Abstract

Wastewater treatment facilities are known to process water by removing nutrients before being discharged into different water bodies or reused. Traditional treatment of wastewater, however, leads to the emission of greenhouse gases contributing to climate change and air pollution. Thus, there is a need to identify the optimal configuration of treatment processes wastewater, coming from different sources, have to go through to satisfy the output quality requirements of various disposal or reuse options, while minimizing costs and negative impact to the environment In addition, microalgae cultivation is a treatment alternative for wastewater since it can remove metals, nutrients, and contaminants from wastewater, with the added benefit of carbon sequestration. The cultivated algae can then be converted to renewable energy. Despite the potential benefits that can be gained from integrating wastewater treatment facilities with microalgal biofuel production, no optimization study has considered this opportunity. Considering different wastewater inputs, the joint system would select the best treatment process for nutrient removal and cultivating algae, weighing the trade-offs in cultivating algae on different water mediums, the appropriate harvesting technique, and whether the water by-product should be sent back to the treatment facility for further processing, disposal, or reuse. The energy produced from the plant may either be sold or used to operate the two facilities. In this work, a novel multi-objective optimization model is developed to design economically and environmentally efficient integration of wastewater treatment facilities and microalgal biofuel production plants through water exchanges. A case study is solved to demonstrate the model’s decision on three different scenarios. In the objective of minimizing the costs, the model utilized the production of biofuels since it was subtracted from the expenses. As for minimizing carbon emissions, the model decided to operate the wastewater treatment plant since there were less processes used in the model. When goal programming was used in order to satisfy both objectives, the model found a balance between the two plants which in return chose the have some exchanges present.

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