Intelligent algorithms-aided modeling and optimization of the deturbidization of abattoir wastewater by electrocoagulation using aluminium electrodes.

The removal of turbidity from abattoir wastewater (AWW) by electrocoagulation (EC) was modeled and optimized using Artificial Intelligence (AI) algorithms. Artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), particle swarm optimization (PSO), and genetic algorithms (GA) were the AI tools employed. Five input variables were considered: pH, current intensity, electrolysis time, settling time, and temperature. The ANN model was evaluated using the Levenberg-Marquardt (trainlm) algorithm, while the ANFIS modeling was accomplished using the Sugeno-type FIS. The ANN and ANFIS models demonstrated linear adequacy with the experimental data, with an R2 value of 0.9993 in both cases. The corresponding statistical error indices were RMSE (ANN = 5.65685E-05; ANFIS = 2.82843E-05), SSE (ANN = 1.60E-07; ANFIS = 3.4E-08), and MSE (ANN = 3.2E-09; ANFIS = 8E-10). The error indices revealed that the ANFIS model had the least performance error and is considered the most reliable of the two. The process optimization performed with GA and PSO considered turbidity removal efficiency, energy requirement, and electrode material loss. An optimal turbidity removal efficiency of 99.39 % was predicted at pH (3.1), current intensity (2 A), electrolysis time (20 min), settling time (50 min), and operating temperature (50 °C). This represents a potential for the delivery of cleaner water without the use of chemicals. The estimated power consumption and the theoretical mass of the aluminium electrode dissolved at the optimum condition were 293.33 kW h/m3 and 0.2237 g, respectively. The work successfully affirmed the effectiveness of the EC process in the removal of finely divided suspended particles from AWW and demonstrated the suitability of the AI algorithms in the modeling and optimization of the process.

Optimal pretreatment of plantain peel waste valorization for biogas production: Insights into neural network modeling and kinetic analysis.

This work proposed a model for the substrate treatment stage of biogas production process in an anaerobic digestion system. Adaptive neuro-fuzzy inference system (ANFIS), response surface method (RSM), and artificial neural network (ANN) were comparatively used in the simulation and modeling of the treatment process for improved biogas yield. Waste plantain peels were pretreated and used as substrate. FTIR and SEM results revealed that the pretreatment improved the substrate's desirable qualities. The amount of biogas yield was controlled by time, NaOH concentration, and temperature of the substrate pretreatment. Optimum pretreatment conditions obtained were a temperature of 102.7 °C, time of 31.7 min and NaOH concentration of 0.125 N. RSM, ANN, and ANFIS modeling techniques were proficient in simulating the biogas production, as evidenced by high R2values of 0.9281, 0.9850, and 0.9852, respectively. Furthermore, the values of the calculated error terms such as RMSE (RSM = 0.04799, ANN = 0.00969, and ANFIS = 0.00587) and HYBRID (RSM = 18.556, ANN = 0.803, and ANFIS = 0.0447) were low, indicating a satisfactory correlation between experimental and predicted values. Scrubbing of the biogas with caustic soda and activated charcoal increased the methane content to 94 %. The kinetics of the cumulative biogas yield were best fit with the Logistics and Modified Logistics models. The low C/N ratio in addition to the presence of potassium, nitrogen, and phosphorus suggested that the spent plantain peel slurry can be utilized as an agricultural fertilizer in crop production. The observations of this study therefore recommends the pre-treatment of biodigestion substrates as a key means to enhance beneficiation of methane production.

Modelling and optimisation of electrocoagulation/flocculation recovery of effluent from land-based aquaculture by artificial intelligence (AI) approaches.

This study examined the modelling and optimisation of the electrocoagulation-flocculation (ECF) recovery of aquaculture effluent (AQE) using aluminium electrodes. The response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) were used for the modelling, while the optimisation tools were the numerical RSM and genetic algorithm (GA). Furthermore, the kinetics of the ECF process was studied to provide insight into the mechanism governing the ECF of AQE. The experimental design was performed using the central composite design (CCD) of the RSM. The ANFIS modelling was accomplished via the Grid Partition (GP) of the data set, while the ANN used the multi-layer perceptron (MLP) based feed-forward system. Statistically, the prediction accuracy of the models followed the order: ANFIS (R2: 0.9990), ANN (R2: 0.9807), and RSM (R2: 0.9790). The process optimisation gave optimal turbidity (TD) removal efficiencies of 98.98, 97.81, and 96.01% for ANFIS-GA, ANN-GA, and RSM optimisation techniques, respectively. The ANFIS-GA gave the best optimization result at optimum conditions of pH 4, current intensity (3 A), electrolysis time (7.2 min), settling time (23 min), and temperature (43.8 °C). In the kinetics study, the experimental data was analysed using pseudo-first-order (0.8787), pseudo-second-order (0.9395), and Elovich (R2: 0.9979) kinetic models; the Elovich model gave the best correlation with the experimental data showing that the process is governed by electrostatic interaction mechanism. This study effectively demonstrated that ECF recovery of AQE can effectively be modelled using RSM, ANN, and ANFIS and be optimised using RSM, ANN-GA, and ANFIS-GA techniques, and the order of performance is ANFIS > ANN > RSM and ANFIS-GA > ANN-GA > RSM, respectively.