Abstract
The mathematical modeling of enzymatic reactors for esterification of fatty acids with sugars in the production of biosurfactants has been a useful tool for studying and optimizing the process. In particular, artificial neural networks and fuzzy systems emerge as promising methods for developing models for those processes. In this work, regarding artificial neural networks application, coupling of networks to reactor mass balances was considered in hybrid models to infer reactant concentrations over time. Computationally, an algorithm was constructed incorporating material balances, neural reaction rates, and step-by-step numerical integration (employing the classical Runge-Kutta method). Besides, based on an available set of experimental data, fuzzy logic was applied for modeling and optimization of the conversion of esterification as a function of operational process parameters (such as time, temperature and molar ratio of substrates). All computational development was carried out using the Matlab software. The neural networks were able to predict the kinetic behavior of xylose esterification process by applying them to reactor mass balances, obtaining R2 values above 0.94, indicating a good predictive capacity. The trained fuzzy models were able to simulate the relationships between input variables and the output variable, enabling the construction of surfaces and estimating the optimal operational condition at 60 h of reaction, 55°C, molar ratio of substrates of 5:1 and enzyme loading of 37.5 U/g. The same condition was obtained when applying the particle swarm optimization algorithm. Thus, this work presented a complete tool based on computational intelligence for modeling, simulation, and optimization of biosurfactant synthesis.