arXiv cs.AI· Rohit Murali, Benaissa Dekhici, Tao Chen, Dongda Zhang, Michael Short·· 14 小时前AI 评分18
基于 LSTM-beta-VAE 生成数据增强的多输出 LSTM 软测量用于工业厌氧共消化
Multi-output LSTM soft sensors for industrial anaerobic co-digestion using LSTM-beta-VAE generative data augmentation
AI 导读
研究提出 LSTM-beta-VAE 生成式数据增强框架结合多输出 LSTM（MO-LSTM）软测量，用一年全规模工厂数据合成不稳定运行序列，填补稀疏特征空间。基线评估显示数据稀缺使 Random Forest、XGBoost 和未增强 LSTM 无法识别向不稳定运行的转变，稳定性指标估计较差；在增强数据上训练的 MO-LSTM 实现了所有目标的同时稳健估计。
正文 · 原文
Abstract:Anaerobic digestion (AD) requires close monitoring of key process variables, including pH, the ratio of volatile organic acids to alkaline buffer capacity and biogas production, to maintain biological stability and prevent process failure. While mechanistic frameworks such as the Anaerobic Digestion Model No. 1 are highly parameterised, computationally expensive, and difficult to calibrate for real-time applications, data-driven Long Short-Term Memory (LSTM) networks offer a potentially more adaptable alternative. However, current data-driven approaches in AD are predominantly limited to single-output architectures that focus solely on production yields, as opposed to simultaneous prediction of vital stability indicators. Furthermore, industrial AD datasets are typically sparse and skewed toward steady-state operations. Baseline evaluations demonstrated this scarcity prevents standard algorithms (Random Forest, XGBoost) and unaugmented LSTMs from recognising the transition to unstable operations, resulting in poor estimation of stability indicators. To overcome these challenges, this study proposes a novel generative data augmentation framework using an LSTM-beta-Variational Autoencoder coupled with a multi-output LSTM (MO-LSTM) soft sensor. By synthesising physically valid sequences of these transitions to unstable operations from one year of full-scale plant data, the method safely populates sparse regions of the feature space. Trained on this augmented dataset, the MO-LSTM achieved robust simultaneous estimation across all targets. This approach mitigates data sparsity, providing a basis for hybrid mechanistic-data-driven modelling and early-warning soft sensors in industrial bioprocesses.
| Comments: | 33 pages |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2610.39768 [cs.LG] |
| (or arXiv:2610.39768v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39768 arXiv-issued DOI via DataCite |
Submission history
From: Michael Short [view email]
[v1]
Thu, 29 Oct 2026 10:18:56 UTC (2,125 KB)
来源：arXiv cs.AI · arxiv.org