The focus of this work is on Statistical Process Control (SPC) a manufacturing process based available measurements. Two important applications SPC in industrial settings are fault detection and diagnosis (FDD). In work, deep learning (DL) methodology proposed for FDD. We investigate the application an explainability concept (explainable artificial intelligence (XAI)) to enhance FDD accuracy ne...

The Tennessee Eastman (TE) process, created by Eastman Chemical Company, is a complex nonlinear process. Many previous studies focus on the detectability of monitoring a multivariate process by using TE process as an example. Principal component analysis (PCA) is a widely used dimension-reduction tool for monitoring multivariate linear process. Recently, the kernel principal component analysis ...

Complex process plants increasingly appear in modern chemical industry due to the considerable economic efficiency that complex and interactive process designs can offer. Interactions between process units (e.g., material recycle and energy integration) often cause significant difficulties in plantwide control. As such, it is important to study plantwide operability (i.e., whether a plantwide p...

The need of designing decentralized control loops emerges to ensure the global stability of a given process plant. To that purpose, the authors have proposed in recent works a systematic approach to derive robust decentralized controllers, which is based on the link between thermodynamics and passivity theory as well as on the fundamentals of process networks. This Thermodynamic-Based Control (...

-This paper proposes a new method for online identification of a nonlinear system using RKHS models. The RKHS model is a linear combination of kernel functions applied to the used training set observations. For large datasets, this kernel based to severs computational problems and makes identification techniques unsuitable to the online case. For instance, in the KPCA scheme the Gram matrix ord...

Adaptation of network weights using LevenbergMarquardt (LM) training algorithm was proposed as a mechanism to improve the performance of Artificial Neural Networks (ANN) in modeling the Tennessee Eastman (TE) chemical process reactor. A Neural Network of the AutoRegressive, eXternal (NNARX) input model was developed. Four sub-models for the TE reactor were built. They are: the reactor level, th...

The Tennessee Eastman (TE) Control Challenge proposed by Downs and Vogel [1] is a test bed problem for use in evaluating advanced process control methodologies from a plant-wide perspective. The dynamic model for the process (based on an actual industrial process) integrates the operation of five unit operations; viz. an exothermic, two-phase reactor, a partial condenser, a centrifugal compress...

The Tennessee Eastman Challenge Process (TECP) represents an interesting case study within PSE for process control and process optimization purposes. It has been widely addressed by the chemical engineering research community since its publication. The TECP is an open-loop unstable recycle reactor. The eigenvalues of the Jacobean matrix of the system at the base-case steady-state conditions, ra...

This paper focuses on the Tennessee Eastman (TE) process and for the first time investigates it in a cognitive way. The cognitive fault diagnosis does not assume prior knowledge of the fault numbers and signatures. This approach firstly employs deterministic reservoir models to fit the multiple-input and multiple-output signals in the TE process, which map the signal space to the (reservoir) mo...