Data-driven process monitoring and fault analysis of reformer units in hydrogen plants: Industrial application and perspectives

Reformer boxes are complex, integrated, and high-temperature units, subject to various failures during continuous operations for extended time periods. Challenges in the development of high-fidelity first principle models, despite easy availability of process measurements motivated the development of data-driven, automated fault detection (FD) systems. Paucity of plant-wide implementation of FD technologies in the chemical industry, accentuates the absence of relevant practical guidelines and best practices. In this paper, a trivially replicable FD system has been developed for large-scale industrial reformer boxes of hydrogen manufacturing units. Actual process data from plant historian has been used for training and validation of a novel model, developed using a combination of partial least squares regression and principal components analysis. Abnormalities based on several important measurements around the reformer were identified. Explicit algorithmic details and insights obtained during development of the expert system have been provided for ease of replication and adaptability. (C) 2020 Elsevier Ltd. All rights reserved.