[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118512-en":3,"doc-seo-118512-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118512,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Application of Machine Learning in Chemical Engineering - Outlook and Perspectives","Chemical engineering relies heavily on mathematical models for formulation, development, and operational stance, yet physical and economic impacts of poor modeling can be severe. While hybrid artificial intelligence approaches have been explored, progress has accelerated with new data sources, indices, interface designs, and machine learning algorithms. Machine learning is especially promising for time-critical applications such as real-time optimization and planning requiring high precision. Despite concerns about engineers’ limited exposure to computer science and data analysis, machine learning is expected to become a dependable part of engineers’ modeling toolbox.","Application of machine learning in chemical engineering:  \noutlook and perspectives  \nAshraf Al Sharah1, Hamza Abu Owida2, Feras Alnaimat2, Mohammad Hassan3, Suhaila Abuowaida4  \nMohammad Alhaj5, Ahmad Sharadqeh1  \n1Department of Electrical Engineering, College of Engineering Technology, Al-Balqa Applied University, Amman, Jordan 2Department of Medical Engineering, Faculty of Engineering, Al-Ahliyya Amman University, Amman, Jordan 3Department of Communications and Computer Engineering, Faculty of Engineering, Al-Ahliyya Amman University, Amman, Jordan 4Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, Jordan  \n5IEEE Member  \nArticle history:  \nReceived Apr 9, 2023 Revised Sep 23, 2023 Accepted Nov 6, 2023  \nKeywords:  \nApplications Chemical engineering Machine learning Models Optimization  \nCorresponding Author:  \nChemical engineers' formulation, development, and stance processes all heavily rely on models. The physical and economic consequences of these decisions can have disastrous effects. Attempts to employ a hybrid form of artificial intelligence for modeling in various disciplines. However, they fell short of expectations. Due to a rise in the amount of data and computational resources during the previous five years. A lot of recent work has gone into developing new data sources, indexes, chemical interface designs, and machine learning algorithms in an effort to facilitate the adoption of these techniques in the research community. However, there are some important downsides to machine learning gains. The most promising uses for machine learning are in time-critical tasks like real-time optimization and planning that require extreme precision and can build on models that can self-learn to recognize patterns, draw conclusions from data, and become more intelligent over time. Due to their limited exposure to computer science and data analysis, the majority of chemical engineers are potentially vulnerable to the development of artificial intelligence. But in the not-too-distant future, chemical engineers' modeling toolbox will include a reliable machine learning component.  \nThis is an open access article under the CC BY-SA license.  \nAshraf Al Sharah  \nDepartment of Electrical Engineering, College of Engineering Technology, Al-Balqa Applied University Amman 001962, Jordan  \nEmail: [aalsharah@bau.edu.jo](aalsharah@bau.edu.jo)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nFor the past 130 years, mathematical modelling has been a crucial tool in chemical engineering, allowing engineers to quickly identify and design chemical processes [1], [2]. Keeping up with the ever-changing demands of today's world is harder than ever. No matter if you're trying to discover and synthesize active pharmaceutical ingredients to treat new diseases or increase process efficiency to conform to stricter environmental legislation, the ability to predict the outcomes of certain events is essential. The efficiency of a chemical interaction, the choice of a reactor, and the regulation of a heat source are all examples. Theories that have been refined over time of several centuries, one can make predictions [3]–[5] . Consequently, for reasonable processes, several of these models can somehow be modeled mathematically and necessitate a great deal of supercomputing capacity to solve numerical results. Because of this limitation, most engineers resort to more elementary models when attempting to explain the world around them. Prandtl's boundary layer model [6] is a notable example of a model from the  \npast that is still useful today. Scientists and engineers in the field of computational chemistry often compromise precision for the sake of efficiency. It is because of this openness that concentration structural functionalism has become so widely used in place of more advanced theoretical models.  \nOn the other hand, there are many scenarios where greater precision is preferred. Scientists and engineers in the fi","cbCairFUKy73U3jf","https://ap.wps.com/l/cbCairFUKy73U3jf","pdf",553613,1,12,"English","en",105,"# Introduction\n## Mathematical modeling in chemical engineering\n## Machine learning as statistical and mathematical models\n## Background and adoption of AI in the field","[{\"question\":\"Why are modeling tools so important in chemical engineering?\",\"answer\":\"Chemical engineers depend on models to predict the outcomes of events and to support decisions such as reactor choice and heat-source regulation. These predictions affect both efficiency and the economic and physical consequences of process development.\"},{\"question\":\"How does machine learning differ from traditional rule-based modeling?\",\"answer\":\"Machine learning models learn relationships from data without relying on predetermined rules. This allows patterns and conclusions to be extracted directly from data, enabling improved performance over time.\"},{\"question\":\"In what chemical engineering tasks is machine learning expected to be most valuable?\",\"answer\":\"The text highlights time-critical tasks such as real-time optimization and planning, where high precision is required and models can self-learn from data to become more intelligent over time.\"}]","Application of Machine Learning in Chemical Engineering - Outlook and Perspectives | PDF",1785683936,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"application-of-machine-learning-in-chemical-engineering-outlook-and-perspectives","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-in-chemical-engineering-outlook-and-perspectives/118512/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are modeling tools so important in chemical engineering?","Question",{"text":75,"@type":76},"Chemical engineers depend on models to predict the outcomes of events and to support decisions such as reactor choice and heat-source regulation. These predictions affect both efficiency and the economic and physical consequences of process development.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning differ from traditional rule-based modeling?",{"text":80,"@type":76},"Machine learning models learn relationships from data without relying on predetermined rules. This allows patterns and conclusions to be extracted directly from data, enabling improved performance over time.",{"name":82,"@type":73,"acceptedAnswer":83},"In what chemical engineering tasks is machine learning expected to be most valuable?",{"text":84,"@type":76},"The text highlights time-critical tasks such as real-time optimization and planning, where high precision is required and models can self-learn from data to become more intelligent over time.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]