ArticleACS omega2026
Online Sensor Fault Detection Using Machine Learning Algorithms on a Laboratory-Scale Batch Reactor: LSTM Approach.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
6 authors.
Funding
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Abstract
This article presents an online fault detection system for a laboratory-scale batch reactor (BR) using the Convolutional Neural Network (CNN)-Squeeze and Excitation-based Improved Multi-Layer Long Short-Term Memory (CS-IMLSTM) model. To identify superimposed and sparse sensor faults in real time, the system continuously monitors the BR parameters, such as reactor temperature, coolant flow rate, and heater current. To reduce noise and dynamic fluctuations, the CS-IMLSTM integrates a channel-spatial attention mechanism and enhances feature significance. The performance of the proposed model is compared with LSTM and CNN-LSTM models. The results indicate that the CS-IMLSTM demonstrates improved accuracy, faster adaptation of online learning, and effectiveness in identifying abnormal circumstances compared with LSTM and CNN-LSTM models. The proposed approach can be used for intelligent predictive maintenance in dynamic industrial environments to enhance the reliability and safety of chemical process operations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.