A KPI-Based Soft Sensor Development Approach Incorporating Infrequent, Variable Time Delayed Measurements

The overall performance of complex industrial systems using control and optimization strategies is often hampered by the lack of available measurements of key quality variables. The development of a soft sensor is a solution that allows for the necessary process information to be made available. Due to the nonlinear characteristics of industrial processes, a new soft sensor development approach that incorporates infrequent, variable time delayed measurements is proposed. Based on the fast-sampled measurement and the nonlinear, first principles model, the frequent and delay-free key performance indicator (KPI) estimation is realized using the square-root unscented Kalman filter (SR-UKF). Then, a modified Kalman Filter (MKF) algorithm is proposed to deal with those infrequent, but accurate measurements, whose values are variably delayed in time. These two types of estimates are fused and optimized to give an optimal, reliable KPI estimate based on the distributed state fusion UKF filter algorithm. The performance and effectiveness of the proposed approach are demonstrated by an experimental application for alumina concentration monitoring in the aluminum electrolysis industry.