{"title":"Simulation and optimization of supported Ziegler–Natta catalyst preparation based on AI approach coupled with genetic algorithm","authors":"Seyed Amin Mirmohammadi , Amin Hedayati Moghaddam , Naeimeh Bahri-Laleh","doi":"10.1080/1023666X.2023.2206509","DOIUrl":null,"url":null,"abstract":"<div><p>In the present work we are aimed to use powerful and state of the art methodology to optimize catalyst synthesis procedure. In this way, the preparation process of supported Ziegler–Natta catalysts was simulated and optimized through artificial intelligence (AI) methodology coupled with genetic algorithm (GA). The yield of preparation process was investigated through assessing the catalyst activity. The effects of several variables including TiCl<sub>4</sub> injection temperature, TiCl<sub>4</sub>/toluene ratio, and TiCl<sub>4</sub> injection time on the activity of prepared catalyst were investigated. In model development, leave-one-out technique was used for training the network. The developed neural network model can be utilized to enhance the efficiency of the catalyst preparation process.</p></div>","PeriodicalId":14236,"journal":{"name":"International Journal of Polymer Analysis and Characterization","volume":"28 3","pages":"Pages 269-278"},"PeriodicalIF":1.7000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Polymer Analysis and Characterization","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S1023666X23000057","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"POLYMER SCIENCE","Score":null,"Total":0}
引用次数: 1
Abstract
In the present work we are aimed to use powerful and state of the art methodology to optimize catalyst synthesis procedure. In this way, the preparation process of supported Ziegler–Natta catalysts was simulated and optimized through artificial intelligence (AI) methodology coupled with genetic algorithm (GA). The yield of preparation process was investigated through assessing the catalyst activity. The effects of several variables including TiCl4 injection temperature, TiCl4/toluene ratio, and TiCl4 injection time on the activity of prepared catalyst were investigated. In model development, leave-one-out technique was used for training the network. The developed neural network model can be utilized to enhance the efficiency of the catalyst preparation process.
期刊介绍：
The scope of the journal is to publish original contributions and reviews on studies, methodologies, instrumentation, and applications involving the analysis and characterization of polymers and polymeric-based materials, including synthetic polymers, blends, composites, fibers, coatings, supramolecular structures, polysaccharides, and biopolymers. The Journal will accept papers and review articles on the following topics and research areas involving fundamental and applied studies of polymer analysis and characterization:
Characterization and analysis of new and existing polymers and polymeric-based materials.
Design and evaluation of analytical instrumentation and physical testing equipment.
Determination of molecular weight, size, conformation, branching, cross-linking, chemical structure, and sequence distribution.
Using separation, spectroscopic, and scattering techniques.
Surface characterization of polymeric materials.
Measurement of solution and bulk properties and behavior of polymers.
Studies involving structure-property-processing relationships, and polymer aging.
Analysis of oligomeric materials.
Analysis of polymer additives and decomposition products.
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