LAUSR

State estimation and fusion is studied using Kalman filter (KF) when a slow-rate integrated measurement is available. Integrated measurement is common in industrial processes, when a sample of material is gradually collected over some period of time and then sent to a laboratory for analysis. In this case, the laboratory measurement will reflect the material properties that have been integrated over the sampling period. The goal is to estimate the fast-rate value of states that evolve with time. A modified KF is proposed to execute state estimation using a slow-rate integrated measurement. Fusion of the slow-rate state estimate and other fast-rate measurements can improve the final state estimation of the process. The performance of the proposed method is demonstrated through both simulation and experimental study in a laboratory scale hybrid tank pilot plant.Note to Practitioners—When a sample of material is sent to laboratory for analysis, a common practice is to gradually collect a small amount of material over a period of time in a container and send the collection to the laboratory at the end of the sampling period. In this case, the laboratory measures the average of the variable during that period of time. Experienced operators can use this measurement to operate the process. However, including this integrated slow rate measurement into the automatic process control and monitoring system is a challenging problem. In this paper, we mathematically formulate the estimation of the real-time value of a variable using integrated laboratory measurement. The estimation is also improved through fusion of this measurement and other conventional measurements. Laboratory experimental result illustrates the feasibility of the proposed method.