The prediction of anaerobic digestion (AD) performance using numerical models, which are based on mathematics and kinetics, is being challenged by poor mechanistic understanding and the non-linear relationships between performance and operational parameters. This study demonstrated that various machine learning (ML) models using the 1-step ahead with the retraining method, which utilized AD performance data from prior states, can improve the prediction accuracy of ML models. For the four types of ML models studied, the 1-step ahead with the retraining method could improve the root mean square errors by 32–49% compared to the conventional multi-step ahead method, which was particularly noteworthy during the transition period when AD reactors were faced with loading shocks and showed inhibited methane yields. Moreover, the 1-step ahead with the retraining method showed the potential of achieving accurate predictions using a single input parameter, pH, which was considerably less labor-intensive to monitor than the other parameters often required in AD models (e.g., VSS). As such, the 1-step ahead with retraining method is suitable for efficient real-time prediction of AD performance in real-world operations.

中文翻译：

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重新训练厌氧消化的先前状态性能可以提高各种机器学习模型中甲烷产量的预测准确性


使用基于数学和动力学的数值模型预测厌氧消化 （AD） 性能，正受到对机理理解不足以及性能与操作参数之间非线性关系的挑战。这项研究表明，使用先一步再训练方法的各种机器学习 （ML） 模型，利用先前状态的 AD 性能数据，可以提高 ML 模型的预测准确性。对于所研究的四种类型的 ML 模型，与传统的多步前进方法相比，使用再训练方法提前 1 步可以将均方根误差提高 32-49%，这在 AD 反应器面临负载冲击并显示出甲烷产量受到抑制的过渡期间尤为值得注意。此外，再训练方法领先 1 步显示了使用单个输入参数 pH 实现准确预测的潜力，与 AD 模型中通常需要的其他参数（例如 VSS）相比，该参数的监测劳动强度要低得多。因此，1-step ahead with retraining 方法适用于在实际操作中对 AD 性能进行高效实时预测。