Biogas yield in anaerobic digestion (AD) involves continuous and complex biological reactions. The traditional linear models failed to quantitatively assess the interactive effects of these factors on AD performance. To further explore the internal relationship between target variables and AD performance, this study developed four machine learning models to predict biogas yield and consider the interaction among various factors. Results indicated that the highest prediction accuracy of AD performance was achieved by adding bacterial genera dataset with environmental factors. Random forest model exhibited the highest accuracy, with the testing coefficient of determination equal to 0.9879. Among two types of input features, the bacterial genera accounted for 89.9 % of the impact on biogas yield, followed by environmental factors. The results revealed Keratinibaculum and Acetomicrobium as critical bacteria. The volatile fatty acid controlled below 2000 mg/L and the improved stirring system in AD process were recommended to achieve maximum biogas yield.

中文翻译：

---

探索环境和微生物因素对食物垃圾厌氧消化性能的交互影响：可解释的机器学习模型


厌氧消化 （AD） 中的沼气产量涉及连续而复杂的生物反应。传统的线性模型无法定量评估这些因素对 AD 性能的交互影响。为了进一步探索目标变量与 AD 性能之间的内部关系，本研究开发了四种机器学习模型来预测沼气产量并考虑各种因素之间的相互作用。结果表明，通过添加具有环境因子的细菌属数据集，AD 性能的预测精度最高。随机森林模型的准确性最高，检验决定系数等于 0.9879。在两种类型的输入特征中，细菌属占对沼气产量影响的 89.9 %，其次是环境因素。结果显示 Keratinibaculum 和 Acetomicrobium 是关键细菌。建议在 AD 过程中使用控制在 2000 mg/L 以下的挥发性脂肪酸和改进的搅拌系统，以实现最大的沼气产量。