Interest in anaerobic co-digestion (AcoD) has increased significantly in recent decades owing to enhanced biogas productivity due to the utilization of different organic wastes, such as food waste and sewage sludge. In this study, a robust AcoD model for biogas prediction is developed using deep learning (DL). We propose a hybrid DL architecture, i.e., DA–LSTM–VSN, wherein a dual-stage-attention (DA)-based long short-term memory (LSTM) network is integrated with variable selection networks (VSNs). To enhance the model predictability, we perform hyperparameter optimization. The model accuracy is validated using long-term AcoD monitoring data measured over two years of municipal wastewater treatment plant operation and then compared with those of two other DL-based models (i.e., DA–LSTM and the standard LSTM). In addition, the feature importance (FI) is analyzed to investigate the relative contribution of input variables to biogas production prediction. Finally, we demonstrate the successful application of the validated DL model to the AcoD process optimization. Results show that the model accuracy improved significantly by incorporating DA into LSTM, i.e., the coefficient of determination (R²) increased from 0.38 to 0.68; however, the R² can be further increased to 0.76 by combining DA–LSTM with a VSN. For the biogas prediction of the AcoD model, the VSN contributes significantly by employing the discontinuous time series of measurement data on biodegradable organic-associated variables during AcoD. In addition, the VSN allows the AcoD model to be interpretable via FI analysis using its weighted input features. The FI results show that the relative importance is vital to variables associated with food waste leachate, whereas it is marginal for those associated with the primary and chemically assisted sedimentation sludges. In conclusion, the AcoD model proposed herein can be utilized in practical applications as a robust tool because it can provide the optimal sludge conditions to improve biogas production. This is because it facilitates the time-series biogas prediction at the full scale using unprocessed datasets with either missing value imputation or outlier removal.

近几十年来，由于利用不同的有机废物（如食物垃圾和污水污泥）提高了沼气生产率，对厌氧共消化 (AcoD) 的兴趣显着增加。在这项研究中，使用深度学习 (DL) 开发了用于沼气预测的强大 AcoD 模型。我们提出了一种混合 DL 架构，即 DA-LSTM-VSN，其中基于双阶段注意 (DA) 的长短期记忆 (LSTM) 网络与变量选择网络 (VSN) 相结合。为了增强模型的可预测性，我们进行了超参数优化。该模型的准确性使用在城市污水处理厂运行两年内测量的长期 AcoD 监测数据进行验证，然后与其他两个基于 DL 的模型（即 DA-LSTM 和标准 LSTM）的数据进行比较。此外，分析特征重要性 (FI) 以研究输入变量对沼气生产预测的相对贡献。最后，我们展示了经过验证的 DL 模型在 AcoD 过程优化中的成功应用。结果表明，通过将 DA 纳入 LSTM，即决定系数（R²）从0.38增加到0.68；然而，R ²通过将 DA-LSTM 与 VSN 相结合，可以进一步增加到 0.76。对于 AcoD 模型的沼气预测，VSN 通过在 AcoD 期间使用有关可生物降解有机相关变量的测量数据的不连续时间序列做出显着贡献。此外，VSN 允许使用其加权输入特征通过 FI 分析来解释 AcoD 模型。FI 结果表明，相对重要性对于与食物垃圾渗滤液相关的变量至关重要，而对于与初级和化学辅助沉淀污泥相关的变量则微不足道。总之，本文提出的 AcoD 模型可以在实际应用中用作强大的工具，因为它可以提供最佳污泥条件以提高沼气产量。