Modeling of adaptive multi-output soft-sensors with applications in wastewater treatments

Wu, Jing and Cheng, Hongchao and Liu, Yiqi and Liu, Bin and Huang, Daoping (2019) Modeling of adaptive multi-output soft-sensors with applications in wastewater treatments. IEEE Access, 7. pp. 161887-161898. ISSN 2169-3536 (https://doi.org/10.1109/ACCESS.2019.2950034)

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Abstract

Given the multivariable coupling, strong nonlinearity and time-varying features in the wastewater treatment processes, adaptive strategies, including just-in-time learning (JITL), time difference (TD), and moving window (MW) methods have been chosen in this paper to enhance multi-output soft-sensor models to ensure online prediction for a variety of hard-to-measure variables simultaneously. In the proposed adaptive multi-output soft-sensors, multi-output partial least squares (MPLS), multi-output relevant vector machine (MRVM) and multi-output Gaussian process regression (MGPR) served as the multi-output models. The integration of adaptive strategies and multi-output models not only provides a solution for multi-output prediction, but also offers a potential to alleviate the degradation of multi-output soft-sensors. To further improve the adaptive ability, four adaptive soft-sensors, termed TD-MW, TD-JIT, JIT-MW, and TD-JIT-MW, have been proposed by mixing the three aforementioned adaptive strategies to upgrade multi-output softsensors. All the adaptive multi-output soft-sensors are analyzed and compared in terms of simulation data and practical industrial data, which exhibit stationary and nonstationary behaviors, respectively.

ORCID iDs

Wu, Jing, Cheng, Hongchao, Liu, Yiqi, Liu, Bin ORCID logoORCID: https://orcid.org/0000-0002-3946-8124 and Huang, Daoping;
CORE (COnnecting REpositories)