Nicolas Chaussard*, Clémence Nikitine and Pascal Fongarland,
{"title":"Intrinsic Kinetics Resolution of an Enantioselective Transesterification Catalyzed with the Immobilized Enzyme Novozym435","authors":"Nicolas Chaussard*, Clémence Nikitine and Pascal Fongarland, ","doi":"10.1021/acsengineeringau.4c0003010.1021/acsengineeringau.4c00030","DOIUrl":null,"url":null,"abstract":"<p >This work investigates the kinetics of the enantioselective transesterification of ethyl butyrate and (<i>R</i>)-2-pentanol in a solventless medium biocatalyzed by <i>Novozym435</i>, an immobilized <i>Candida antarctica</i> <i>Lipase B</i>. A reaction-diffusion reversible Ping-Pong bi-bi model was developed to represent the reaction rate with the additional estimation of the internal mass transfer using an orthogonal collocations method. A total of 18 experiments (774 data points) were realized in the SpinChem Vessel V2 batch reactor at a constant stirring speed of 400 rpm, varying temperatures (30–60 °C), component initial molar fraction (0.2–0.8), catalyst ratio (1–4% wt), and size fraction (200–1000 μm). Kinetics data were fitted using the model with a mean average percentage error of 3.45%, the 10 optimized kinetic parameters being coherent with the expected behavior of the Ping-Pong Michaelis–Menten mechanisms. Values for the effectiveness factor η for intraparticle mass transfer diffusion vary between 0.37 and 1, confirming the necessity to include mass transfer into kinetic modeling in our case.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"4 6","pages":"545–561 545–561"},"PeriodicalIF":4.3000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.4c00030","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Engineering Au","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsengineeringau.4c00030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
This work investigates the kinetics of the enantioselective transesterification of ethyl butyrate and (R)-2-pentanol in a solventless medium biocatalyzed by Novozym435, an immobilized Candida antarcticaLipase B. A reaction-diffusion reversible Ping-Pong bi-bi model was developed to represent the reaction rate with the additional estimation of the internal mass transfer using an orthogonal collocations method. A total of 18 experiments (774 data points) were realized in the SpinChem Vessel V2 batch reactor at a constant stirring speed of 400 rpm, varying temperatures (30–60 °C), component initial molar fraction (0.2–0.8), catalyst ratio (1–4% wt), and size fraction (200–1000 μm). Kinetics data were fitted using the model with a mean average percentage error of 3.45%, the 10 optimized kinetic parameters being coherent with the expected behavior of the Ping-Pong Michaelis–Menten mechanisms. Values for the effectiveness factor η for intraparticle mass transfer diffusion vary between 0.37 and 1, confirming the necessity to include mass transfer into kinetic modeling in our case.
期刊介绍:
)ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)