Optimization and prediction of biogas yield from pretreated Ulva Intestinalis Linnaeus applying statistical-based regression approach and machine learning algorithms

Abstract

A statistical-based regression approach and machine learning (ML) algorithms (response surface methodology (RSM), feed-forward backpropagation artificial neural network (ANN) and multi-layer adaptive neuro-fuzzy inference system (ANFIS)) were explored for the optimization and prediction of biogas resulting from the anaerobic digestion (AD) of pretreated Ulva Intestinalis Linnaeus (UIL). ANFIS model was found to better predict and model the process of biogas production from the AD of pretreated UIL owing to low mean square error (MSE) and RMSE values (0.8841 and 0.9402-US, 0.9628 and 0.9812-O3, 0.1387 and 0.3724-MW and 0.3018 and 1.1410-Fe3O4) and highest values of R2 (0.9998-US, 0.9996-O3, 0.9996-MW and 0.9995-Fe3O4). Optimum conditions for biogas yield as a result of the various pretreatment processes based on the ANFIS model were US-15 power/time, time-40 min and the biogas yield-181.0 mL.gVS−1, O3-15.0 mg/min, time-40.0 min and biogas yield of 164.0 mL.gVS−1, MW-2.3 power/time, time-40.0 min and biogas yield-81.7 mL.gVS−1 and Fe3O4-20.0 mg L−1, time-40.0 min and biogas yield-154 mL.gVS−1. The results obtained show that the ML and statistical-based models were effective in approximating the biogas yield with high precision and low error and could be beneficial for the biogas production scale-up process.