Soft Sensor for VFA Concentration in Anaerobic Digestion Process for Treating Kitchen Waste Based on SSAE-KELM

Abstract

Anaerobic digestion technology is the most environmentally friendly approach to treat kitchen waste. Volatile fatty acid (VFA) is an essential quality monitoring indicator in the anaerobic digestion process of treating kitchen waste. In this paper, a soft measurement method of volatile fatty acid (VFA) concentration in the anaerobic digestion process is established based on stacked supervised auto-encoder combine kernel extreme learning machine algorithm (SSAE-KELM) to improve the real-time monitoring level and resource conversion efficiency of the anaerobic digestion process. Given the problems of poor feature extraction and low accuracy and efficiency of the model, a stack supervised autoencoder is proposed to realize nonlinear and deep feature extraction of process data. Simultaneously, using the idea of the extreme learning machine to train the network significantly improves the efficiency of the model. Then, the kernel extreme learning machine is used to realize regression modelling. Besides, a combined feature selection algorithm is presented to select auxiliary variables more accurately. The simulation results demonstrate that the soft sensor model can predict the concentration of volatile fatty acids (VFA) more efficiently and accurately.