Êtes-vous un étudiant de l'EPFL à la recherche d'un projet de semestre?
Travaillez avec nous sur des projets en science des données et en visualisation, et déployez votre projet sous forme d'application sur Graph Search.
The increasing demand for heat, electricity, fuels and chemicals is pushing naturalresources towards a non-reversible situation. Current solutions have to be adapted,and alternative (desirably sustainable) sources have to be found. With growth as-sured due to an increasing global population, waste is able to provide a plethora ofcomponents in the near future. This work approaches waste management, by usingwastewater from a dairy production. The current state-of-the-art which concernsindustrial and municipal wastewater treatment focuses on single process design andoptimization or, at most, on a set of competing unitary processes. In this study,a superstructure-based model for industrial wastewater integration and valorizationis presented. It is formulated as a MILP problem with the objective of minimizingoperational costs, while constrain investment costs. It comprises traditional wasteconversion roots, but more importantly it proposes greener solutions in order torecover the intrinsic chemical and energetic potential of industrial waste.Starting with a reference scenario of 23.4 Meof operating costs and an exergyefficiency of 25 %, corresponding to a typical (optimized) wastewater treatmentplant, with proper investment, exergy efficiency can go as high as 70 %, which as adirect link to environmental impact. The compromise solution that minimizes totalcost, shows external electricity reduction by 70 %, providing an investment of 27Me, recoverable in 12 years. Innovative solutions, like solid oxide co-electrolysiscells and methane synthesis from syngas are, with the present costs assumptions,non-profitable. Nevertheless, with incentives for bio-SNG production, as well as areduction in electricity prices, an innovative and highly efficient solution is proposed,yielding an exergy efficiency of 86 %. The current work provides operating andinvestment costs of new technologies, as well as relevant technical data.
Dario Floreano, Bokeon Kwak, Markéta Pankhurst, Jun Shintake
Paul Joseph Dyson, Mingyang Liu, Xinbang Wu
Rafael Amorim Leandro De Castro Amoedo