Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes

The focus of this work is on Statistical Process Control (SPC) of a manufacturing process based on available measurements. Two important applications of SPC in industrial settings are fault detection and diagnosis (FDD). In this work a deep learning (DL) based methodology is proposed for FDD. We investigate the application of an explainability concept to enhance the FDD accuracy of a deep neural network model trained with a data set of relatively small number of samples. The explainability is quantified by a novel relevance measure of input variables that is calculated from a Layerwise Relevance Propagation (LRP) algorithm. It is shown that the relevances can be used to discard redundant input feature vectors/ variables iteratively thus resulting in reduced over-fitting of noisy data, increasing distinguishability between output classes and superior FDD test accuracy. The efficacy of the proposed method is demonstrated on the benchmark Tennessee Eastman Process.Comment: Under Review. arXiv admin note: text overlap with arXiv:2012.0386

Robust Inferential Control for a Packed-Bed Reactor

An inferential robust control technique is applied to an experimental fixed bed reactor. The controlled variables are the exit concentration and the maximum bed temperature. Both controlled variables are inferred from one single temperature measurement. The location of this measurement is selected to optimize the performance of the closed-loop system when model uncertainty is allowed. Closed-loop experiments are conducted to test the robustness characteristics of the controller. From these experiments, the operating regions most sensitive to modelling uncertainty are determined. The nonlinear system characteristics can cause significant offset in the inferred controlled variables

On the use of physical boundary conditions for two-phase flow simulations: Integration of control feedback

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.compchemeng.2018.08.012 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/The sensitivity of two-phase flow simulations using the Euler–Euler model on the inlet boundary conditions (BCs) is studied. Specifically, the physical relevance of Dirichlet uniform inlet velocity BCs is studied which are widely used due their simplicity and the lack of a priori knowledge of the slip velocity between the phases. It is found that flow patterns obtained with the more physically realistic uniform inlet pressure BCs are radically different from the results obtained with Dirichlet inlet velocity BCs, refuting the argument frequently put forward that Dirichlet uniform inlet velocity BCs can be interchangeably used because the terminal slip velocity is reached after a short entrance region. A comparison with experimental data is performed to assess the relevance of the flows obtained numerically. Additionally, a multivariable feedback control method is demonstrated to be ideal for enforcing desired flow rates for simulations using pressure BCs.Natural Sciences and Engineering Research Council of Canad

Identification of Uncertainty Bounds for Robust Control with Applications to a Fixed Bed Reactor

A model-based robust controller is designed for a packed bed methanation reactor. To accomplish this objective, model uncertainty bounds are identified from experimental data. A physically motivated methodology of "regions mapping" was developed to compute the uncertainty bounds in the complex plane. This technique is compared to other existing nonparametric approaches for a simple nonlinear system and is shown to produce a more accurate description of the model uncertainty for the purpose of robust control design. This "regions-mapping" approach is then applied to a fixed bed reactor and uncertainty bounds are computed. A robust controller with a single adjustable parameter is designed for the reactor using internal model control (IMC) theory. The computed uncertainty bounds are experimentally validated using the IMC controller

Identification of Uncertainty Bounds for Robust Control with Applications to a Fixed Bed Reactor

A model-based robust controller is designed for a packed bed methanation reactor. To accomplish this objective, model uncertainty bounds are identified from experimental data. A physically motivated methodology of "regions mapping" was developed to compute the uncertainty bounds in the complex plane. This technique is compared to other existing nonparametric approaches for a simple nonlinear system and is shown to produce a more accurate description of the model uncertainty for the purpose of robust control design. This "regions-mapping" approach is then applied to a fixed bed reactor and uncertainty bounds are computed. A robust controller with a single adjustable parameter is designed for the reactor using internal model control (IMC) theory. The computed uncertainty bounds are experimentally validated using the IMC controller

Identification of Uncertainty Bounds for Robust Control with Applications to a Fixed Bed Reactor

A model-based robust controller is designed for a packed bed methanation reactor. To accomplish this objective, model uncertainty bounds are identified from experimental data. A physically motivated methodology of "regions mapping" was developed to compute the uncertainty bounds in the complex plane. This technique is compared to other existing nonparametric approaches for a simple nonlinear system and is shown to produce a more accurate description of the model uncertainty for the purpose of robust control design. This "regions-mapping" approach is then applied to a fixed bed reactor and uncertainty bounds are computed. A robust controller with a single adjustable parameter is designed for the reactor using internal model control (IMC) theory. The computed uncertainty bounds are experimentally validated using the IMC controller