Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling
Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling
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DOI：
10.1021/acsestengg.1c00179
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发表时间：
2021-09-09
影响因子：
7.1
通讯作者：
Cusick, Roland D.
中科院分区：
文献类型：
--
作者：
Li, Shaobin;Emaminejad, Seyed Aryan;Cusick, Roland D.
综合流域建模需要耦合水资源恢复设施（WRFs）与农业管理的整体流域养分管理。代理建模可以促进模型耦合。本研究应用人工神经网络作为WRRF模型的替代模型，以有效地评估进水波动下的长期处理性能和成本。具体而言，我们首先开发了五个WRRF，包括活性污泥，活性污泥与化学沉淀（ASCP），增强生物除磷（EBPR），EBPR与乙酸盐添加（EBPR-A），EBPR与鸟粪石回收（EBPR-S），在高保真模拟程序（GPS-X）。这五个WRRF是基于一个现有的处理家庭和工业废水的工厂。人工神经网络在捕获所有五个WRRF的非线性生物行为方面具有令人满意的性能，尽管预测性能（R平方）随着模型复杂性的增加而略有下降。我们先进的人工神经网络在WRRF模型模拟长期（10年）的性能与每月进水量波动，使用从稳态模型的模拟数据训练的人工神经网络，并评估其性能的磷（P）和氮（N）去除。EBPR-S表现出最大的弹性，而EBPR是更敏感的雨水流入的影响特性。在10年的模拟期内，比较每个布局的氮和磷去除的生命周期成本时，EPBR-S是最具成本效益的替代方案，突出了侧流磷回收的运营和成本效益。通过捕获生物处理和运行成本与计算精益人工神经网络的非线性行为，本研究提供了一个范例，将复杂的WRRF模型集成在综合流域建模框架。
Integrated watershed modeling is needed to couple water resource recovery facilities (WRRFs) with agricultural management for holistic watershed nutrient management. Surrogate modeling can facilitate model coupling. This study applies artificial neural networks (ANNs) as surrogate models for WRRF models to efficiently evaluate the long-term treatment performance and cost under influent fluctuations. Specifically, we first developed five WRRFs, including activated sludge, activated sludge with chemical precipitation (ASCP), enhanced biological phosphorus removal (EBPR), EBPR with acetate addition (EBPR-A), and EBPR with struvite recovery (EBPR-S), in a high-fidelity simulation program (GPS-X). The five WRRFs were based on an existing plant that treats combined domestic and industrial wastewater. The ANNs have satisfactory performance in capturing nonlinear biological behaviors for all five WRRFs, even though the prediction performance (R-square) slightly decreases as the model complexity increases. We advanced ANNs application in WRRF models by simulating long-term (10-yr) performance with monthly influent fluctuations using ANNs trained by simulation data from steady-state models and evaluated their performance on Phosphorus (P) and Nitrogen (N) removal. EBPR-S shows the most resilience, while EBPR is more sensitive to influent characteristics impacted by stormwater inflow. When comparing life cycle costs of N and P removal for each layout over the 10-yr simulation period, EPBR-S is the most cost-effective alternative, highlighting both the operational and cost benefits of side-stream P recovery. By capturing both nonlinear behaviors of biological treatment and operating costs with computationally lean ANNs, this study provides a paradigm for integrating complex WRRF models within integrated watershed modeling frameworks.