Statistical Performance Monitoring & Control Of Batch Processes

Batch processes are widely used in process industry. Due to the flexibility, batch processes play important role in the production of low-volume, high-value products such as pharmaceuticals, colorants, flavors and biochemical products. Based on the historical operation data, batch SPMC (statistical performance monitoring & control) establishes monitoring and fault diagnosis models with statistical approach and applies them in the on-line monitoring of batch process operation. Batch SMPC, enabling the detection and correction of abnormal process behaviors, guarantees the efficient, safe and stable operations of batch processes and thus improves the quality consistency and the profits of enterprise. Because batch SPMC only relies on process data and thus general-purposed, it has become one of the most active research areas in process control.In this thesis, some important problems of batch SMPC are widely studied and some new SMPC algorithms are proposed with respect to the characteristic of batch process data.The main contributions are as follows:(1) Mote-Carlo experiments are involved to analyze the properties of the statistics of PCA. It is shown that the actual false alarms rate will be different with the theoretical value if the number of modeling data is too few or the number of principal components is incorrect. After analyzing the cause of the difference, a new two-stage PCA algorithm is proposed and demonstration of the distributions of relative statistics is given. Further experiments demonstrate that two-stage PCA overcomes the problem of tradition PCA.(2) A robust Multiway Principal Component Analysis (MPCA) is developed to construct monitoring models when outliers are present in historical data. By involving the robust estimation theory and projection pursuit algorithm, robust MPCA eliminates the effect of outliers efficiently while tradition MPCA is strongly affected by them. Applications on DuPont batch polymerization process reveal the efficiency of robust MPCA.(3) A new step-by-step batch SPMC approach is proposed for the monitoring of multi-stage batch processes. It overcomes the need to estimating or filling in the unknown part of the process variable trajectory deviations from the current time until the end. The adaptive rate is easily controlled through a parameter that controls the weight of past data in a summation manner. This algorithm is evaluated on industrial fermentation process data and compared with the traditional MPCA. The method describes the behavior of batch process more precisely and has significant benefits especially when monitoring multi-stage batch process where the latent vector structure may change at several points.(4) A PSO-NGPP batch monitoring approach is proposed without assuming that thelatent variables subject to normal distribution. The approach is based on non-Gauss evaluation criterion, projection pursuit and partial swarm optimization. PSO-NGPP decomposes the original signals into a series of independent components whose distributions can be easily obtained by kernel density estimation. Application results on the penicillin fermentation benchmark process demonstrate the power and advantages of presented method.(5) The influence of auto & cross correlations on statistical process control (SPC) is investigated in detail via Monte Carlo experiments. It is revealed that non independent identically distribution will cause the actual FAR to deviate from its theoretic value apparently and result in unexpected consecutive false alarms for normal operating process. Dynamic PCA and ARMA-PCA are demonstrated to be inefficient to remove the auto & cross correlations which still influence the FAR. Subspace identification based PCA (SI-PCA) is proposed to improve the monitoring of dynamic processes which can remove the auto & cross correlations efficiently and avoid consecutive false alarms. A criterion to determine the order of SI model is also introduced. The applications in Tennessee Eastman challenge process illustrate the advantages of the proposed approach.(6) Senso...