Partially precise instrument measurements-aided deep learning for industrial quality prediction

Abstract

Material composition is a kind of important quality index in the process industry. Even though instruments for online measuring these compositions have been widely applied, the precision of material composition measurements is suspicious due to corrosion, scaling and other factors. Laboratory values are more convinced, while these instruments are largely idle in real applications. Nevertheless, despite suspicious precision, partially precise trends exist in these measurements, which are also useful for indicating the variation in quality. This means that a wealth of information directly related to quality variables can provide positive guidance for quality prediction. Enlightened by the requirement of information utilization, a long short-term memory network with embedded trend consistency criteria (TCC-LSTM) is proposed for industrial quality prediction through extremely efficient utilization of partially precise quality instrument data. Specifically, based on the property that the trends of the measured values for quality variable are similar to that of the corresponding laboratory values over time, six trend consistency criteria are designed to evaluate the reliability of instrument data, so as to determine the contribution weights of these data in deep learning-based quality prediction. Moreover, in the neural network structure, the space-wise and time-wise attention mechanisms are designed for capturing important variables and time information. Extensive experiments on an actual alumina digestion process demonstrate the efficiency of TCC-LSTM, whose correlation coefficient is averagely improved by 0.2247 and mean absolute error is as low as 0.008079.