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 ...

Ž . Principal component analysis PCA is a well-known data dimensionality technique that has been used to detect faults Ž . during the operation of industrial processes. Dynamic principal component analysis DPCA and canonical variate analysis Ž . CVA are data dimensionality techniques which take into account serial correlations, but their effectiveness in detecting faults in industrial processes...

Handling missing values and large-dimensional features are crucial requirements for data-driven fault diagnosis systems. However, most intelligent diagnostic systems not able to handle data. The presence of high-dimensional feature sets can also further complicate the process diagnosis. This paper aims devise a data imputation unit along with dimensionality reduction in pre-processing module sy...

Abstract The partial least squares (PLS) is a commonly applied multi-variate method in anomaly detection problems. PLS strategy has been amalgamated with $$T^{2}$$ T 2 and squared prediction error (SPE) based statistical indicators to detect anomalies process. These tra...

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 (...

Hypertoxic materials make it critical to ensure the safety of fluorochemical engineering processes. This mainly depends on over maintenance or manual operations due lack precise models and mechanism knowledge. To quantify deviations operating variables product quality from their target values at same time overcome measurement delay quality, a novel integrated fuzzy inference system (QFIS) was p...

This paper proposes a fault diagnosis method based on an improved residual network (ResNet) for complex chemical processes. The can automatically and efficiently extract features from the extensive data generated by operation process. improvement is carried out in three aspects. Firstly, 1D convolution introduced construction of model to reduce number parameters training time, shortcut connecti...

Fault diagnosis is a pre-requisite for ensuring safe, efficient and optimal operation of chemical process plants. The success of any diagnosis strategy depends critically on the sensors measuring the process variables. With potentially many sensor locations, sensor placement can be optimized based on criteria like cost, reliability etc. We present formulations to perform sensor reallocation and...

During the past few decades, a number of methods for selection of input-output pairings for decentralized control have been proposed. Most of these available methods require evaluation of every alternative in order to find the optimal pairings. As the number of alternatives grows rapidly with problem size, pairing selection through an exhaustive search can be computationally forbidding for larg...