Optimization-based methodology for the development of wastewater facilities for energy and nutrient recovery. (December 2015)

• Record Type: Journal Article
• Authors: Puchongkawarin, C.; Gomez-Mont, C.; Stuckey, D.C.; Chachuat, B.
• Is Part Of: Chemosphere. Volume 140(2015)
• Page Start: 150
• Page End: 158
• Publication Date: 2015-12
• Subjects: Biological treatment -- Biorefining -- Energy recovery -- Nutrient recovery -- Superstructure optimization -- Wastewater treatment; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2014.08.061
• Languages: English
• ISSNs: 0045-6535

Abstract

Highlights: Growing number of treatment/recovery options calls for a superstructure optimization framework for decision support. Tractable performance and costing models for the units can be derived from state-of-the-art process simulators. Computational tractability of the superstructure optimization methodology is established on a simple case study. Abstract: A paradigm shift is currently underway from an attitude that considers wastewater streams as a waste to be treated, to a proactive interest in recovering materials and energy from these streams. This paper is concerned with the development and application of a systematic, model-based methodology for the development of wastewater resource recovery systems that are both economically attractive and sustainable. With the array of available treatment and recovery options growing steadily, a superstructure modeling approach based on rigorous mathematical optimization appears to be a natural approach for tackling these problems. The development of reliable, yet simple, performance and cost models is a key issue with this approach in order to allow for a reliable solution based on global optimization. We argue that commercial wastewater simulators can be used to derive such models, and we illustrate this approach with a simple resource recovery system. The results show that the proposed methodology is computationally tractable, thereby supporting its application as a decision support system for selection of promising resource recovery systems whose development is worth pursuing.