[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290481-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-290481-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","design-of-the-ethylbenzene-production-process-using-machine-learning","Design of the Ethylbenzene production process using machine learning","","Ethylbenzene (EB) serves as a key feedstock for producing styrene monomer, while demand is growing and EB is increasingly produced using zeolite-catalyzed processes. The separation step imposes a high energy load and increases fuel consumption, motivating the search for lower energy intensity designs. The study designs an EB process with reduced fuel energy per unit output by first optimizing the process structure and then the design variables using machine learning. The proposed approach replaces labor-intensive simulation-based optimization, delivering low fuel energy intensity while satisfying multiple reaction conditions.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/design-of-the-ethylbenzene-production-process-using-machine-learning/290481/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/design-of-the-ethylbenzene-production-process-using-machine-learning/290481.png","ImageObject",300,407,{"name":42,"@type":43},"Maeve","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-04","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",8,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why is energy consumption a key issue in the current ethylbenzene production process?","Question",{"text":63,"@type":64},"In zeolite-catalyzed EB production, a large amount of unreacted benzene must be recycled via separation. The separation step has a high energy load, leading to high fuel consumption.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the proposed method optimize the EB production process?",{"text":68,"@type":64},"The method first optimizes the process structure and then optimizes design variables. Machine learning is used to efficiently search and optimize design variables rather than relying on repeated, labor-intensive simulations.",{"name":70,"@type":61,"acceptedAnswer":71},"What improvements does machine learning enable compared with conventional process design?",{"text":72,"@type":64},"Conventional design optimizes design variables through extensive simulations based on chemical engineering knowledge, which is time- and labor-intensive. By selecting promising variables from many candidates, machine learning automates the optimization loop while maintaining multiple reaction conditions and achieving lower fuel energy intensity.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290481,1789641793,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":55,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":107,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":80,"read_time":143,"preview_page_count":4},5909877438554,"https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272","Design of the Ethylbenzene production process using machine learning  \n\n| メタデータ | 言語: English\u003Cbr>出版者: Elsevier\u003Cbr>公開日: 2025-07-30\u003Cbr>キーワード(Ja):\u003Cbr>キーワード(En): Process design, Ethylbenzene, Superstructure, Machine learning, Process informatics, Design of experiments, Bayesian optimization\u003Cbr>作成者: Ishikawa,Eri, Kaneko,Hiromasa\u003Cbr>メールアドレス:\u003Cbr>所属: |\n| --- | --- |\n| URL | [http://hdl.handle.net/10291/0002001865](http://hdl.handle.net/10291/0002001865) |\n\nThis work is licensed under a Creative Commons Attribution 4.0 International License.  \nCase Studies in Chemical and Environmental Engineering 11 (2025) 101157  \nContents lists available at ScienceDirect  \nCase Studies in Chemical and Environmental Engineering  \njournal [homepage:](homepage: www.sciencedirect.com/journal/case-studies-in-chemical)[ www.sciencedirect.com/journal/case-studies-in-chemical](homepage: www.sciencedirect.com/journal/case-studies-in-chemical)and-environmental-engineering  \n| Case Report\u003Cbr>Design of the Ethylbenzene production process using machine learning\u003Cbr>*\u003Cbr>Eri Ishikawa , Hiromasa Kaneko \u003Cbr>Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 214-8571, Japan |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Process design Ethylbenzene Superstructure Machine learning Process informatics Design of experiments\u003Cbr>Bayesian optimization |  | Ethylbenzene (EB) is a raw material used to produce the styrene monomer, and the EB market size has been increasing. Recently, EB has been produced by a production process using zeolite catalysts. However, the fuel consumption in this process is an issue because the energy load in the separation process is high. The objective of this study was to design an EB production process with low energy consumption per unit production. In the proposed method, the process structure is first optimized, followed by optimization of the design variables. In the conventional process design, the design variables are optimized by calculations based on knowledge of the chemical engineering and repeated simulations, but it is labor and time intensive. The proposed method uses machine learning to efficiently optimize the design variables. By using machine learning, we achieved the design of a process with a low fuel energy intensity while satisfying multiple reaction conditions. |\n\n1. Introduction  \nEthylbenzene (EB) is a raw material used to produce the styrene monomer, which is mainly used for plastics. The market size of EB was 47,000,000 metric tons in 2014 [1]. When commercial production ofEB first began, EB was produced by a process using aluminum chloride catalysts. This process was replaced by a zeolite-catalyzed liquid-phase EB production process owing to its superiority in terms of operation and environmental friendliness [2].  \nIn the liquid-phase EB production process, a large amount ofunreacted benzene from the reaction system must be recycled by separation, but the energy load in the separation process is high and consumes much energy [3]. Various studies have been performed to achieve efficient EB production with less energy. Gerzeliev et al. [4] explored zeolite catalysts with high activity and EB selectivity and demonstrated their usefulness not only on a laboratory scale, but also in a reactor of the same scale as the actual commercial process. Ulyev et al. [5] reduced the steam consumption by thermal integration based on pinch analysis, resulting in lower fuel consumption and carbon dioxide emissions. Lei et al. [6] incorporated extractive distillation with ionic solvents in the distillation separation part of the EB production process, resulting inefficient separation and reduced energy consumption.  \nThe first step in the process design is to determine the equipment tobe used and its coupling relationship, or process structure, from the raw materials and product specifications. For example, Uebbing","cbCait7W4mSD2r7k","https://ap.wps.com/l/cbCait7W4mSD2r7k","pdf",2958421,"English","# Introduction\n# Background and motivation\n## Energy-intensive separation in EB production\n## Prior studies and optimization approaches\n# Proposed machine-learning-based process design\n## Structure optimization and variable optimization\n## Advantages over simulation-heavy workflows","[{\"question\":\"Why is energy consumption a key issue in the current ethylbenzene production process?\",\"answer\":\"In zeolite-catalyzed EB production, a large amount of unreacted benzene must be recycled via separation. The separation step has a high energy load, leading to high fuel consumption.\"},{\"question\":\"How does the proposed method optimize the EB production process?\",\"answer\":\"The method first optimizes the process structure and then optimizes design variables. Machine learning is used to efficiently search and optimize design variables rather than relying on repeated, labor-intensive simulations.\"},{\"question\":\"What improvements does machine learning enable compared with conventional process design?\",\"answer\":\"Conventional design optimizes design variables through extensive simulations based on chemical engineering knowledge, which is time- and labor-intensive. By selecting promising variables from many candidates, machine learning automates the optimization loop while maintaining multiple reaction conditions and achieving lower fuel energy intensity.\"}]","Design of the Ethylbenzene production process using machine learning | PDF",18]