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Inverse design of chemoenzymatic epoxidation of soyabean oil through artificial intelligence-driven experimental approach.
Sarmah, Nipon; Mehtab, Vazida; Borah, Kashmiri; Palanisamy, Aruna; Parthasarathy, Rajarathinam; Chenna, Sumana.
Afiliación
  • Sarmah N; Chemical Engineering & Process Technology, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India; Department of Chemical and Environmental Engineering, School of Engineering, RMIT University, Melbourn
  • Mehtab V; Chemical Engineering & Process Technology, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
  • Borah K; Polymers & Functional Materials, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
  • Palanisamy A; Polymers & Functional Materials, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India.
  • Parthasarathy R; Department of Chemical and Environmental Engineering, School of Engineering, RMIT University, Melbourne VIC - 3001, Australia.
  • Chenna S; Chemical Engineering & Process Technology, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India. Electronic address: sumana@iict.res.in.
Bioresour Technol ; 412: 131405, 2024 Nov.
Article en En | MEDLINE | ID: mdl-39222857
ABSTRACT
This paper presents an inverse design methodology that utilizes artificial intelligence (AI)-driven experiments to optimize the chemoenzymatic epoxidation of soyabean oil using hydrogen peroxide and lipase (Novozym 435). First, experiments are conducted using a systematic 3-level, 5-factor Box-Behnken design to explore the effect of input parameters on oxirane oxygen content (OOC (%)). Based on these experiments, various AI models are trained, with the support vector regression (SVR) model being found to be the most accurate. SVR is then used as a fitness function in particle swarm optimization, and the suggested optimal conditions, upon experimental validation, resulted in a maximum OOC of 7.19 % (∼98.5 % relative conversion of oil to epoxy). The results demonstrate the superiority of the proposed approach over existing methods. This framework offers a general intensified process optimization strategy with minimal resource utilization that can be applied to any other process.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Compuestos Epoxi / Lipasa Idioma: En Revista: Bioresour Technol Asunto de la revista: ENGENHARIA BIOMEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Compuestos Epoxi / Lipasa Idioma: En Revista: Bioresour Technol Asunto de la revista: ENGENHARIA BIOMEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido