to produce stable power and heat. However, increasing energy demand and population growth have intensified the emission of biological pollutants due to fossil fuel use. The Global Alliance on Health and Pollution advocates for integrating renewable energy sources to mitigate these issues.</p></div><div><h3>Objectives</h3><p>This study aims to evaluate the integration of a solar-biomass polygeneration system with a hybrid solar-waste-fossil fuel cogeneration system. The goal is to analyze the system from technical, economic, and environmental perspectives, focusing on optimizing energy demand and minimizing environmental impact.</p></div><div><h3>Methods</h3><p>To assess energy demand and supply, the R-curve methodology was applied to the hybrid cogeneration system, with a specific focus on solar and biomass renewable energies. Various scenarios were analyzed, including total annual costs, pollutant emissions, water footprint, and overall environmental impact based on life cycle assessment. The study examined and compared the performance of three types of biomass waste (Municipal solid waste, mixed paper waste, and date palm waste). Multi-objective optimization was performed using artificial intelligence and machine learning techniques, employing four meta-heuristic algorithms. The conditions generated by each algorithm were analyzed and compared.</p></div><div><h3>Results</h3><p>Municipal solid waste, being the most readily available fuel, provided the most favorable economic conditions for the system. Environmentally, municipal solid waste ranked in the middle compared to other fuels. Among the optimization algorithms, the Salps swarm algorithm proved to be the most efficient in terms of calculation time and system efficiency improvements. The optimization improved net power generation by 5.25 %, overall energy efficiency by 16.27 %, total cost rate by 10.19 %, and total environmental impact rate by 14.02 %.</p></div><div><h3>Conclusion</h3><p>The integrated system's performance was analyzed across different climatic change throughout the year. The multi-objective Salps swarm algorithm optimization demonstrated significant benefits in enhancing system efficiency and reducing costs and environmental impacts.</p></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":null,"pages":null},"PeriodicalIF":6.9000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal 4E design and innovative R-curve approach for a gas-solar- biological waste polygeneration system for power, freshwater, and methanol production\",\"authors\":\"\",\"doi\":\"10.1016/j.psep.2024.09.042\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Cogeneration power plants traditionally rely on fossil fuels to produce stable power and heat. However, increasing energy demand and population growth have intensified the emission of biological pollutants due to fossil fuel use. The Global Alliance on Health and Pollution advocates for integrating renewable energy sources to mitigate these issues.</p></div><div><h3>Objectives</h3><p>This study aims to evaluate the integration of a solar-biomass polygeneration system with a hybrid solar-waste-fossil fuel cogeneration system. The goal is to analyze the system from technical, economic, and environmental perspectives, focusing on optimizing energy demand and minimizing environmental impact.</p></div><div><h3>Methods</h3><p>To assess energy demand and supply, the R-curve methodology was applied to the hybrid cogeneration system, with a specific focus on solar and biomass renewable energies. Various scenarios were analyzed, including total annual costs, pollutant emissions, water footprint, and overall environmental impact based on life cycle assessment. The study examined and compared the performance of three types of biomass waste (Municipal solid waste, mixed paper waste, and date palm waste). Multi-objective optimization was performed using artificial intelligence and machine learning techniques, employing four meta-heuristic algorithms. The conditions generated by each algorithm were analyzed and compared.</p></div><div><h3>Results</h3><p>Municipal solid waste, being the most readily available fuel, provided the most favorable economic conditions for the system. Environmentally, municipal solid waste ranked in the middle compared to other fuels. Among the optimization algorithms, the Salps swarm algorithm proved to be the most efficient in terms of calculation time and system efficiency improvements. The optimization improved net power generation by 5.25 %, overall energy efficiency by 16.27 %, total cost rate by 10.19 %, and total environmental impact rate by 14.02 %.</p></div><div><h3>Conclusion</h3><p>The integrated system's performance was analyzed across different climatic change throughout the year. The multi-objective Salps swarm algorithm optimization demonstrated significant benefits in enhancing system efficiency and reducing costs and environmental impacts.</p></div>\",\"PeriodicalId\":20743,\"journal\":{\"name\":\"Process Safety and Environmental Protection\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Process Safety and Environmental Protection\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0957582024011741\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Process Safety and Environmental Protection","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957582024011741","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
