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.

中文翻译：

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使用人工神经网络作为全厂建模的替代物，在随机进水特征下评估水资源回收设施的长期处理性能和养分去除成本

需要综合流域建模来将水资源回收设施 (WRRF) 与农业管理结合起来，以进行整体流域养分管理。代理建模可以促进模型耦合。本研究应用人工神经网络 (ANN) 作为 WRRF 模型的替代模型，以有效评估进水波动下的长期处理性能和成本。具体来说，我们首先开发了五种WRRF，包括活性污泥、化学沉淀活性污泥（ASCP）、增强生物除磷（EBPR）、加醋酸盐EBPR（EBPR-A）和带鸟粪石回收的EBPR（EBPR-S），在高保真模拟程序 (GPS-X) 中。这五个 WRRF 以现有的处理家庭和工业废水的工厂为基础。电阻-square）随着模型复杂度的增加而略有下降。我们通过使用由稳态模型的模拟数据训练的人工神经网络模拟长期（10 年）性能和每月进水波动，改进了人工神经网络在 WRRF 模型中的应用，并评估了它们在磷 (P) 和氮 (N) 去除方面的性能。EBPR-S 显示出最大的弹性，而 EBPR 对受雨水流入影响的进水特征更敏感。在比较 10 年模拟期间每个布局的 N 和 P 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 P 回收的运营和成本优势。通过使用计算精益的 ANN 捕获生物处理的非线性行为和运营成本，