Identifying the maturity of co-compost of olive mill waste and natural mineral materials: Modelling via ANN and multi-objective optimization

Abstract

In this study, olive mill waste (OMW) and natural mineral amendments were co-composted to evaluate the compost maturity efficiency. The results were modelled by Feed-Forward Neural Networks (FF-NN) and Elman-Recurrent Neural Networks (ER-NN) and compared Response Surface Methodology (RSM). According to RSM produced a prediction error of more than 10% while Neural Networks (NNs) models were <2%. From, multi-objective optimization, the most suitable materials were expanded vermiculite and pumice with overall desirabilities of 0.60 and 0.56, respectively. The optimum amendment ratios were achieved with 14.3% of expanded vermiculite and 16.0% of pumice for OMW composting. Multivariate Analysis of Variance (MANOVA) results indicated that the materials had a strong effect on composting in parallel with the optimization results. NNs were predictors with superior properties to model the composting processes, can be used as modeling tools in many areas that are difficult and costly to perform new experiments.

Keywords: Artificial neural networks; Composting; Genetic algorithm; Olive mill waste; Response surface methodology.