Use of artificial neural network and adaptive neuro-fuzzy inference system for prediction of biogas production from spearmint essential oil wastewater treatment in up-flow anaerobic sludge blanket reactor

[Display omitted] •Pollutants removal performance and biogas production of SEOW using UASB studied.•Best BP-ANN topology for predicting biogas production in UASB was 10-6-7-1.•Best ANFIS topology was a three layer ANFIS structure with eight ANFIS sub-networks.•Two AI-based models validation examined via statistical indicators: R2, RMSE, RRMSE.•Success of two AI-based models in predicting biogas produced in UASB treating SEOW. Two artificial intelligence (AI)-based models, namely feed-forward backpropagation artificial neural network (ANN) and multi-layer adaptive neuro-fuzzy inference system (ANFIS), were developed to estimate the biogas production in an up-flow anaerobic sludge blanket (UASB) reactor. The models' input variables including influent chemical oxygen demand (COD), pH, effluent mixed liquor suspended solids, effluent mixed liquor volatile suspended solids, turbidity removal, oil and grease removal, COD removal, phenol removal, and effluent volatile fatty acids and alkalinity were collected from an UASB reactor fed with spearmint essential oil wastewater (SEOW) during 141 consecutive operating days. The determination coefficient, root mean square error, and relative root mean the ANN model's square error reached 0.975, 2650 mL/d, and 0.234%, respectively, while those of ANFIS model reached 0.956, 3517 mL/d, and 0.315%, respectively. The results achieved herein demonstrated that two AI-based models were successful to estimate the biogas production in a lab-scale UASB reactor treating SEOW with high accuracy and low error.