Monitoring of papermaking wastewater treatment processes using t-distributed stochastic neighbor embedding. Issue 6 (December 2021)

• Record Type: Journal Article
• Authors: Ma, Xiaobo; Zhang, Yuchen; Zhang, Fengshan; Liu, Hongbin
• Is Part Of: Journal of environmental chemical engineering. Volume 9:Issue 6(2021)
• Publication Date: 2021-12
• Subjects: Wastewater treatment processes -- t-distribution stochastic neighbor embedding -- Gaussian mixture model -- Fault detection; Chemical engineering -- Environmental aspects -- Periodicals; Environmental engineering -- Periodicals; Chemical engineering -- Environmental aspects; Environmental engineering; Periodicals; 660.0286
• DOI: 10.1016/j.jece.2021.106559
• Languages: English
• ISSNs: 2213-2929

Abstract

Abstract: A combination of t-distribution stochastic neighbor embedding with a Gaussian mixture model (t-SNE-GMM) is proposed for sensor fault detection in wastewater treatment processes. The proposed method can be used to handle the non-Gaussian and nonlinear characteristics of wastewater treatment processes simultaneously. The t-SNE method is first used to reduce the dimension of process data, and then GMM only uses the normal process data to accomplish fault detection. With manifold learning, the hybrid model can reduce computation complexity and improve detection accuracy. Two methods that combined GMM with principal component analysis (PCA-GMM) and kernel PCA-GMM (KPCA-GMM) are used for comparing with t-SNE-GMM. The fault detection performance was verified by simulating sensor faults in the wastewater treatment process. Among them, the fault detection rates of bias fault, drifting fault, and complete failure fault using t-SNE-GMM are increased by 67.8%, 5%, and 109.52%, respectively, compared with KPCA-GMM. Although the improvement of the fault detection rate of drifting faults is not obvious, it has an excellent performance in the false alarm rate. The combined method has the capability of detecting the sensor faults in the wastewater treatment process. Graphical Abstract: ga1 Highlights: t-SNE-GMM is proposed for sensor fault detection in papermaking wastewater treatment processes. t-SNE is used for dimensionality reduction to improve the accuracy of fault detection. t-SNE-GMM is used to detect sensor faults in a paper manufacture factory. t-SNE-GMM provides best fault detection performance and improves system operation efficiency.