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Analysis of time-varying cause-effect relations based on qualitative trends and change amplitudes. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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2
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Causal Network Structure Learning Based on Partial Least Squares and Causal Inference of Nonoptimal Performance in the Wastewater Treatment Process. Processes (Basel) 2022. [DOI: 10.3390/pr10050909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
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.
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Sivaram A, Venkatasubramanian V. XAI‐MEG
: Combining symbolic
AI
and machine learning to generate first‐principles models and causal explanations. AIChE J 2022. [DOI: 10.1002/aic.17687] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Abhishek Sivaram
- Complex Resilient Intelligent Systems Laboratory, Department of Chemical Engineering Columbia University New York New York USA
| | - Venkat Venkatasubramanian
- Complex Resilient Intelligent Systems Laboratory, Department of Chemical Engineering Columbia University New York New York USA
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Naef M, Chadha K, Lefsrud L. Decision support for process operators: Task loading in the days of big data. J Loss Prev Process Ind 2022. [DOI: 10.1016/j.jlp.2021.104713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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5
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Reinartz C, Kulahci M, Ravn O. An extended Tennessee Eastman simulation dataset for fault-detection and decision support systems. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107281] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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