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Switching probabilistic slow feature extraction for semisupervised industrial inferential modeling
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DOI：10.1016/j.jprocont.2024.103277.png)
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Abstract
En 中文
Predicting quality-relevant process variables is of paramount importance in optimizing and controlling chemical processes. Probabilistic Slow Feature Analysis (PSFA), a potent data-driven technique, plays a pivotal role in deducing quality indices by abstracting gradual variations in processes distinctly characterized by pronounced inertia. Nevertheless, PSFA's predictive efficacy encounters a substantial bottleneck due to the assumption of a single operating condition, compromising its accuracy, particularly in industries represented by switching operating conditions. To surmount this limitation, this study proposes an innovative approach that enriches PSFA with multi-operating condition process data and limited labels within a Bayesian framework, effectively combining continuous and discrete first-order Markov chains to capture the processes' inertia and dynamic shifts. The proposed method updates latent posterior distributions and model parameters iteratively via the Expectation-Maximization algorithm. The effectiveness of the proposed methodology is verified through a numerical case and industrial hydrocracking process data.
Keywords:
Expectation-maximization algorithm
Industrial hydrocracking process
Multimode process
Probabilistic slow feature analysis
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