This paper presents the prototype of an advanced platform for production analysis and optimization, referred to as ProOpter. The platform was developed to support the recently derived concept of holistic production control (HPC), which relies on model-based control. The prototype is comprised of a set of offline and on-line modules. The off-line modules support the definition of key performance...

Effective monitoring of industrial processes provides many benefits. However, for dynamic processes with strong nonlinearity many existing techniques still cannot give satisfactory monitoring performance. This is evidenced by the well known Tennessee Eastman (TE) benchmark process, where some faults, e.g. Faults 3 and 9, have not been comfortably detected by almost all data-driven approaches pu...

To overcome the disadvantage of linear dissimilarity analysis (DISSIM) when monitoring nonlinear processes, a kernel dissimilarity analysis algorithm, termed KDISSIM here, is presented, which is the nonlinear version of DISSIM algorithm. A kernel dissimilarity index is introduced to quantitatively evaluate the differences between nonlinear data distribution structures, which can reflect the cha...

Root cause analysis is an important method for fault diagnosis when used with multivariate statistical process monitoring (MSPM). Conventional contribution analysis in MSPM can only isolate the effects of the fault by pinpointing inconsistent variables, but not the underlying cause. By integrating reconstruction-based multivariate contribution analysis (RBMCA) with fuzzy-signed directed graph (...

Qualitative trend analysis (QTA) is a process-history-based data-driven technique that works by extracting important features (trends) from the measured signals and evaluating the trends. QTA has been widely used for process fault detection and diagnosis. Recently, Dash et al. (2001, 2003) presented an intervalhalving-based algorithm for off-line automatic trend extraction from a record of data...

In this work we describe a conceptual approach which has been recently developed to design efficient decentralized control structures for flexible chemical plant operation. The approach combines concepts from thermodynamics, inventory networks and process control to construct a set of stable and robust decentralized control candidate structures. Such candidates consist of inventory control loop...

Identification of faulty variables is an important component of multivariate statistical process monitoring (MSPM); it provides crucial information for further analysis of the root cause of the detected fault. The main challenge is the large number of combinations of process variables under consideration, usually resulting in a combinatorial optimization problem. This paper develops a generic r...

This thesis is about the multivariate process monitoring (detection and diagnosis) withbayesian networks. It allows to unify in a same tool (a bayesian network) some monitoring ded-icated methods like multivariate control charts, discriminant analysis and the MYT method.The first chapter gives some essential points of the process monitoring, with a study of differ-ent approa...