[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126540-en":3,"doc-seo-126540-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126540,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Optimal 6E Design of an Integrated Solar Energy-driven Polygeneration and CO2 Capture System - A Machine Learning Approach","Renewable energy-driven decentralized polygeneration systems offer strong potential for climate change mitigation and sustainable development. This study presents a machine learning-based multi-objective optimization method for an integrated solar energy-driven polygeneration and CO2 capture system to satisfy a greenhouse’s power, freshwater, and CO2 needs. The solar-assisted configuration includes a 486-kW gas turbine, multiple expansion units, HDH desalination using waste heat, and post-combustion CO2 capture, modeled via dynamic MATLAB simulation. Genetic Programming and Artificial Neural Networks optimize cost and environmental metrics while maximizing exergy efficiency and freshwater production. SixE analyses and sensitivity study evaluate performance impacts, yielding notable reductions versus prior indicators under three- and four-objective formulations.","Journal Pre-proofs  \nOptimal 6E Design of an Integrated Solar Energy-driven Polygeneration and CO2 Capture System: A Machine Learning Approach  \nNastaran Khani, Mohammad H. Khoshgoftar Manesh, Viviani C. Onishi  \nPII: S2451-9049(23)00022-7  \nDOI: [https://doi.org/10.1016/j.tsep.2023.101669](https://doi.org/10.1016/j.tsep.2023.101669)  \nReference: TSEP 101669  \nTo appear in: Thermal Science and Engineering Progress  \nReceived Date: 16 September 2022  \nRevised Date: 14 December 2022  \nAccepted Date: 12 January 2023  \nPlease cite this article as: N. Khani, M.H. Khoshgoftar Manesh, V.C. Onishi, Optimal 6E Design of an Integrated Solar Energy-driven Polygeneration and CO2 Capture System: A Machine Learning Approach, Thermal Science  \nand Engineering Progress (2023), doi: [https://doi.org/10.1016/j.tsep.2023.101669](https://doi.org/10.1016/j.tsep.2023.101669)  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2023 The Author(s). Published by Elsevier Ltd.  \nOptimal 6E Design of an Integrated Solar Energy-driven Polygeneration and CO2 Capture System: A Machine Learning Approach  \nNastaran Khani a, Mohammad H. Khoshgoftar Manesh a, Viviani C. Onishi b, *  \na Energy, Environmental and Biological Systems Research Lab (EEBRlab), Division of Thermal Sciences and Energy  \nSystems, Department of Mechanical Engineering, Faculty of Technology & Engineering, University of Qom, Qom,  \nIran; [Nastaran.Khani@ymail.com](Nastaran.Khani@ymail.com), [M.Khoshgoftar@qom.ac.ir](M.Khoshgoftar@qom.ac.ir)  \nb School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Merchiston Campus,  \n10 Colinton Road, Edinburgh EH10 5DT, UK; [V.Onishi@napier.ac.uk](V.Onishi@napier.ac.uk)  \n* Corresponding author: [V.Onishi@napier.ac.uk](V.Onishi@napier.ac.uk) (V. C. Onishi), Edinburgh Napier University, UK.  \nABSTRACT  \nRenewable energy-driven decentralized polygeneration systems herald great potential in tackling  \nclimate change issues and promoting sustainable development. In this light, this study introduces  \na new machine learning-based multi-objective optimization approach of an integrated solar  \nenergy-driven polygeneration and CO2 capture system for meeting a greenhouse’s power,  \nfreshwater, and CO2 demands. The integrated solar-assisted polygeneration system comprises a  \n486-kW gas turbine, two steam turbines, two organic Rankine cycles, a humidification  \ndehumidification desalination unit to recover waste heat while producing freshwater, and a post  \ncombustion CO2 capture unit. The proposed system is mathematically modelled and evaluated via  \na dynamic simulation approach implemented in MATLAB software. Moreover, sensitivity  \nanalysis is conducted to identify the most influential decision variables on the system performance.  \nThe machine learning-based multi-objective optimization strategy combines Genetic Programming (GP) and Artificial Neural Networks (ANN) to minimize total costs, environmental impacts, and economic and environmental emergy rates whilst maximizing the system exergy  \nefficiency and freshwater production. Finally, the system performance is further investigated through comprehensive Energy, Exergy, Exergoeconomic, Exergoenvironmental, Emergoeconomic, and Emergoenvironmental (6E) analyses. The three-objective optimization of the integrated system reduces total costs, environmental impacts, and monthly environmental emergy rate by 11.4%, 34.31% and 6.38%, respectively. Furthermore, red","cbCaiojIXTgaOl8i","https://ap.wps.com/l/cbCaiojIXTgaOl8i","pdf",3934577,2,1,78,"English","en",105,"# Abstract\n## System design and modeling\n## Multi-objective optimization via machine learning\n## Sensitivity analysis\n## 6E performance assessment","[{\"question\":\"What system does the study optimize?\",\"answer\":\"It optimizes an integrated solar energy-driven polygeneration and post-combustion CO2 capture system that supplies greenhouse power, freshwater, and CO2 needs. The setup includes gas turbine and steam turbines, waste-heat-driven HDH desalination, and a CO2 capture unit.\"},{\"question\":\"How are the optimization objectives handled?\",\"answer\":\"The approach uses Genetic Programming and Artificial Neural Networks to perform multi-objective optimization. It minimizes total costs, environmental impacts, and environmental/economic emergy-related rates while maximizing system exergy efficiency and freshwater production.\"},{\"question\":\"Which methods evaluate system performance results?\",\"answer\":\"The paper relies on dynamic simulation implemented in MATLAB for modeling and evaluation. It further conducts sensitivity analysis and comprehensive 6E (Energy, Exergy, Exergoeconomic, Exergoenvironmental, Emergoeconomic, Emergoenvironmental) analyses.\"}]","Optimal 6E Design of an Integrated Solar Energy-driven Polygeneration and CO2 Capture System - A Machine Learning Approach | PDF",1785933220,197,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"optimal-6e-design-of-an-integrated-solar-energy-driven-polygeneration-and-co2-capture-system-a-machine-learning-approach","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/optimal-6e-design-of-an-integrated-solar-energy-driven-polygeneration-and-co2-capture-system-a-machine-learning-approach/126540/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What system does the study optimize?","Question",{"text":76,"@type":77},"It optimizes an integrated solar energy-driven polygeneration and post-combustion CO2 capture system that supplies greenhouse power, freshwater, and CO2 needs. The setup includes gas turbine and steam turbines, waste-heat-driven HDH desalination, and a CO2 capture unit.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are the optimization objectives handled?",{"text":81,"@type":77},"The approach uses Genetic Programming and Artificial Neural Networks to perform multi-objective optimization. It minimizes total costs, environmental impacts, and environmental/economic emergy-related rates while maximizing system exergy efficiency and freshwater production.",{"name":83,"@type":74,"acceptedAnswer":84},"Which methods evaluate system performance results?",{"text":85,"@type":77},"The paper relies on dynamic simulation implemented in MATLAB for modeling and evaluation. 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