摘要
背景热电联产发电厂传统上依靠化石燃料生产稳定的电力和热能。然而,日益增长的能源需求和人口增长加剧了化石燃料使用所导致的生物污染物排放。全球健康与污染联盟提倡整合可再生能源,以缓解这些问题。本研究旨在评估太阳能-生物质多联产系统与太阳能-废物-化石燃料混合热电联产系统的整合情况。方法为了评估能源需求和供应情况,对混合热电联产系统采用了 R 曲线方法,重点关注太阳能和生物质可再生能源。对各种方案进行了分析,包括年度总成本、污染物排放、水足迹以及基于生命周期评估的总体环境影响。研究考察并比较了三种生物质废物(城市固体废物、混合废纸和椰枣废料)的性能。利用人工智能和机器学习技术,采用四种元启发式算法进行了多目标优化。结果城市固体废物是最容易获得的燃料,为系统提供了最有利的经济条件。在环境方面,城市固体废物与其他燃料相比处于中间位置。在各种优化算法中,Salps 蜂群算法在计算时间和提高系统效率方面被证明是最有效的。通过优化,净发电量提高了 5.25%,总体能源效率提高了 16.27%,总成本率提高了 10.19%,总环境影响率提高了 14.02%。多目标萨尔普斯蜂群算法优化在提高系统效率、降低成本和环境影响方面取得了显著成效。
Optimal 4E design and innovative R-curve approach for a gas-solar- biological waste polygeneration system for power, freshwater, and methanol production
Background
Cogeneration power plants traditionally rely on fossil fuels to produce stable power and heat. However, increasing energy demand and population growth have intensified the emission of biological pollutants due to fossil fuel use. The Global Alliance on Health and Pollution advocates for integrating renewable energy sources to mitigate these issues.
Objectives
This study aims to evaluate the integration of a solar-biomass polygeneration system with a hybrid solar-waste-fossil fuel cogeneration system. The goal is to analyze the system from technical, economic, and environmental perspectives, focusing on optimizing energy demand and minimizing environmental impact.
Methods
To assess energy demand and supply, the R-curve methodology was applied to the hybrid cogeneration system, with a specific focus on solar and biomass renewable energies. Various scenarios were analyzed, including total annual costs, pollutant emissions, water footprint, and overall environmental impact based on life cycle assessment. The study examined and compared the performance of three types of biomass waste (Municipal solid waste, mixed paper waste, and date palm waste). Multi-objective optimization was performed using artificial intelligence and machine learning techniques, employing four meta-heuristic algorithms. The conditions generated by each algorithm were analyzed and compared.
Results
Municipal solid waste, being the most readily available fuel, provided the most favorable economic conditions for the system. Environmentally, municipal solid waste ranked in the middle compared to other fuels. Among the optimization algorithms, the Salps swarm algorithm proved to be the most efficient in terms of calculation time and system efficiency improvements. The optimization improved net power generation by 5.25 %, overall energy efficiency by 16.27 %, total cost rate by 10.19 %, and total environmental impact rate by 14.02 %.
Conclusion
The integrated system's performance was analyzed across different climatic change throughout the year. The multi-objective Salps swarm algorithm optimization demonstrated significant benefits in enhancing system efficiency and reducing costs and environmental impacts.
期刊介绍:
The Process Safety and Environmental Protection (PSEP) journal is a leading international publication that focuses on the publication of high-quality, original research papers in the field of engineering, specifically those related to the safety of industrial processes and environmental protection. The journal encourages submissions that present new developments in safety and environmental aspects, particularly those that show how research findings can be applied in process engineering design and practice.
PSEP is particularly interested in research that brings fresh perspectives to established engineering principles, identifies unsolved problems, or suggests directions for future research. The journal also values contributions that push the boundaries of traditional engineering and welcomes multidisciplinary papers.
PSEP's articles are abstracted and indexed by a range of databases and services, which helps to ensure that the journal's research is accessible and recognized in the academic and professional communities. These databases include ANTE, Chemical Abstracts, Chemical Hazards in Industry, Current Contents, Elsevier Engineering Information database, Pascal Francis, Web of Science, Scopus, Engineering Information Database EnCompass LIT (Elsevier), and INSPEC. This wide coverage facilitates the dissemination of the journal's content to a global audience interested in process safety and environmental engineering.