Intrinsic Kinetics Resolution of an Enantioselective Transesterification Catalyzed with the Immobilized Enzyme Novozym435

Nicolas Chaussard*, Clémence Nikitine and Pascal Fongarland, 
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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 antarctica Lipase 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.

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固定化酶Novozym435催化对映选择性酯交换反应的本征动力学分析
本文研究了固定化南极假丝酵母脂肪酶b Novozym435在无溶剂介质中催化丁酸乙酯和(R)-2-戊醇的对映选择性酯交换动力学。采用正交配位法,建立了反应扩散可逆的“a - bet - bi-bi”模型来表示反应速率,并对内部传质进行了附加估计。在SpinChem Vessel V2间歇式反应器中,在400 rpm的恒定搅拌速度、30-60℃的温度、组分初始摩尔分数(0.2-0.8)、催化剂比(1-4% wt)和粒径分数(200-1000 μm)条件下,完成了18个实验(774个数据点)。采用该模型拟合动力学数据,平均误差为3.45%,优化后的10个动力学参数与乒乓Michaelis-Menten机制的预期行为一致。颗粒内传质扩散的有效因子η值在0.37到1之间变化,这证实了在我们的情况下将传质纳入动力学模型的必要性。
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ACS Engineering Au
ACS Engineering Au 化学工程技术-
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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)
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