Fault detection for NOx emission process in thermal power plants using SIP-PCA

With the era of big data, data-driven models are increasingly vital to just-in-time decision support in pollution emission management and planning. This article aims to evaluate the usability of the proposed data-driven model to monitor NOx emission from a coal-fired boiler process using easily measured process variables. As the emission process is highly complex, process variables interact with each other, and they cannot guarantee that all the variables in the actual operation obey the Gaussian distributions. As conventional principal component analysis (PCA) can only extract variance information, a novel data-driven model is proposed, called survival information potential-based PCA (SIP-PCA) model, is proposed in this work. First, an improved PCA model is established based on the SIP performance index. SIP-PCA can extract more information in the latent space from the process variables following the non-Gaussian distributions. Then, the control limits for fault detection are determined based on the kernel density estimation method. Finally, the proposed algorithm is successfully applied to a real NOx emission process. By monitoring the operation of process variables, possible failures can be detected as soon as possible. Fault isolation and system reconstruction can be implemented in time, preventing NOx emissions from exceeding its standard.(c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

With the era of big data, data-driven models are increasingly vital to just-in-time decision support in pollution emission management and planning. This article evaluates the usability of a proposed data-driven model for monitoring NOx emissions from a coal-fired boiler process using easily measured process variables. The proposed SIP-PCA model extracts more information from non-Gaussian distributed process variables and enables real-time detection of possible failures to prevent excessive NOx emissions.