Underflow concentration prediction based on improved dual bidirectional LSTM for hierarchical cone thickener system

Abstract

In the practical thickener cone systems, the underflow concentration is hard to measure through physical sensors while there exist the high cost and significant measurement delay. This paper presents a novel and deeply efficient long short-time memory (DE-LSTM) method for concentration prediction in the deep cone thickener system. First, the DE-LSTM for thicker systems is developed for feature learning and long temporal preprocessing. Then, the feedforward and reverse LSTM subnetworks are employed to learn the robust information without loss. At last, the experimental verification of an industrial deep cone thicker demonstrates the proposed DE-LSTM’s performance outperforms other state-of-the-art methods.