Mudassir M. Rashid

A non-Gaussian pattern matching based dynamic process monitoring approach and its application to cryogenic air separation process

Abstract Principal component analysis (PCA) based pattern matching methods have been applied to process monitoring and fault detection. However, the conventional pattern matching approaches do not specifically take into account the non-Gaussian dynamic features in chemical processes. Furthermore, those techniques are more focused on fault detection instead of fault diagnosis. In this study, a non-Gaussian pattern matching based fault detection and diagnosis method is developed and applied to monitor cryogenic air separation process. First, independent component analysis (ICA) models are built on the normal benchmark and monitored data sets along sliding windows. The IC subspaces from the benchmark and monitored data are then extracted to evaluate the non-Gaussian patterns and detect process faults through a mutual information based dissimilarity index. Further, a difference subspace between the two IC subspaces is computed to characterize the divergence of the dynamic and non-Gaussian patterns between the benchmark and monitored data. Subsequently, the mutual information between the IC difference subspace and each process variable direction is defined as a new non-Gaussian contribution index for fault identification and diagnosis. The presented approach is applied to a simulated cryogenic air separation plant and the monitoring results are compared against those of PCA based pattern matching techniques and ICA based monitoring method. The application study demonstrates that the developed non-Gaussian pattern matching approach can effectively monitor the complex air separation process with superior fault detection and diagnosis capability.

An independent component analysis and mutual information based non-Gaussian pattern matching method for fault detection and diagnosis of complex cryogenic air separation process

The conventional principal component analysis (PCA) based pattern matching methods have been applied to dynamic process monitoring. However, they do not take into account the non-Gaussian features in industrial processes and are also more focused on fault detection instead of fault diagnosis. In this paper, an independent component analysis and mutual information based non-Gaussian pattern matching approach is developed for fault detection and diagnosis of complex chemical processes. The presented approach is applied to a simulated cryogenic air separation process and the application study demonstrates that the developed non-Gaussian pattern matching method can effectively monitor the complex air separation process with strong capability of fault detection and diagnosis.

Handling multi-rate and missing data in system identification

In the present work we consider the problem of subspace-based system identification of batch processes subject to multi-rate and missing data. To this end, we develop a state-space system identification approach for batch processes capable of handling multi-rate and missing data by utilizing the incremental singular value decomposition technique. Simulation case studies involving application to the electric arc furnace process demonstrate the efficacy of the proposed modeling approach compared to traditional identification subject to limited availability of process measurements, missing data and measurement noise.

Economic model predictive control of the electric arc furnace using data-driven multi-rate models

This work considers the problem of economic model predictive control (EMPC) of electric arc furnaces (EAF), subject to the limited availability of process measurements and noise. The key issues addressed are: (1) the multi-rate sampling of process variables; and (2) the requirement of optimized operation that achieves desired product specifications and also minimizes the operating costs. To this end, we identify data-driven models that capture the temporal dynamics of process measurements sampled at different rates. The resulting multi-rate models are used to design a two-tiered predictive controller that enables achieving the target end-point while minimizing the associated costs. The EMPC is implemented on the EAF process and the closed-loop simulation results illustrate the improvement in economic performance over existing trajectory-tracking approaches.

Multi-kernel Gaussian process regression and Bayesian model averaging based nonlinear state estimation and quality prediction of multiphase batch processes

Batch processes are characterized by inherent nonlinearity, multiplicity of operating phases, between-phase transient dynamics and batch-to-batch uncertainty that pose significant challenges for accurate state estimation and quality prediction. Conventional multi-model strategies, however, may be ill-suited for multiphase batch processes because the localized models do not specifically characterize the complex transient dynamics between two consecutive operating phases. In this study, a novel Bayesian model averaging based multi-kernel Gaussian process regression (BMA-MKGPR) approach is proposed for state estimation and quality prediction of nonlinear batch processes with multiple operating phases and between-phase transient dynamics. The new approach is applied to a simulated batch polymerization process and the result comparison shows that it can effective handle multiple nonlinear operating phases, between-phase transient dynamics and process uncertainty with high prediction accuracies.