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Spatio-Temporal Control of Nutrient Pollution from Organic Waste
Citation:
Hu, Y., G. Ruiz-Mercado, AND V. Zavala. Spatio-Temporal Control of Nutrient Pollution from Organic Waste. 29th European Symposium on Computer Aided Process Engineering, EindhovenN, June 16 - 19, 2019. Computer Aided Chemical Engineering. Elsevier B.V., Amsterdam, Netherlands, 46:1069-1074, (2019). https://doi.org/10.1016/B978-0-12-818634-3.50179-X
Impact/Purpose:
Holistic management strategies of organic waste will be required to achieve a more comprehensive and permanent solution for nutrient pollution, energy security, and reduce the human impact on the environment. This conference proceeding describes the dynamic management of organic waste by an optimized supply chain approach for recovering energy and nutrients, controlling nutrient pollution, and studying the effect of decisions made in the organic waste supply chain on reducing the potential for harmful algal blooms.
Description:
Better management of anthropogenic organic waste and other primary sources of nutrient pollution such as agricultural, municipal, and industrial waste, will reduce the human impact on the environment. Harmful algal blooms (HABs) are a major environmental impact from organic waste and nutrient pollution. HABs can pose severe threats to human health due to the release of dangerous toxins in fresh or marine water that can negatively affect public health, increase treatment costs for drinking water, and cause enormous economic loss in industries that depend on clean water. In this study, the effect of decisions made in the organic waste supply chain on reducing the potential for HABs is investigated by integrating three types of models: a supply chain optimization model, a nutrient transport model, and an algae growth model. This contribution presents a comprehensive spatio-temporal management strategy for short-term HAB reduction by adjusting components in the supply chain, including technologies, logistics, nutrient management plans (or environmental costs), and seasonal waste storage planning. In addition, it is presented a case study of the Upper Yahara Watershed in the State of Wisconsin to illustrate the practicability of this modeling framework.
