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Kinetic modeling of lipase‐catalyzed esterification reaction between oleic acid and trimethylolpropane: A simplified model for multi‐substrate multi‐product ping–pong mechanisms
Author(s) -
Bornadel Amin,
Åkerman Cecilia Orellana,
Adlercreutz Patrick,
HattiKaul Rajni,
Borg Niklas
Publication year - 2013
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1002/btpr.1806
Subject(s) - trimethylolpropane , ping pong , substrate (aquarium) , chemistry , lipase , catalysis , candida antarctica , product inhibition , oleic acid , reaction mechanism , organic chemistry , computer science , enzyme , simulation , biochemistry , non competitive inhibition , human arm , oceanography , polyurethane , geology
Kinetic models are among the tools that can be used for optimization of biocatalytic reactions as well as for facilitating process design and upscaling in order to improve productivity and economy of these processes. Mechanism pathways for multi‐substrate multi‐product enzyme‐catalyzed reactions can become very complex and lead to kinetic models comprising several tens of terms. Hence the models comprise too many parameters, which are in general highly correlated and their estimations are often prone to huge errors. In this study, Novozym ® 435 catalyzed esterification reaction between oleic acid (OA) and trimethylolpropane (TMP) with continuous removal of side‐product (water) was carried out as an example for reactions that follow multi‐substrate multi‐product ping‐pong mechanisms. A kinetic model was developed based on a simplified ping‐pong mechanism proposed for the reaction. The model considered both enzymatic and spontaneous reactions involved and also the effect of product removal during the reaction. The kinetic model parameters were estimated using nonlinear curve fitting through unconstrained optimization methodology and the model was verified by using empirical data from different experiments and showed good predictability of the reaction under different conditions. This approach can be applied to similar biocatalytic processes to facilitate their optimization and design. © 2013 American Institute of Chemical Engineers Biotechnol. Prog ., 29:1422–1429, 2013