Water treatment processes are known to consume substantial amounts of energy, making it crucial to understand their efficiency, drivers, and potential energy savings. In this study, we apply Efficiency Analysis Tree (EAT), which combines machine learning and linear programming techniques to assess the energy performance of 146 Chilean drinking water treatment plants (DWTPs) for 2020. Additionally, we utilize bootstrap regression techniques to examine the influence of operating characteristics on energy efficiency. The results indicate that the evaluated DWTPs exhibited poor energy performance, with an average energy efficiency score of 0.197. The estimated potential energy savings were found to be 0.005 kWh/m³. Several factors, such as the age of the facility, source of raw water, and treatment technology, were identified as significant drivers of energy efficiency in DWTPs. The insights gained from our study can be valuable for policymakers in making informed decisions regarding the adoption of practices that promote efficient and sustainable energy use within the water cycle.

众所周知，水处理过程会消耗大量能源，因此了解其效率、驱动因素和潜在的节能潜力至关重要。在本研究中，我们应用效率分析树 (EAT) 结合机器学习和线性规划技术来评估 146 个智利饮用水处理厂 (DWTP) 2020 年的能源绩效。此外，我们利用引导回归技术来检查影响运行特性对能源效率的影响。结果表明，所评估的分布式污水处理厂的能源绩效较差，平均能源效率得分为0.197。估计潜在的节能效果为 0.005 kWh/m ³。设施的使用年限、原水来源和处理技术等多个因素被认为是分布式污水处理厂能源效率的重要驱动因素。从我们的研究中获得的见解对于政策制定者在采取促进水循环中高效和可持续能源利用的做法方面做出明智的决策非常有价值。