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arXiv AI By Rohit Murali, Benaissa Dekhici, Tao Chen, Dongda Zhang, Michael Short

Multi-output LSTM soft sensors for industrial anaerobic co-digestion using LSTM-beta-VAE generative data augmentation

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The paper presents a novel approach to monitoring industrial anaerobic co‑digestion by combining a generative LSTM‑beta‑VAE with a multi‑output LSTM soft sensor. By synthesising physically valid sequences of unstable operation from a year of plant data, the method augments sparse datasets and enables simultaneous prediction of key stability indicators such as pH, volatile acid ratio, and biogas production. The augmented model outperforms standard algorithms and unaugmented LSTMs, offering robust early‑warning capabilities for bioprocess stability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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