Enhancing municipal solid waste leachate treatment efficiency: AI-based prediction of electrocoagulation/flocculation recovery using iron electrodes.

This study addresses a gap in municipal leachate (MUPL) treatment by introducing a pioneering application of artificial intelligence (AI) in the electrocoagulation/electroflocculation (EC/EF) process utilizing iron electrodes. The overarching aim is to demonstrate the efficacy of AI, particularly a multi-layer perceptron (MLP)-based feed-forward artificial neural network (ANN) incorporating the Levenberg-Marquardt (LMb) algorithm, in predicting and optimizing EC/EF outcomes for turbidity (TDY) removal. The research methodology involved experimentation and robust ANN data modeling. The significance of this work emerges from the successful integration of AI, showcasing its potential in advancing wastewater, demonstrated through a strong positive correlation (0.994) between the ANN model predictions and experimental outcomes. The study achieves a remarkable 99.4% TDY removal at an electrolysis time of 10 min and contributes valuable insights into the critical parameters influencing the EC/EF process. Results from the ANN modeling exhibit high predictive accuracy, supported by elevated R-squared values and minimal mean square error. Statistical analyses underscore the significance of key process parameters, highlighting the influential roles of current intensity and settling time. The study emphasized the favourable impact of maintaining an acidic pH range, as it reduced electrostatic repulsion between particles, facilitating pollutant agglomeration, and identified electrolysis time as a key factor in enhancing treatment efficiency, supported by a strong positive correlation between electrolysis time and TDY reduction. Energy cost savings were realized by not requiring temperature elevation. Achieving a 99.4% TDY removal translates to substantial reductions in other pollutants present in the MUPL, thereby elevating water quality and ensuring compliance.

1-Butyl-3-methylimidazolium methane sulfonate ionic liquid corrosion inhibitor for mild steel alloy: Experimental, optimization and theoretical studies.

The current research reports the performance of 1-butyl-3-methylimidazolium methane sulfonate ([C4MIM][OMs](IL)) as effective corrosion inhibitor for mild steel in 1 M H2SO4 electrolyte. For proper evaluation, weight loss, electrochemical study, theoretical modeling and optimization techniques were used. Weight loss and electrochemical methods shows that the inhibition performance of [C4MIM][OMs] on the metal surface strengthens as the concentration increases. Maximum inhibition efficiency of 85.71%, 92.5% and 91.1% at 0.8 g L-1 concentration of [C4MIM][OMs] were obtained from the weight loss, polarization and impedance studies, respectively. In addition, response surface methodology (RSM) a statistical tool was used for modeling and optimization of the empirical data. The RSM model validates the empirical findings. Also, DFT/MD-simulation investigations evidenced that [C4MIM][OMs] forms a barrier film on the mild steel surface. The result shows that the synthesized [C4MIM][OMs] could open up opportunities in corrosion and materials protection for sustainability.

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.

Purification of aquaculture effluent using Picralima nitida seeds.

Aquaculture effluent treatment is essential to eliminate the undesirable characteristics of water to ensure cleaner production and environmental sustainability. In an effort to develop green coagulant without compromising cost, this research investigated the feasibility of aquaculture effluent (AQEF) pollutant removal using Picralima nitida seeds extract (PNSC) and its bio-coagulation/adsorption kinetic characteristics with the substrate in water. The coagulative decrease was observed in terms of TD (turbidity), TSS (total suspended solids), COD (chemical oxygen demand), BOD (biochemical oxygen demand), and COLR (color) from AQEF. The active coagulant was extracted from the seeds and analyzed for its spectral and morphological characteristics through FTIR and SEM. The influence of PNSC dosage (0.10-0.50 g L-1), pH (2-10), settling time (0-60 min), and temperature (303-323 K) on the removal of contaminants were surveyed. The process kinetics of coagulation-flocculation were also explored. Maximal TD reduction of 90.35%, COD (82.11%), BOD (82.38%); TSS (88.84%), and COLR (65.77%) at 0.2 g PNSC L-1, pH 4, and 303 K was achieved. Analysis of variance (ANOVA) tests proved that pH, temperature, and settling time had a significant effect on pollutant removal. Results fitted Von Smoluchowski's perikinetics theory at the optimum conditions, which gave R2 > 0.900. At perikinetics circumstances, the Kb (reaction rate) and [Formula: see text] (half-life) correspond to 0.0635 Lg-1 min-1 and 1.9 min. More so, sorption results fitted the Lagergren over the Ho model. Additionally, the net cost of using PNSC to handle 1 L of AQEF (including electricity, material, and labor costs) was evaluated to be €4.81. Overall, the PNSC appears reliable and useful in pretreating AQEF for improved biodegradability and superior effluent quality.

