Developing an integrated technology-environment-economics model to simulate food-energy-water systems in Corn Belt watersheds

Li, Shaobin; Cai, Ximing; Emaminejad, Seyed Aryan; Juneja, Ankita; Niroula, Sundar; Oh, Seojeong; Wallington, Kevin; Cusick, Roland D.; Gramig, Benjamin M.; John, Stephen; McIsaac, Gregory F.

doi:10.1016/j.envsoft.2021.105083

Cited by 24 publications

(28 citation statements)

References 45 publications

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“…A 38% underestimation of point source nitrate will lead to an overall 2.9% underestimation for the watershed, which is acceptable for future watershed optimization efforts. 11 Compared with the nitrate simulation, TP loading simulations compared favorably to historical data (Figure 5d). The TP loading from AS followed the trend of historical TP loading and the discrepancy was much smaller (18% overestimate on average), as compared to the case of nitrate loading.…”

Section: Resultsmentioning

confidence: 87%

“…8,9 Integrated models that simulate the effectiveness of point source and nonpoint source nutrient reductions are required for holistic nutrient management at watershed scales. 10,11 Various computer programs (e.g., GPS-X and BioWin) have been developed that allow for simulation of WRRF treatment alternatives. 12−15 In practice, these simulators help identify alternative processes that best align with point source nutrient reduction goals and determine the most cost-effective approaches to nutrient reduction.…”

Section: Introductionmentioning

confidence: 99%

“…In another work that focused on model integration coupling multiple process and empirical models, we demonstrated the importance of surrogating a process model to facilitate model integration. 11 WRRF plant-wide models can also be computationally expensive to execute, depending on the degree of complexity for various treatment processes (e.g., activated sludge versus enhanced biological phosphorus removal), number of simulations required, and temporal scales (e.g., real-time, daily, monthly, annual). 16 By developing data-driven WRRF surrogate models, wastewater treatment performance under various combinations of influent characterisitics and fluctuations can be evaluated efficiently, instead of using processbased WRRF models in a commercial software package to run the simulation.…”

Section: Introductionmentioning

confidence: 99%

“…The surrogate models developed in this work have been further coupled with other nonpoint source hydrological models for integrated technology-environment-economics modeling (ITEEM), which is part of a separate work focused on surrogate-based model coupling. 11…”

Section: Introductionmentioning

confidence: 99%

“…To achieve the nutrient reduction goal outlined in the NLRS, considerable changes to both nonpoint (e.g., agricultural runoff) and point source (wastewater treatment effluent) management are needed. For nonpoint sources, nutrient pollution can be reduced by adopting various agricultural best management practices. , For point sources, nutrient pollution can be reduced by optimizing the existing wastewater treatment and resource recovery processes used in water resource recovery facilities (WRRFs) or upgrading to a more advanced treatment process with new infrastructure. , Integrated models that simulate the effectiveness of point source and nonpoint source nutrient reductions are required for holistic nutrient management at watershed scales. , …”

Section: Introductionmentioning

confidence: 99%

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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

Emaminejad

Aguiar

et al. 2021

ACS EST Eng.

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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.

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Section: Resultsmentioning

confidence: 87%