Modeling and simulation of the enzymatic degradation of 2,4,6-trichlorophenol using soybean peroxidase

• Published in: Brazilian journal of chemical engineering Vol. 38; no. 4; pp. 719 - 730
• Main Authors: da Cunha, Alexandre Santuchi, dos S. Vianna Jr, Ardson, Laurenti, Enzo
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2021
• Subjects: Chemistry; Chemistry and Materials Science; Industrial Chemistry/Chemical Engineering; Original Paper; Enzymes; Soybean peroxidase; Enzymatic degradation; Kinetics
• ISSN: 0104-6632; 1678-4383
• DOI: 10.1007/s43153-021-00135-0

The enzymatic degradation of organic pollutants is a promising and ecological method for the remediation of industrial effluents. 2,4,6-Trichlorophenol is a major pollutant in many residual waters, and its consumption has been linked to lymphomas, leukemia, and liver cancer. The goal of the present work is to comprehend the enzymatic degradation of 2,4,6-trichlorophenol using soybean peroxidase. Different assumptions for the kinetic model were evaluated, and the simulations were compared to experimental data, which was obtained in a microreactor. The literature pointed out that the bi-bi ping-pong model represents well the kinetics of soybean peroxidase degradation. Since it is a complex model, some reactions can be considered or not. Six different possibilities for the model were considered, regarding different combinations of the generated enzyme forms that depend on the hypotheses for simplifying the model. The adjustment of the models was compared based on different metrics, such as the value of the objective function, coefficient of determination and root-mean-square error. The process modeling was obtained by the mass balance of all the reaction components, and all the simulations were performed in MATLAB R2015a. Reaction parameters were estimated based on the weighted least squares between the experimental data set and the values predicted by the model. The results showed that the data were better adjusted by the model that considers all the enzyme forms, including enzyme inactivation. Therefore, a better comprehension of the reaction mechanism was achieved, which allows a more precise reactor project and process simulation.