Hybrid Intelligent Fault Diagnosis Model Based on Improved MPCA-V for Sensors in a Laboratory-Scale Wastewater Treatment Process

作者：Liu, Yin; Niu, Guoqiang; Zhou, Jing; Shen, Wenhao^*; Corriou, Jean-Pierre; Seferlis, Panagiotis

来源：Industrial and Engineering Chemistry Research, 2022, 61(50): 18445-18456.

DOI：10.1021/acs.iecr.2c02334

• BATCH PROCESSES
• PCA
• NETWORK

The study objective is to propose a hybrid fault diagnosis method for a laboratory-scale sequential batch reactor (SBR) wastewater treatment process based on time-varying covariance and variable-wise unfolded MPCA method (MPCA-V), which can detect the fault batch, determine the fault time simultaneously, and further identify the fault source. To establish and validate the MPCA-V model, 50 normal batches and 55 batches including 7 fault batches were employed separately. Furthermore, the classical MPCA (MPCA-B) model was introduced for comparison. For the three detected fault batches, with the MPCA-V model, not only the fault occurring time and fault source were located and identified by the contribution degree calculation of each variable to the T2 and SPE statistics simultaneously but also the fault detection rate was averaged as 90%, which was much higher than that of MPCA-B (67%). Introducing time dependency and correlation in a laboratory-scale SBR process gives the work practical significance and breakthrough.