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Engineering interventions in enzyme production: Lab to industrial scale

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dc.contributor.authorTarafdar, Ayon-
dc.contributor.authorSirohi, Ranjna-
dc.contributor.authorGaur, Vivek Kumar-
dc.contributor.authorKumar, Sunil-
dc.contributor.authorSharma, Poonam-
dc.contributor.authorVarjani, Sunita-
dc.contributor.authorPandey, Hari Om-
dc.contributor.authorSindhu, Raveendran-
dc.contributor.authorMadhavan, Aravind-
dc.contributor.authorRajasekharan, Reshmy-
dc.contributor.authorSim, Sang Jun-
dc.date.accessioned2021-08-30T02:47:06Z-
dc.date.available2021-08-30T02:47:06Z-
dc.date.created2021-06-19-
dc.date.issued2021-04-
dc.identifier.issn0960-8524-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/49446-
dc.description.abstractCommercial enzyme production has gained popularity due to its extensive applications in traditional and modern industrial sectors. Rigorous research activities are being conducted worldwide to make the enzyme production system more efficient, cost-effective and hence, sustainable. To overcome the lacunae in earlier enzyme production methods, new engineering interventions are being introduced to meet the growing demand for industrial enzymes. This review focuses initially on the current global scenario of the enzyme market followed by a discussion on different bioreactor design approaches. The use of novel membrane based, airlift and reciprocating plate bioreactors along with the emergence of micro-reactors have also been discussed. Further, the review covers different modelling and optimization strategies for the enzyme production process including advanced techniques like neural networks, adaptive neuro-fuzzy inference systems and genetic algorithms. Finally, the required thrust areas in the enzyme production sector have been highlighted with directions for future research.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER SCI LTD-
dc.titleEngineering interventions in enzyme production: Lab to industrial scale-
dc.typeArticle-
dc.contributor.affiliatedAuthorSim, Sang Jun-
dc.identifier.doi10.1016/j.biortech.2021.124771-
dc.identifier.scopusid2-s2.0-85100434472-
dc.identifier.wosid000620172900001-
dc.identifier.bibliographicCitationBIORESOURCE TECHNOLOGY, v.326-
dc.relation.isPartOfBIORESOURCE TECHNOLOGY-
dc.citation.titleBIORESOURCE TECHNOLOGY-
dc.citation.volume326-
dc.type.rimsART-
dc.type.docTypeReview-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaAgriculture-
dc.relation.journalResearchAreaBiotechnology & Applied Microbiology-
dc.relation.journalResearchAreaEnergy & Fuels-
dc.relation.journalWebOfScienceCategoryAgricultural Engineering-
dc.relation.journalWebOfScienceCategoryBiotechnology & Applied Microbiology-
dc.relation.journalWebOfScienceCategoryEnergy & Fuels-
dc.subject.keywordPlusSOLID-STATE FERMENTATION-
dc.subject.keywordPlusOPTIMIZATION-
dc.subject.keywordPlusBIOREACTOR-
dc.subject.keywordPlusHYDROLYSIS-
dc.subject.keywordPlusLACCASE-
dc.subject.keywordPlusDESIGN-
dc.subject.keywordPlusBED-
dc.subject.keywordAuthorBioreactors-
dc.subject.keywordAuthorMicroreactors-
dc.subject.keywordAuthorModeling-
dc.subject.keywordAuthorNeural network-
dc.subject.keywordAuthorGenetic algorithm-
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