DualLSTM: A novel key-quality prediction for a hierarchical cone thickener

Abstract

Due to the inaccuracy and significant disturbance of the complex and harsh environment in real industrial processes, the traditional sensor devices cannot meet the high-performance requirement of measuring key quality variables. However, in practical industrial thickener cone systems, the underflow concentration is hard to measure and has a high cost and significant time delay. Furthermore, the higher encoder representation often causes information loss from the process variables. This paper presents a novel efficient dual long short-time memory (LSTM) method for concentration prediction in the deep cone thickener system. To this end, dual feedforward and inverse bidirectional long-short time memory are proposed for feature learning and long temporal prediction. The proposed framework introduces an averaging moving filtering to pass through features, therefore the performance of dual LSTM is increased by a large margin. In addition, the feedforward and reverse bidirectional LSTM are employed to learn the robust information without loss. At last, experimental verification of the performance of an industrial deep cone thickener demonstrates the proposed dual LSTM method outperforms other state-of-the-art methods.