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

Abstract 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.

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Enzymatic Degradation of 2,4,6-Trichlorophenol in a Microreactor using Soybean Peroxidase

Symmetry ◽

10.3390/sym12071129 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1129

Author(s):

Rodrigo A. Costa ◽

Alexandre S. Cunha ◽

José Carlos G. Peres ◽

Adriano R. Azzoni ◽

Enzo Laurenti ◽

...

Keyword(s):

Experimental Data ◽

Enzymatic Degradation ◽

Mechanistic Model ◽

Soybean Seed ◽

Secondary Flows ◽

Least Square ◽

Raw Material ◽

Square Function ◽

Soybean Peroxidase ◽

Data Set

Soybean peroxidase is an enzyme extracted from soybean seed hulls. In the presence of hydrogen peroxide, the enzyme has the potential to catalyze the biodegradation of toxic substances like chlorophenols. For this reason, its use in wastewater treatment processes is environmentally friendly since the enzyme can be obtained from a renewable and abundant raw material. In this work, enzymatic biodegradation of 2,4,6-trichlorophenol performed by soybean peroxidase in a microreactor was studied experimentally and theoretically. The experimental data set was obtained with a volume of 250 μL by using different soybean peroxidase concentrations and different reaction times. The fluid dynamics of the microreactor was modeled as well, using ANSYS CFX. The simulations exhibited secondary flows, which enhanced mixing. Although the laminar flow was developed, it can be assumed to be a well-mixed medium. The kinetic data were evaluated through a mechanistic model, the modified bi-bi ping-pong model, which is adequate to represent the enzymatic degradation using peroxidases. The model was composed of an initial value problem for ordinary differential equations that were solved using MATLAB. Some kinetic constants were estimated using the least square function. The results of the model fit well the experimental data.

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Calibration of stormwater quality regression models: a random process?

Water Science & Technology ◽

10.2166/wst.2010.324 ◽

2010 ◽

Vol 62 (4) ◽

pp. 875-882 ◽

Cited By ~ 4

Author(s):

A. Dembélé ◽

J.-L. Bertrand-Krajewski ◽

B. Barillon

Keyword(s):

Experimental Data ◽

Least Squares ◽

Regression Models ◽

Linear Models ◽

Least Squares Method ◽

Weighted Least Squares ◽

Ordinary Least Squares ◽

Data Sets ◽

Data Set ◽

Urban Catchments

Regression models are among the most frequently used models to estimate pollutants event mean concentrations (EMC) in wet weather discharges in urban catchments. Two main questions dealing with the calibration of EMC regression models are investigated: i) the sensitivity of models to the size and the content of data sets used for their calibration, ii) the change of modelling results when models are re-calibrated when data sets grow and change with time when new experimental data are collected. Based on an experimental data set of 64 rain events monitored in a densely urbanised catchment, four TSS EMC regression models (two log-linear and two linear models) with two or three explanatory variables have been derived and analysed. Model calibration with the iterative re-weighted least squares method is less sensitive and leads to more robust results than the ordinary least squares method. Three calibration options have been investigated: two options accounting for the chronological order of the observations, one option using random samples of events from the whole available data set. Results obtained with the best performing non linear model clearly indicate that the model is highly sensitive to the size and the content of the data set used for its calibration.

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Application of Neural Networks To Simulate the Growth Profile of Lactic Acid Bacteria in Green Olive Fermentation

Journal of Food Protection ◽

10.4315/0362-028x-70.8.1909 ◽

2007 ◽

Vol 70 (8) ◽

pp. 1909-1916 ◽

Cited By ~ 5

Author(s):

EFSTATHIOS Z. PANAGOU ◽

CHRYSOULA C. TASSOU ◽

ELEFTHERIOS K. A. SARAVANOS ◽

GEORGE-JOHN E. NYCHAS

Keyword(s):

Experimental Data ◽

Lactic Acid ◽

Lactic Acid Bacteria ◽

Multilayer Perceptron ◽

Coefficient Of Determination ◽

Percentage Error ◽

Data Set ◽

Growth Profile ◽

Multilayer Perceptron Network ◽

Green Olive

The growth profile of five strains of lactic acid bacteria (Lactobacillus plantarum ACA-DC 287, L. plantarum ACA-DC 146, Lactobacillus paracasei ACA-DC 4037, Lactobacillus sakei LQC 1378, and Leuconostoc mesenteroides LQC 1398) was investigated in controlled fermentation of cv. Conservolea green olives with a multilayer perceptron network, a combined logistic-Fermi function, and a two-term Gompertz function. Neural network training was based on the steepest-descent gradient learning algorithm. Model performance was compared with the experimental data with five statistical indices, namely coefficient of determination (R2), root mean square error (RMSE), mean relative percentage error (MRPE), mean absolute percentage error (MAPE), and standard error of prediction (SEP). The experimental data set consisted of 125 counts (CFU per milliliter) of lactic acid bacteria during the green olive fermentation process for up to 38 days (5 strains × 25 sampling days). For model development, a standard methodology was followed, dividing the data set into training (120 data) and validation (25 data) subsets. Our results demonstrated that the developed network was able to model the growth and survival profile of all the strains of lactic acid bacteria during fermentation equally well with the statistical models. The performance indices for the training subset of the multilayer perceptron network were R2 = 0.987, RMSE = 0.097, MRPE = 0.069, MAPE = 0.933, and SEP = 1.316. The relevant mean values for the logistic-Fermi and two-term Gompertz functions were R2 = 0.981 and 0.989, RMSE = 0.109 and 0.083, MRPE = 0.026 and 0.030, MAPE = 1.430 and 1.076, and SEP = 1.490 and 1.127, respectively. For the validation subset, the network also gave good predictions (R2 = 0.968, RMSE = 0.149, MRPE = 0.100, MAPE = 1.411, and SEP = 2.009).

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