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International Journal of Chemical Engineering
Volume 2019, Article ID 2621048, 11 pages
https://doi.org/10.1155/2019/2621048
Research Article

Energy Integration in Wastewater Treatment Plants by Anaerobic Digestion of Urban Waste: A Process Design and Simulation Study

Department of Chemical Engineering and Environmental Technology, University of Valladolid, C/Dr. Mergelina s/n, 47011 Valladolid, Spain

Correspondence should be addressed to Rocio Vicentin; moc.liamg@nitneciv.oicor

Received 1 November 2018; Revised 21 January 2019; Accepted 17 February 2019; Published 13 March 2019

Academic Editor: Maurizio Volpe

Copyright © 2019 Rocio Vicentin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The process simulation performed in the present study aimed at investigating energetically self-sufficient wastewater treatment plant of 500,000 population equivalents. To implement this, three different scenarios were evaluated using computational tools named GPS-X® and SuperPro®. They were designed based on municipal wastes recovery to energy generation and its utilisation within the facility. An anaerobic/anoxic/oxic process for biological treatment of wastewater was considered and mesophilic anaerobic digestion at different scenarios (1) primary sludge (PS) with waste activated sludge (WAS), (2) PS with thermally hydrolysed WAS, and (3) PS with WAS and organic fractions derived from municipal solid waste. The results from scenario 1 and scenario 2 showed only enough thermal energy to meet their demand (they reach only 44 and 52% of electrical self-sufficiency, respectively), while positive net thermal and electrical energy result in scenario 3 from codigestion of sewage sludge and the organic fraction of municipal solid waste. The main limitation of tools used is their lack of sensitivity to economies of scale and their dependence on real data used for process design to obtain more accurate results.