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| LEADER |
01844nam a2200229Ia 4500 |
| 001 |
10.3390-pr10050909 |
| 008 |
220706s2022 CNT 000 0 und d |
| 020 |
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|a 22279717 (ISSN)
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| 245 |
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|a Causal Network Structure Learning Based on Partial Least Squares and Causal Inference of Nonoptimal Performance in the Wastewater Treatment Process
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| 260 |
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|b MDPI
|c 2022
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| 856 |
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|z View Fulltext in Publisher
|u https://doi.org/10.3390/pr10050909
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| 520 |
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|a Due to environmental fluctuations, the operating performance of complex industrial processes may deteriorate and affect economic benefits. In order to obtain maximal economic benefits, operating performance assessment is a novel focus. Therefore, this paper proposes a whole framework from operating performance assessment to nonoptimal cause identification based on partial-least-squares-based Granger causality analysis (PLS-GC) and Bayesian networks (BNs). The proposed method has three main contributions. First, a multiblock operating performance assessment model is established to correspondingly extract economic-related information and dynamic information. Then, a Bayesian network structure is established by PLS-GC that excludes the strong coupling of variables and simplifies the network structure. Lastly, nonoptimal root cause and and nonoptimal transmission path are identified by Bayesian inference. The effectiveness of the proposed method was verified on Benchmark Simulation Model 1. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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| 650 |
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|a Bayesian network
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| 650 |
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|a Granger causality analysis
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| 650 |
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|a nonoptimal cause identification
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| 650 |
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|a partial least squares
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| 700 |
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|a Cheng, H.
|e author
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| 700 |
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|a Peng, X.
|e author
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|a Wang, Y.
|e author
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| 700 |
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|a Yang, D.
|e author
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| 700 |
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|a Zhong, W.
|e author
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| 773 |
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|t Processes
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