Review of the Approach to Modelling Pesticides Dispersion in Environment for Determining the Concentrations to Which Organisms are Exposed as Part of Risk Assessment

There is an interest in the toxicity of pesticides in plant protection treatments for humans and the environment. As such, assessing toxicity risk is essential. Risk assessment is constrained due to the large amount of data to be measured, short collection times, insufficient data even when available, and the absence of bioaccumulation of the pollutant in the target organism. Modelling becomes an ally in overcoming these shortcomings. The assessor thus has at his disposal statistical, compartmental, Gaussian, Lagrangian, and Eulerian models to estimate the exposure of target organisms

MODELLING OF ROUGH RICE SOLAR DRYING UNDER NATURAL CONVECTION

In this study, the sun drying behaviour of aromatic rough rice was investigated. Drying experiments were conducted at three different seasons in Ivory Coast. The drying data were fitted to ten different mathematical models. Among the models, the Two-term model was found to best explain thin layer open sun drying behaviour of the rice. The performance of these models was investigated by comparing the determination of coefficient (R²), sum square error (SSE) and root mean square error (RMSE) between the observed and predicted moisture ratios. The effective diffusivity coefficient of moisture transfer during drying, computed on the basis of Fick’s law, was within the range of 8.345.10-12 and 4.517.10-11m2.s-1. In addition, the activation energy was estimated to 68.255 Kj.mol-1

Modeling and Optimization of M-cresol Isopropylation for Obtaining N-thymol: Combining a Hybrid Artificial Neural Network with a Genetic Algorithm

The application of a hybrid framework based on the combination, artificial neural network-genetic algorithm (ANN-GA), for n-thymol synthesis modeling and optimization has been developed. The effects of molar ratio propylene/cresol (X1), catalyst mass (X2) and temperature (X3) on n-thymol selectivity Y1 and m-cresol conversion Y2 were studied. A 3-8-2 ANN model was found to be very suitable for reaction modeling. The multiobjective optimization, led to optimal operating conditions (0.55 ≤X1≤0.77; 1.773 g ≤ X2 ≤1.86 g; 289.74 °C ≤ X3 ≤291.33 °C) representing good solutions for obtaining high n-thymol selectivity and high m-cresol conversion. This optimal zone corresponded to n-thymol selectivity and m-cresol conversion ranging respectively in the interval [79.3; 79.5]% and [13.4 %; 23.7]%. These results were better than those obtained with a sequential method based on experimental design for which, optimum conditions led to n-thymol selectivity and m-cresol conversion values respectively equal to 67%and 11%. The hybrid method ANN-GA showed its ability to solve complex problems with a good fitting

BATCH FERMENTATION PROCESS OF SORGHUM WORT MODELING BY ARTIFICIAL NEURAL NETWORK

The production of tchapalo (traditional beer) remains uncontrolled and artisanal. For the improvement of the product quality, we need to know more about the traditional process and beer characteristics. The fermentation process is one of the most critical steps, which determines the quality of the beer. In this study, artificial neural network, precisely multi layer perceptron was used for modeling batch fermentation process of sorghum wort. The artificial neural network showed its ability to predict the ph, temperature, substrate, biomass, carbon dioxide (CO2) and alcohol (ethanol) evolution during batch fermentation of sorghum wort. All the correlation coefficients between the observed and predicted values for the artificial neural network were higher than 0.96. Thus, artificial neural network can be used to determine fermentation deviations during production of tchapalo and also to monitor and improve its quality

BATCH FERMENTATION PROCESS OF SORGHUM WORT MODELING BY ARTIFICIAL NEURAL NETWORK

The production of tchapalo (traditional beer) remains uncontrolled and artisanal. For the improvement of the product quality, we need to know more about the traditional process and beer characteristics. The fermentation process is one of the most critical steps, which determines the quality of the beer. In this study, artificial neural network, precisely multi layer perceptron was used for modeling batch fermentation process of sorghum wort. The artificial neural network showed its ability to predict the ph, temperature, substrate, biomass, carbon dioxide (CO2) and alcohol (ethanol) evolution during batch fermentation of sorghum wort. All the correlation coefficients between the observed and predicted values for the artificial neural network were higher than 0.96. Thus, artificial neural network can be used to determine fermentation deviations during production of tchapalo and also to monitor and improve its quality

MODELISATION DE LA CINETIQUE DE SECHAGE DES FEVES DE CACAO PAR DES MODELES SEMI-EMPIRIQUES ET PAR UN RESEAU DE NEURONES ARTIFICIELS RECURRENT: CAS DU SECHAGE MICROONDE PAR INTERMITTENCE

This study aimed to describe the cocoa beans behavior, and to predict the cocoa beans moisture content, during an intermittent microwave drying at 3 power levels (450 w, 600 w and 700 w). Experiments were carried out on fermented cocoa beans using a domestic microwave oven. The data obtained were adjusted using five semi-empirical models of drying on thin layer and a recurrent artificial neural network. Among the semi empirical models used, Page model was observed the most appropriate one for describing the cocoa beans behavior. For the various power levels, it presented respectively R² of 0.9993, 0.9971 and 0.9967. The recurrent artificial neural network used (R² > 0.999), presented a good ability to predict the moisture content of cocoa beans

Evaluation Des Caracteristiques Physico-Chimiques Et Microbiologiques D’un Beignet Traditionnel A Base De Mil Fermente (Gnomy) Commercialise Dans La Ville De Yamoussoukro (Cote D’ivoire)

Food craft is very active in Ivory Coast. It is mainly sold in the street and show a large variety of products, among them gnomy, a traditional donut prepared from fermented and deep fried millet. Evaluation of physical, chemical and microbiological characteristics of this food was the main theme of this study. The results demonstrate that gnomy is an acid food with a pH around 5.88 and protein content of 4.7 g/100 g. The absence of Salmonella and enumeration of aerobic mesophilic bacteria, Coliforms and Streptococci in the final product show that the gnomy produced at the laboratory following good hygiene practices is a food of satisfactory sanitary quality