Multi-objective Optimisation Approach for the Synthesis of Water Treatment Plants
Date
2016
Authors
Koleva, M.N.
Liu, S.
Styan, C.A.
Papageorgiou, L.G.
Editors
Kravanja, Z.
Bogataj, M.
Bogataj, M.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Book chapter
Citation
Source details - Title: Computer aided chemical engineering, 2016 / Kravanja, Z., Bogataj, M. (ed./s), vol.38, pp.2379-2384
Statement of Responsibility
Conference Name
Abstract
More efficient water treatment plants are needed if future global water requirements are going to be met. Plant design, however, is complicated by the need to optimise separate but linked treatment processes, with individual process optima not necessarily leading to the most efficient overall systems. In this work, a superstructure optimisation-based methodology for flowsheet synthesis is proposed. The problem is formulated as a mixed integer non-linear programming (MINLP) model. The superstructure encompasses the most commonly used technologies (coagulation-flocculation, sedimentation, dissolved air flotation, media filtration, microfiltration, ultrafiltration, nanofiltration, reverse osmosis) in water treatment, advanced wastewater treatment and desalination, which enables the design of fit-for-purpose treatment. Physico-chemical characteristics (e.g. pressure, temperature, hydrophobicity, mixing gradient, etc.) of the candidates allow the prediction of their technical and economic performance. The model identifies the optimum configuration of passes and stages in the flow diagram and capital costs estimation. A multi-objective optimisation is performed for the minimisation of water net cost and minimisation of contaminants concentrations using epsilon - constraint method. The applicability of the model is verified through a theoretical case study. The computational results fall into the lower margin of water purification facilities worldwide and indicate the effectiveness and efficiency of the developed approach.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2016 Elsevier