Abstract
In this work, the mechanism of soybean oil transesterification reaction was investigated by Particle Swarm Optimization (PSO), and the reaction progress was monitored by Artificial Neural Networks (ANN) using Self-Organizing Maps (SOM). The PSO was used to adjust the values of the reaction rate constants (kn) in the four proposed mechanisms/models approaches. The mean error values obtained by the PSO method showed a good agreement between the experimental and simulated data for all evaluated approaches, ranging from 4.3 to 8.5%. The results suggest that under the evaluated conditions reverse reactions may be disregarded, following a second-order kinetic model for the three stepwise reactions. The SOM proved to be an efficient tool for exploring data obtained during the transesterification process, allowed to organize the data in the form of clusters presenting in the weight maps the relationship between the monitored variables during the progress of the transesterification reaction. The PSO and ANN type SOM has been shown efficient tools for investigating the kinetics of the transesterification reaction, and they offer good perspectives for further kinetic studies of other types of industrial processes.
Published Version
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