Cleaner Production Assessment for Wastewater Treatment Plants Based on Backpropagation Artificial Neural Network

Abstract

This paper aims to create a rational standard for cleaner production (CP) in wastewater treatment plants (WWTPs). To this end, a cleaner production assessment system was established for WWTPs in light of relevant theories on cleaner production review; then, the analytic hierarchy process (AHP) and the artificial neural network (ANN) were combined into an AHP-based BP-ANN model for CP assessment of WWTPs. In the proposed model, the AHP evaluation results are taken as the network inputs, and trained and tested via backpropagation artificial neural network (BP-ANN). Then, the proposed model was verified through a case study on several WWTPs in Central China. The verification results show that the model fully absorbs the tacit knowledge and experience of expert scoring, and reduces the arbitrariness of subjective evaluation. With high accuracy, sound feasibility and controllable error, the proposed method boasts a great potential in the cleaner production evaluation of WWTPs.