This article is concerned with determination of the underlying source of problematic control performance through a data-driven Bayesian approach. This approach synthesizes information from different monitoring algorithms to isolate possible problem sources. A main issue encountered in the application of the data-driven approach is the problem of missing data or missing monitor reading. By introducing the concept of missing pattern, data missing problems are classified into single and multi missing patterns. A novel method based on marginalization over underlying complete evidence matrix is proposed to circumvent missing data problems. Performance of the proposed Bayesian approach is examined through simulations as well as an industrial application example to verify its ability of information synthesis. V V C 2009 American Institute of Chemical Engineers AIChE J, 56: 179-195, 2010

Advanced process control (APC)in particular, model predictive control (MPC)has emerged as the most effective control strategy in process industry, and numerous applications have been reported. Nevertheless, there are several factors that limit the achievable performance of MPC. One of the limiting factors considered in this paper is the presence of constraints. To exploit optimal control performance, continuous performance assessment, with respect to the constraints of MPC, is necessary. MPC performance assessment has received increasing interest, both in academia and in industry. This paper is concerned with a practical aspect of performance assessment of industrial MPC by investigating the relationship among process variability, constraints, and probabilistic economic performance of MPC. The proposed approach considers the uncertainties induced by process variability and evaluates the economic performance through probabilistic calculations. It also provides a guideline for the constraint tuning, to improve MPC performance.

Performance assessment of model predictive control (MPC) systems has been focusing on evaluation of the variability with, for example, minimum variance or LQG/MPC tradeoff curve as benchmarks. These previous studies are mainly concerned with the dynamic performance of MPC. However, the benefit of MPC is largely attributed to its capability for economic optimization. The economic performance, on the other hand, is also dependent on the variability reduction achieved through dynamic control. There is a need to assess MPC performance by considering economic performance, variability reduction, and their relationships. One of the good indications of this relation is the constraint tuning. In practical MPC applications, the constraint setups are important whenever an MPC is commissioned, and constraint tunings are not uncommon, even when the MPC is already on-line. Thus, the questions to ask are which constraints should be adjusted, and what is the benefit to do so? By investigating the relationship between variability and constraints, problems of interest are solved under the Bayesian inference framework (namely, through the Bayesian approach for decision evaluation and decision-making). The decisions that are referenced are whether to tune the constraints to achieve the optimal economic MPC performance and which constraints should be tuned. A detailed case study for a distillation column MPC application is provided to illustrate the proposed performance assessment methods.

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