ISSN print edition: 0366-6352 ISSN electronic edition: 1336-9075 Registr. No.: MK SR 9/7 Published monthly

Multifactor optimization for treatment of textile wastewater using complex salt–Luffa cylindrica seed extract (CS-LCSE) as coagulant: response surface methodology (RSM) and artificial intelligence algorithm (ANN–ANFIS)

Patrick Chukwudi Nnaji, Valentine Chikaodili Anadebe, Okechukwu Dominic Onukwuli, Chukwunonso Chukwuzuloke Okoye, and Chiamaka Joan Ude

Department of Chemical Engineering, Michael Okpara University, Umudike, Nigeria

E-mail: pc.nnaji@mouau.edu.ng

Received: 16 June 2021  Accepted: 3 November 2021

Abstract:

The effectiveness of using complex salt–Luffa cylindrica seed extract (CS-LCSE) in a coagulation/flocculation (CF) method for the treatment of textile wastewater was investigated. Jar test procedure was used at different pH (2–10), dosage (1000–1800 mg/L) and stirring time (10–30 min). The optimum condition for the removal of chemical oxygen demand (COD) and color/total suspended solids (CTSS) from textile wastewater was determined. Response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict COD and CTSS removal efficiencies from textile wastewater under different conditions. The adequacy and predictive relevance of the three optimization methods were assessed using regression coefficient (R²), and mean square error (MSE). ANFIS (R² 0.9997, MSE 0.0002643), ANN (R² 0.9955, MSE 0.0845014) and RSM (R² 0.9474, MSE 1.049412) are the model indicators for CTSS removal, while for COD removal, the indicators are: ANFIS (R² 0.9996, MSE 0.0038472), ANN (R² 0.9885, MSE 0.0160658) and RSM (R² 0.9731, MSE 0.9083140). The suitability of ANFIS models over ANN and RSM in predicting COD and CTSS removal efficiency is demonstrated by the results obtained.

Keywords: RSM–ANN–ANFIS; Coagulation/flocculation; Luffa cylindrica; Textile wastewater

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-021-01971-7

Chemical Papers 76 (4) 2125–2144 (2022)