6533b7d5fe1ef96bd1265369

RESEARCH PRODUCT

Potential of interactive multiobjective optimization in supporting the design of a groundwater biodenitrification process

Jussi HakanenKaisa MiettinenMarkus HartikainenGiulia SaccaniVesa OjalehtoKarthik SindhyaManuela Antonelli

subject

Pareto optimalityDecision support systemdecision supportEnvironmental EngineeringProcess (engineering)Computer science0208 environmental biotechnologypäätöksentukijärjestelmät02 engineering and technologyActivated sludge model010501 environmental sciencesManagement Monitoring Policy and Law01 natural sciencesMulti-objective optimizationInteractive methodIND-NIMBUSWater treatmentSensitivity (control systems)Process engineeringWaste Management and DisposalGroundwater0105 earth and related environmental sciencesvedenpuhdistusNitratesSewagepareto optimalitypareto-tehokkuusbusiness.industrywater treatmentGeneral Medicineinteractive methodvedenkäsittelymonitavoiteoptimointi020801 environmental engineeringDecision supportRange (mathematics)Decision support; IND-NIMBUS; Interactive method; NIMBUS method; Pareto optimality; Water treatment; Algorithms; Denitrification; Nitrates; Sewage; GroundwaterDenitrificationA priori and a posterioriWater treatmentNIMBUS methodbusinessAlgorithms

description

The design of water treatment plants requires simultaneous analysis of technical, economic and environmental aspects, identified by multiple conflicting objectives. We demonstrated the advantages of an interactive multiobjective optimization (MOO) method over a posteriori methods in an unexplored field, namely the design of a biological treatment plant for drinking water production, that tackles the process drawbacks, contrarily to what happens in a traditional volumetric-load-driven design procedure. Specifically, we consider a groundwater denitrification biofilter, simulated by the Activated Sludge Model modified with two-stage denitrification kinetics. Three objectives were defined (nitrate removal efficiency, drawbacks on produced water, investment and management costs) and the interactive method NIMBUS applied to identify the best-suited design without any a priori evaluation, as for volumetric-load-driven design procedures. When compared to an evolutionary MOO algorithm, the interactive solution process was faster, more understandable and user-friendly and supported the decision maker well in identifying the most preferred solution (main design/operating parameters) to be implemented. Approach strength has been proved through both sensitivity analysis and positive experimental validation through a pilot scale biofilter operated for three months. In synthesis, without any “a priori” evaluation based on practical experience, the MOO design approach allowed obtaining a preferred Pareto optimal design, characterized by volumetric loading in the range 0.85–2.54 kgN m−3 d−1 (EBCTs: 5–15 min), a carbon dosage of 0.5–0.8 gC,dos/gC,stoich, with SRTs in the range 4–27 d. peerReviewed

10.1016/j.jenvman.2019.109770http://hdl.handle.net/11311/1123817