Hybrid statistical-machine learning ammonia forecasting in continuous activated sludge treatment for improved process control
Hybrid statistical-machine learning ammonia forecasting in continuous activated sludge treatment for improved process control
复制标题
连续活性污泥处理中的混合统计机器学习氨预测，以改进过程控制
DOI：
10.1016/j.jwpe.2020.101389
复制
发表时间：
2020
影响因子：
7
通讯作者：
Hering, Amanda S.
中科院分区：
文献类型：
--
作者：
Newhart, Kathryn B.;Marks, Christopher A.;Rauch-Williams, Tanja;Cath, Tzahi Y.;Hering, Amanda S.
在这项工作中，统计稳定性度量和新的混合算法-机器学习氨氮预测模型的开发，以提高城市污水处理的准确性和精度。曝气生物脱氮是城市污水处理厂最大的能耗。基于氨的曝气控制（ABAC）是一种设计成通过从在线氨测量而不是从溶解氧（DO）传感器调节鼓风机输出来最小化过度曝气的方法，溶解氧传感器是常规曝气控制方法。我们提出了一个定量的稳定性指标，总样本方差，比较系统范围内的竞争曝气控制策略的变化。使用这个指标，传统的DO和ABAC控制策略与不同的设定值和控制参数的性能进行了比较，在一个中型污水处理厂，最稳定的策略被确定和实施的设施。为了进一步提高ABAC的性能，氨预测模型的构建使用统计和机器学习，以提高曝气控制系统的准确性。日，日线性，人工神经网络（ANN），和混合日线性人工神经网络预测模型进行训练的实时工厂范围内的过程数据。日线性和日线性人工神经网络预测被认为是最准确的预测氨;提高现有的氨测量高达32%和46%，分别，而人工神经网络模型预测只能提高8%。这项工作展示了整合统计和机器学习方法的简单性和灵活性，用于在常规污水处理厂中开发新的处理模型，以适应全规模常规活性污泥系统的特点。
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.
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DOI：
--
发表时间：
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期刊：
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