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The objective of this study was to model the removal efficiency of ternary adsorption system using feed-forward back propagation artificial neural network (FFBP-ANN). The ANN model was trained with Levenberg-Marquardt back propagation algorithm and the best model was found with the architecture of {9-11-4-3} neurons for the input layer, first and second hidden layers, and the output layer, respectively, based on two metrics, namely, mean squared error (MSE) = (0.2717-0.5445) and determination coefficient (R 2 ) = (0.9997-0.9999). Results confirmed the robustness and the efficiency of the developed ANN model to model the adsorption process.

The predictability of the adsorption capacity of the multicomponent adsorption system was modelled using Support Vector Machine (SVM). Two SVM models were built and compared. In the first model, the SVM method was used with an already built-in optimisation algorithm. However, in the second model, the SVM method was used by means of a very recent and efficient optimisation algorithm: the Dragonfly Algorithm (DA). The models' accuracy was evaluated by three well-established statistical metrics (root mean squared error RMSE, determination coefficient R 2 , and correlation coefficient R). The used data were collected from previous experimental papers published in literature containing all kinds of pollutants, such as heavy metal ions, dyes, and organic compounds, and different natural/ synthetic adsorbents. The dataset contained five important variables with 1023 points; the variables were divided into four inputs (molecular weight, equilibrium concentrations of adsorbate, specific area of adsorbent, and temperature), and one output (adsorption capacity at equilibrium). The data were divided using the holdout function into two subsets (80 % for training set, and 20 % for test set). The programming stage was carried out using MATLAB software. The results showed that the optimised DA-SVM model with RBF-Gaussian kernel function had good ability for global search combined with high prediction accuracy, with R 2 = 0.997, R = 0.998, and RMSE = 2.539. The obtained model can be used to predict the efficiency of the adsorption system, and provides a tool for process optimisation responding to changes in operating conditions. A new graphical user interface (GUI) was developed with MATLAB GUI to estimate accurately the desired responses by using the best DA-SVM model.

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The aim of this study consists of the production of a bio-surfactant from a new bacterial strain, Marinobacter hydrocarbono clasticus SF (96.76 % simila-rity) isolated from soil contaminated by hydrocarbons in Hassi-Messaoud. (Southern Algeria) to treat liquid effluent from slaughterhouse water. The characteristics of organic matter biodegradation tests were discussed. Despite the high pollutant load and the unfavorable Physico-chemical composition of the effluent, the specific growth rate of the isolated strain after 10 days of incubation in the range of 0-30 g L-1 of NaCl was at neutral pH 7.4 and temperature of 45 ?C. The best bio-surfactant production yield was obtained after 72 h of incubation and under the optimal production conditions such as diesel as carbon source, ammonium chloride as nitrogen source, and a C/N ration of 5. The bio-surfactant produced is of glycolipid type with a low critical micellar concentration (CMC), good emulsifying power, and chemical and functional stability. Significant pollutant removal efficiency was obtained using the bacterial strain (up to 82 %) and the bio-surfactant (up to 96 %). Several anions, such as nitrates, phosphates, ammonium, and suspended solids, were measured.