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Mohd Azlan Huzzain
Department of Chemical Engineering, Faculty of Engineering. University of Malaya, 50603 Kuala Lumpur, Malaysia
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry

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A Two-Step Fault Detection and Diagnosis Framework for Chemical Processes Lau Chee Kong; Che Rosmani; Che Hasan; Mohd Azlan Huzzain
ASEAN Journal of Chemical Engineering Vol 10, No 2 (2010)
Publisher : Department of Chemical Engineering, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1549.49 KB) | DOI: 10.22146/ajche.50083

Abstract

An effective process monitoring system serves as an early warning system for influences affecting the chemical plant and helps plant operator to devise remedial actions to mitigate the adverse effects. However, the design of such system presents challenges such as complex cause-effect correlations, imprecise process model and novelty identifiability. In this work, a two-step fault detection and diagnosis framework is presented. This framework utilizes boundary models developed from mass and energy balances for each section of the chemical plant. The fault detection step consists of a fuzzy inference system (FIS) to analyze the balances and identify the faulty section if the balances deviate from the normal boundary. Then, multiple adaptive neuro-fuzzy inference system (ANFIS) classifiers are constructed to diagnose the exact root causes of bad performance. The combination of boundary models and FIS provides fault isolation of the faulty plant section even when novel faults have occurred. Utilization of multiple ANFIS classifiers reduces the complexity of the networks and improves the proficiency of the process monitoring system. The proposed scheme is applied on a model of a large scale industrial process.
Co-Authors Che Hasan Che Rosmani Lau Chee Kong