Multi-characteristic optimization and modeling analysis of electrocoagulation treatment of abattoir wastewater using iron electrode pairs. (October 2022)

• Record Type: Journal Article
• Authors: Obi, Christopher Chiedozie; Nwabanne, Joseph Tagbo; Igwegbe, Chinenye Adaobi; Ohale, Paschal Enyinnya; Okpala, Charles Odilichukwu R.
• Is Part Of: Journal of water process engineering. Volume 49(2022)
• Publication Date: 2022-10
• Subjects: Electrocoagulation -- Abattoir wastewater -- Artificial intelligent tools -- RSM -- ANN -- ANFIS; Water-supply engineering -- Periodicals; Saline water conversion -- Periodicals; Seawater -- Distillation -- Periodicals; Sanitary engineering -- Periodicals; Sewage -- Purification -- Periodicals; 627
• DOI: 10.1016/j.jwpe.2022.103136
• Languages: English
• ISSNs: 2214-7144

Abstract

Abstract: Multi-characteristic optimization and modeling analysis of electrocoagulation (EC) treatment of abattoir wastewater (AWW) using iron‑iron electrodes are reported. Response Surface Methodology (RSM) and Artificial Intelligent (AI) modeling tools viz. Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used for the modeling while the numerical method of RSM and RSM-based Genetic Algorithm (RSM-GA) were used to optimize the response variable. The independent variables were pH, current intensity, electrolysis time, settling time, and temperature while the dependent variable was percentage turbidity removal. Based on high determination coefficient (R 2 ) and low standard deviation (SD) values, the quadratic model was selected from the ANOVA of the RSM model. For the ANN model, the number of neurons were varied between 2 and 10 but based on low MSE and high R 2 values, 10 was selected as the optimum number of neurons. The ANFIS network used the triangular input membership function (trimf) with 100 epochs. The three models depicted linear adequacy with respective R 2 values of 0.9978, 0.9995, and 0.8285 for RSM, ANFIS, and ANN respectively. However, the performance error indices indicated that the prediction accuracy of the three models followed the order — ANFIS > ANN > RSM. Further, the validated optimization results produced 94.74 % and 97.42 % turbidity removal for the RSM and RSM-GA optimization methods respectively. This proved that RSM, ANN, and ANFIS can reliably be used to model the EC treatment of AWW using FeFe electrodes; while the response variable can successfully be optimized by RSM and RSM-GA methods. Graphical abstract: Unlabelled Image Highlights: The EC treatment of abattoir wastewater was modeled using RSM, ANN, and ANFIS. The response variable was optimized using numerical RSM and RSM-GA. Prediction accuracy of the models followed the order — ANFIS > RSM > ANN. RSM and RSM-GA optimization produced 94.74 and 97.42 % turbidity removal, respectively.