Learn More
Multivariate statistical process control (MSPC) has been successfully applied to chemical processes. In order to improve the performance of fault detection, two kinds of advanced methods, known as moving principal component analysis (MPCA) and DISSIM, have been proposed. In MPCA and DISSIM, an abnormal operation can be detected by monitoring the directions(More)
In order to control product compositions in a multicomponent distillation column, the distillate and bottom compositions are estimated from on-line measured process variables. In this paper, inferential models for estimating product compositions are constructed using dynamic Partial Least Squares (PLS) regression, on the basis of simulated time series data.(More)
Multivariate statistical process control (MSPC) has been widely used for process monitoring. When a fault is detected, it is important to identify an actual cause of the fault. Fault identification methods are classified into two groups by availability of historical data sets obtained from faulty situations. When such historical data sets are not available,(More)
Univariate and multivariate statistical process control (USPC and MSPC) methods have been widely used in process industries for fault detection. However, their practicability and achievable performance are limited due to the assumptions that a process is operated in a steady state and that variables are normally distributed. In the present work, external(More)
In this paper, an autonomous decentralized scheduling system for just-in-time production is proposed. In the proposed system, each scheduling subsystem belonging to respective production stage derives a near optimal schedule by repeating the generation of the schedule of its own stage and data exchange among the other production stages. The objective(More)
The maintenance of model predictive control (MPC) systems is one of the major problems identified by industrial process control engineers. Since performance deterioration is usually caused by changes in process characteristics, effective re-modeling is the key to success. Obviously, not all sub-models have to be reconstructed; thus, it is crucial to(More)