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

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.

A review of remediation technologies for uranium-contaminated water

Uranium is a naturally existing radioactive element present in the Earth's crust. It exhibits lithophilic characteristics, indicating its tendency to be located near the surface of the Earth and tightly bound to oxygen. It is ecotoxic, hence the need for its removal from the aqueous environment. This paper focuses on the variety of water treatment processes for the removal of uranium from water and this includes physical (membrane separation, adsorption and electrocoagulation), chemical (ion exchange, photocatalysis and persulfate reduction), and biological (bio-reduction and biosorption) approaches. It was observed that membrane filtration and ion exchange are the most popular and promising processes for this application. Membrane processes have high throughput but with the challenge of high power requirements and fouling. Besides high pH sensitivity, ion exchange does not have any major challenges related to its application. Several other unique observations were derived from this review. Chitosan/Chlorella pyrenoidosa composite adsorbent bearing phosphate ligand, hydroxyapatite aerogel and MXene/graphene oxide composite has shown super-adsorbent performance (>1000 mg/g uptake capacity) for uranium. Ultrafiltration (UF) membranes, reverse osmosis (RO) membranes and electrocoagulation have been observed not to go below 97% uranium removal/conversion efficiency for most cases reported in the literature. Heat persulfate reduction has been explored quite recently and shown to achieve as high as 86% uranium reduction efficiency. We anticipate that future studies would explore hybrid processes (which are any combinations of multiple conventional techniques) to solve various aspects of the process design and performance challenges.

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.

Nitrogen Removal from Landfill Leachate Using Biochar Derived from Wheat Straw

Landfill leachate (LLCH) disposal poses challenges due to high pollutant concentrations. This study investigates the use of biochar (BC) derived from wheat straw for nitrogen content reduction. Laboratory experiments evaluated BC's adsorption capacity (qm) for nitrogen removal from ammonium chloride solution (NH4Cl) and LLCH, along with testing isotherm models. The results demonstrated that BC was more efficient (95.08%) than commercial activated carbon AC (93.11%), the blank, in adsorbing nitrogen from NH4Cl. This superior performance of BC may be attributed to its higher carbon content (57.74%) observed through elemental analysis. Lower results for BC/LLCH may be due to LLCH's complex chemical matrix. The Langmuir isotherm model best described BC/NH4Cl adsorption (qm = 0.5738 mg/g). The AC/NH4Cl data also fitted into the Langmuir (R2 ˃ 0.9) with a qm of 0.9469 mg/g, and 26.667 mg/g (R2 ˂ 0.9) was obtained for BC/LLCH; the BC/LLCH also gave higher qm (R2 ˃ 0.9) using the Jovanovich model (which also follows Langmuir's assumptions). The mean energy of the adsorption values estimated for the AC/NH4Cl, BC/NH4Cl, and BC/LLCH processes were 353.55, 353.55, and 223.61 kJ/mol, respectively, suggesting that they are all chemisorption processes and ion exchange influenced their adsorption processes. The Freundlich constant (1/n) value suggests average adsorption for BC/LLCH. The BC/LLCH data followed the Harkins-Jura model (R2: 0.9992), suggesting multilayered adsorption (or mesopore filling). In conclusion, biochar derived from wheat straw shows promising potential for landfill leachate remediation, offering efficient nitrogen removal capabilities and demonstrating compatibility with various adsorption models. This research also lays the groundwork for further exploration of other biochar-based materials in addressing environmental challenges associated with landfill leachate contamination.

Modeling and optimization study on degradation of organic contaminants using nZVI activated persulfate based on response surface methodology and artificial neural network: a case study of benzene as the model pollutant

Due to the complicated transport and reactive behavior of organic contamination in groundwater, the development of mathematical models to aid field remediation planning and implementation attracts increasing attentions. In this study, the approach coupling response surface methodology (RSM), artificial neural networks (ANN), and kinetic models was implemented to model the degradation effects of nano-zero-valent iron (nZVI) activated persulfate (PS) systems on benzene, a common organic pollutant in groundwater. The proposed model was applied to optimize the process parameters in order to help predict the effects of multiple factors on benzene degradation rate. Meanwhile, the chemical oxidation kinetics was developed based on batch experiments under the optimized reaction conditions to predict the temporal degradation of benzene. The results indicated that benzene (0.25 mmol) would be theoretically completely oxidized in 1.45 mM PS with the PS/nZVI molar ratio of 4:1 at pH 3.9°C and 21.9 C. The RSM model predicted well the effects of the four factors on benzene degradation rate (R2 = 0.948), and the ANN with a hidden layer structure of [8-8] performed better compared to the RSM (R2 = 0.980). In addition, the involved benzene degradation systems fit well with the Type-2 and Type-3 pseudo-second order (PSO) kinetic models with R2 > 0.999. It suggested that the proposed statistical and kinetic-based modeling approach is promising support for predicting the chemical oxidation performance of organic contaminants in groundwater under the influence of multiple factors.