In this work, a statistical stability metric and novel hybrid statistical-machine learning ammonia forecasting model are developed to improve the accuracy and precision of municipal wastewater treatment. Aeration for biological nutrient removal is typically the largest energy expense for municipal wastewater treatment plants (WWTP). Ammonia-based aeration control (ABAC) is one approach designed to minimize excessive aeration by adjusting air blower output from online ammonia measurements rather than from a dissolved oxygen (DO) sensor, which is the conventional aeration control approach. We propose a quantitative stability metric, Total Sample Variance, to compare system-wide variability of competing aeration control strategies. Using this metric, the performance of traditional DO and ABAC control strategies with varying setpoints and control parameters were compared in a medium-sized WWTP, and the most stable strategy was identified and implemented at the facility. To further improve ABAC performance, ammonia forecasting models were constructed using both statistical and machine learning to improve the accuracy of the aeration control system. Diurnal, diurnal-linear, artificial neural network (ANN), and hybrid diurnal-linear-ANN forecasting models were trained on real-time plant-wide process data. The diurnal-linear and diurnal-linear-ANN forecasts were found to most accurately forecast ammonia; improving upon the existing ammonia measurement by up to 32% and 46%, respectively, whereas the ANN model forecast was only able to improve by up to 8%. This work demonstrates the ease and flexibility of integrating statistics and machine learning methods for developing new treatment models in conventional WWTP for features in full-scale conventional activated sludge systems.

在这项工作中，统计稳定性指标和新型混合统计机器学习氨预测模型被开发出来，以提高市政废水处理的准确性和精度。去除生物营养物的曝气通常是市政废水处理厂（WWTP）的最大能源消耗。基于氨的曝气控制（ABAC）是一种设计方法，可通过调整在线在线氨气测量而不是传统的曝气控制方法从溶解氧（DO）传感器输出的鼓风机输出来最大程度地减少过度曝气。我们提出了定量稳定性指标，总样本差异，以比较竞争性曝气控制策略在系统范围内的可变性。使用此度量标准，比较了中型污水处理厂中具有不同设定值和控制参数的传统DO和ABAC控制策略的性能，并在工厂确定并实施了最稳定的策略。为了进一步提高ABAC的性能，使用统计和机器学习方法构建了氨气预测模型，以提高曝气控制系统的准确性。日间，日间线性，人工神经网络（ANN）和混合日间线性ANN预测模型是在全厂实时过程数据上进行训练的。发现日线性和日线性ANN预报可最准确地预测氨。现有氨气测量值分别提高了32％和46％，而ANN模型的预测最多只能提高8％。这项工作展示了集成统计数据和机器学习方法以在常规WWTP中开发新的处理模型以实现大规模常规活性污泥系统功能的便捷性和灵活性。