Towards development of a decision support tool for conceptual design of wastewater treatment plants using stochastic simulation optimization

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Available treatment technologies for wastewater are diversifying as the industry is undergoing a paradigm shift from considering wastewater as a waste to treat to an increasingly valuable source for energy production and resource recovery. With the purpose of addressing the problem of determining optimal plant network for wastewater treatment plants, we develop a new decision support tool (SPDLab) relying on a novel simulation optimization based process synthesis framework and a library of rigorous process models. The capabilities of SPDLab are demonstrated with a case study for designing an energy surplus WWTP. The Benchmark Simulation Model No. 2 (BSM2) (Gernaey et al., 2014) plant layout is populated with additional options for primary treatment technologies, such as a rotating belt filter with or without polymer addition, and mainline treatment options including also fixed film activated sludge (IFAS) technology. A side stream treatment with partial nitrification-Anammox process is also included in the superstructure. Results obtained show that the combination of efficient carbon recovery with main stream Anammox and anaerobic digester produces the most net energy among the investigated alternatives.
Original languageEnglish
Title of host publicationProceedings of the 29th European Symposium on Computer Aided Process Engineering
EditorsKiss Anton, Edwin Zondervan, Richard Lakerveld, Leyla Özkan
Publication date2019
ISBN (Print)9780128186343
Publication statusPublished - 2019
Event29th European Symposium on Computer Aided Process Engineering - Eindhoven, Netherlands
Duration: 16 Jun 201919 Jun 2019


Conference29th European Symposium on Computer Aided Process Engineering
SeriesComputer Aided Chemical Engineering
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Process synthesis and design, Wastewater treatment plant design, Simulation-based optimization, Monte Carlo simulation

ID: 189362125