Computational modeling and multi-objective optimization of engine performance of biodiesel made with castor oil.

In this research the engine performance of biodiesel made with castor oil through homogeneous alkali catalyzed transesterification was analyzed. The input variables for the performance analysis were biodiesel blend and engine speed while the response variables were break power (BP), basic specific fuel consumption (BSFC), break thermal efficiency (BTE), torque and unit cost. The engine performance was modeled using artificial neural network (ANN) and the ANN was subsequently used as the objective function for a non dominated sorting genetic algorithm (NSGA-II) for multi objective optimization of the engine performance. The ANN was equally coupled with a desirability function whose outputs were optimized using simulated annealing for multi objective optimization of the engine performance. Subsequent comparison of the two optimization models was done. The results show that biodiesel from castor oil could be a good replacement for biodiesels from fossil fuels. The ANN model predicted engine performance very well with the lowest value of the correlation coefficient between the experimental responses and ANN predictions being 0.9733. The multi objective optimization using desirability function performed excellently well with the optimum blend and speed being 78.7% and 1754.48 rpm respectively. The Pareto front from the NSGA-II algorithm generally has high desirability values. The Pareto front solution which is more flexible than the desirability function solution would serve as an excellent guide for engine designers. Finally, castor oil based biodiesel cost was for the first time integrated into engine performance optimization studies.

Electrochemical-kinetics, MD-simulation and multi-input single-output (MISO) modeling using adaptive neuro-fuzzy inference system (ANFIS) prediction for dexamethasone drug as eco-friendly corrosion inhibitor for mild steel in 2 M HCl electrolyte.

In this research, the effect of Dexamethasone drug (DM) on mild steel corrosion  in  2 M HCl was analyzed using weight loss, potentiodynamic polarization, electrochemical impedance spectroscopy (EIS) and MD-simulation. In addition, Fourier transform infrared spectra (FTIR), scanning electron microscopy (SEM), Energy dispersive x-ray spectroscopy (EDX), and atomic force microscopy (AFM) were employed to inspect the mild steel surface in the blank and inhibited medium. For the optimization tool, adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the inhibition efficiency. The experimental data was categorized into two different sections for training and testing the ANFIS model. The developed model aimed to evaluate the fitness between the experimental and predicted values. From the results generated, optimum value (IE%) of DM was recorded as 80%, 81% and 83% at concentration of 0.4 g/L for weight loss, EIS and PDP respectively. Potentiodynamic polarization results reveal that Dexamethasone functions as a mixed-type inhibitor, whereas studies of EIS show that the inhibition mechanism is by the transfer of charges. Mild steel surface examination confirmed the presence of a protective adsorbed film on the mild steel surface. Thermodynamic parameters obtained imply that Dexamethasone is adsorbed on the steel surface by a physiochemical process and obeys Langmuir adsorption isotherm. Also the MD-simulation results evidenced that DM forms a metallic surface adsorbed film on the steel surface. From the ANFIS model, the sensitivity analysis shows that time and inhibitor concentration were the most important input variable while other input variables could not be neglected. ANFIS model coefficient of determination (R 2 0.993) was found between the observed and predicted values. ANFIS model gave optimum prediction (80%) with high degree accuracy and robustness. The outcomes of this investigation provide more information, simulation, and prediction about inhibition of metal corrosion.

Adsorptive treatment of brewery effluent using activated Chrysophyllum albidium seed shell carbon.

Chrysophyllum albidium seed shell, an abundant, biodegradable and inexpensive natural resource was used as a precursor to bioadsorbent production for the removal of suspended and dissolved particles (SDP) from initially coagulated Brewery Effluent (BRE). Influence of key parameters such as contact time, bioadsorbent dose, pH and temperature were investigated using batch mode. The thermal behavior studies were evaluated using Thermogravimetric and Differential scanning calorimetric analyses. The morphological observations and functional groups of the bioadsorbents were determined using scanning electron microscopy and Fourier transform infrared spectroscopy, respectively. The adsorption equilibrium, thermodynamics and kinetic of SDP adsorption on H3PO4-treated shell and NH4Cl-treated shell were examined at specified temperatures. Equilibrium data sufficiently fitted the Langmuir isotherm model (R (2) > 0.99; SSE < 0.09). The pseudo-second order kinetic model provided the best correlation (R (2) > 0.99; SSE < 0.14) with the experimental data. The values of ΔG° and ΔH° indicated the spontaneous and endothermic nature of the process. This study demonstrated that C. albidium seed shell could be utilized as low cost, renewable, ecofriendly bioadsorbent for the uptake of SDP from BRE.