Statistical Modeling, Online Monitoring And Quality Prediction For Multiphase Batch Processes

As an important industry production way, batch processes, which have a close relationship with people’s everyday life, have been widely applied to fine chemical, biopharmaceutical, food, polymer, and metal industries etc. Recently, with the urging market requirement for various product types and high product quality, the manufacturing of higher-value-added products that are mainly produced through batch processes have become increasingly important in many industries. The batch process safety and product quality have been the focus of people’s attention. Data-based multivariate statistical analysis techniques only require the normal process data for modeling and show particular advantages to deal with the high-dimension and coupling data, which makes them specially and increasingly attractive. Multivariate statistical modeling, online monitoring, fault diagnosis and quality prediction have been under wide investigation for batch processes.Batch processes are fairly more complex with more rich data statistical characteristics compared with continuous processes. The operation process covers multiple phases, which have specific control objects, different dominant process variables and distinct process correlation characteristics. Therefore, it is more challenging to conduct statistical analysis and online application for multiphase batch processes. It should not only pay attention to the whole operation status and find the causal relationship between process variables and quality variables, but also focus on different phases to analyze their local nature and reveal their different effects on quality.Based on the further research on multiplicity of operation phase, this dissertation developes a series of phase-based statistical modeling, process monitoring and quality prediction methods for batch processes to solve the practical problems:(1). For the phase transition behaviors in multiphase batch processes, the concept of fuzzy phase is introduced and a transition-based soft phase partition algorithm is developed, which divides the process into different subphases and transition regions between neighboring phases according to the changes of underlying process correlations along time. Consequently, focusing on their different data nature, different statistical models are respectively developed as well as the corresponding online monitoring strategy. (2). For limited reference batches, a new statistical analysis strategy is proposed to explore the phase information and thus develop the phase-based statistical models for online monitoring. Meanwhile, an online adaptive updating strategy is adopted to adjust the monitoring models with the accumulation of new successful batches, which can enhance the reliability of monitoring results.(3). Under the influence of various factors, batch processes commonly involve normal slow variations over batches. For slow-varying batches, a phase-based statistical modeling and nonline monitoring algorithm is developed using between-batch "relative changes". It extracts and models the statistical rules and evolving characteristics of slow-varying behaviors from the between-batch difference trajectory that represents the batch-wise relative changes, which thus accommodates the slow-varying mode into the initial monitoring models. In this way, it endows the initial monitoring system with adaptive competency to batch-to-batch slow-varying behaviors, avoids the online updating requirement and enhances the robustness of monitoring models.(4). For phase-type quality, which is only determined by one or several specific phases and has no close relationship with others, a phase-based PLS regression modeling algorithm is developed for quality analysis and prediction. On the one hand, it identifies the critical-to-quality phases and key predictors from an overall phase viewpoint, and then develops phase-based PLS models for online quality prediction. On the other hand, it analyzes the average effects of process behaviors in each phase on quality based on phase-specific average trajectory and thus develops a stabler quality prediction relationship.(5). For process-type quality, which depends upon all phases within the batch operation, it further develops phase-based quality analysis strategy to comprehend and analyze the local and cumulative effects of various phases on quality more detailedly. On the one hand, it quantitatively extracts the critical-to-quality feature information within each phase and the part of quality attribute interpreted by each local phase, which, thus, enhances the causal relationship between them. On the other hand, it captures the overall effects of the whole process on quality interpretation and prediction by stacking the different local effects of various phases.Phase-based multivariate statistical analysis can more detailedly reveal the process operation status and the changes of underlying characteristics over different phases, which will help to improve the online monitoring performance. Moreover, it can further comprehend the causal relationship between process variables and quality attribute, and explore the different effects of various phases on quality, which will be of great benefit to quality interpretation and prediction. The successful applications to batch process systems and simulation experiments demonstrate the effectiveness of the present methods, which, thus, enrich the achievement of statistical modeling, online monitoring and quality prediction for batch processes and also suggest the necessarity and potential of further research.