Abstract
Project decisions are currently decided primarily on initial costs and neglect environmental and social aspects. In this study, a new optimization algorithm has been developed based on the three-legged stool of sustainability, namely technical, environmental, and socioeconomic aspects. The new algorithm, entitled the sustainability-based optimization algorithm, will minimize the average of normalized values of life-cycle costs, environmental impacts, and social objections subject to the constraint that all options will be acceptable. The optimal combinations of input variable values are found by optimization. The algorithm is demonstrated on algal biofuel production and is adapted from the model described in the greenhouse gases, regulated emissions, and energy use in transportation model. The indicators that are practical and widely applicable in algal production were based on previous studies. The results show the minimum objective function occurs at the combination of input variables that minimizes each component of sustainability, namely CO2-reduction, environmental, and economic aspects. The minimum objective considering only the social aspects occurs at the water-recycling reduction scenario. Considering the optimal solution with the focus only on one of the environmental, social, or economic terms could potentially lead to a solution that is not robust. The advantage of this algorithm is that by considering all active and effective indicators together the most sustainably robust result is found. The new algorithm is potentially applicable for various types of projects involved in resilient infrastructure such as bridges, roads, electrical grids, water distribution systems, sewer systems, water and wastewater treatment plants, and renewable energy.
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The completion of this undertaking could not have been possible without the support of Department of Civil and Environmental Engineering at Michigan Technological University.
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Tavakoli, H., Barkdoll, B.D. Sustainability-based optimization algorithm. Int. J. Environ. Sci. Technol. 17, 1537–1550 (2020). https://doi.org/10.1007/s13762-019-02535-9
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DOI: https://doi.org/10.1007/s13762-019-02535-9