Developing an adaptive neuro‐fuzzy inference system based on particle swarm optimization model for forecasting Cr(VI) removal by NiO nanoparticles

• Published in: Environmental progress & sustainable energy Vol. 40; no. 4
• Main Authors: Rajabi Kuyakhi, Hossein, Tahmasebi Boldaji, Ramin
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.07.2021
• Subjects: adaptive neuro‐fuzzy inference system; Cr(VI); nanoparticles; particle swarm optimization; wastewater treatment
• ISSN: 1944-7442; 1944-7450
• DOI: 10.1002/ep.13597

The treatment of wastewater from heavy metal ions such as hexavalent chromium Cr(VI) is considered as an important issue in recent years, which is harmful to human health and environment. Since, in engineering, performing the experiments to solve problems is time‐consuming and costly. In this study, adaptive neuro‐fuzzy inference system (ANFIS) was coupled with particle swarm optimization (PSO) algorithm to develop a predictive model for modeling of Cr(VI) removal percent on NiO nanoparticle. To this end, the trace of four initial parameters containing contact time, Cr(VI) initial concentration, NiO adsorbent dosage, and pH on removing Cr(VI) was investigated. The performance of the developed algorithm was evaluated by statistical parameters such as mean absolute relative deviation mean squared error (MSE) maximum absolute error and, R2 and graphic methods. The ANFIS‐PSO shows high‐performance modeling of Cr(VI) removal with R2 = 0.998, MSE = 0.0014, and AARD = 0.0011 compare to the established model in previous works.