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Title Authors
Wastewater treatment: New insight provided by interactive multiobjective optimization
Hakanen, Jussi
Miettinen, Kaisa
Sahlstedt, Kristian
Benchmarking energy consumption in municipal wastewater treatment plants in Japan.Mizuta, Kentaro
Shimada, Masao
Energy benchmarking of South Australian WWTPs Krampe
Multi-criteria selection of optimum WWTP control set points based on microbiology-
related failures, effluent quality and operating costs
Guerrero, J.
Guisasola, A.
Comas, J.
WWTP design in warm climates - guideline comparison and parameter adaptation for
a full-scale activated sludge plant using mass balancing.
Walder, C
Lindtner, S
Proesl, AKlegraf, F
Weissenbacher, N
Benchmarking of large municipal wastewater treatment plants treating over 100,000
PE in Austria
Lindtner, S.
Schaar, H.
Kroiss, H.
Benchmarking of WWTP design by assessing costs, effluent quality and process
variability
Benedetti, L.
Bixio, D.
Vanrolleghem, P.a.
Comparing the efficiency of wastewater treatment technologies through a DEA
metafrontier model
Sala-Garrido, R.
Molinos-Senante,
M.
Hernndez-
Sancho, F.
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Waste-to-Energy (W2E) software - a support tool for decision making process
Tous, M
Bebar, L
Houdkova, L
Pavlas, M
Stehlik, P
Making Water Resource Decisions More "Informationally" Efficient: Development of aGeospatial Water Rights Decision Support System for Kittitas County, Washington Pease, MichaelMurray, Jeremy
Data evaluation of full-scale wastewater treatment plants by mass balance.
Puig, S
van Loosdrecht, M
C M
Colprim, J
Meijer, S C F
Multi-criteria analysis of wastewater treatment plant design and control scenarios under
uncertainty
Benedetti, L.
De Baets, B.
Nopens, I.
Vanrolleghem, P.a.
A systematic approach for fine-tuning of fuzzy controllers applied to WWTPs
Ruano, M.V.Ribes, J.
Sin, G.
Seco, A.
Ferrer, J.
Improving the performance of a WWTP control system by model-based setpoint
optimisation
Guerrero, Javier
Guisasola, Albert
Vilanova, Ramon
Baeza, Juan a.
Introduction to Decision support system Marakas
[ebook] Cognition-Driven Decision Support for Business Intelligence
Niu, Li
Lu, Jie
Zhang, Guangquan
Test, Bla
Cruise Management
Information and Decision Support Systems
Decision Support - An Examination of the DSS Discipline Different authors
Decision Support Systems
Collaborative Models and Approaches
in Real Environments
Zarate, Jorge E.
Hernndez Pascale
Delibaic, Ftima
Dargam Boris
(Eds.), Shaofeng
Liu Rita Ribeiro
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Ebook: Decision Support Systems II Recent Developments Applied to DSS Network
Environments
Liu, Jorge E.
Hernndez
Shaofeng
Zarat, Boris
Delibaic Pascale
(Eds.), FtimaDargam Rita
Ribeiro
Handbook on decision support system 1 e 2Frada Burstein; W.
Holsapple
Intelligent Decision-making Support Systems
Jatinder N.D.
Gupta, Guisseppi
A. Forgionne
T., and Manuel
Mora
Development of a knowledge-based decision support system for identifying adequate
wastewater treatment for small communities
Comas, J
Alemany, JPoch, M
Torrens, A
Salgot, M
Bou, J
Girona, Universitat
De
Montilivi, Campus
Barcelona,
Universitat De
Environmental Decision Support SystemsCortes
An advanced integrated expert system for wastewater treatment plants control Paraskevas, P.a
Pantelakis, I.S
Lekkas, T.D
A knowledge based system to support the process selection during waste water treatment
Wukovits, W
Harasek, M
Friedl, A
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Model-based knowledge acquisition in environmental decision support system forwastewater integrated management.
Prat, Pau
Benedetti, LorenzoCorominas, Llus
Comas, Joaquim
Poch, Manel
Wastewater treatment plant design and operation under multiple
conflicting objective functions
Hakanen, J.
Sahlstedt, K.
Miettinen, K.
A linear ASM1 based multi-model for activated sludge systems
Smets, Ilse
Verdickt, LiesbethVan Impe, Jan
ACTIVATED SLUDGE MODELS ASM1, ASM2, ASM2d AND ASM3
Henze, Mogens
Gujer, Willi
Mino, Takashi
Loosdrecht, Mark
Van
Ebook:Non linear multiobjective optimization
Reti Neurali Artificiali: Teoria ed Applicazioni Crescenzio Gallo
Introduzione alle Reti Neurali Lazzerini
Ebook: Artificial Neural Networks
and Machine Learning
ICANN 2013
23rd International
Conference on
Artificial Neural
Networks
Sofia, Bulgaria,
September 2013
Proceedings
Artificial neural networks for rapid WWTP performance evaluation: Methodology andcase study
Rduly, B.
Gernaey, K.V.
Capodaglio, A.G.Mikkelsen, P.S.
Henze, M.
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Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY
wastewater treatment plant performance-EGYPT
Nasr, Mahmoud S.
Moustafa, Medhat
a.E.
Seif, Hamdy a.E.
El Kobrosy, Galal
Prediction of wastewater treatment plant performance using artificial neural networks
Hamed, Maged M
Khalafallah, Mona
G
Hassanien, Ezzat a
Simulation of an industrial wastewater treatment plant using
artificial neural networks
A fuzzy neural network approach for online fault detection in waste water treatment
process
Honggui, Han
Ying, Li
Junfei, Qiao
Artificial Intelligence and Environmental Decision Support SystemsCeccaroni, L
Es, U Cort
Poch, M
Montilivi,
Environmental decision support systems: Current issues, methods and tools
Matthies, Michael
Giupponi, Carlo
Ostendorf, Bertram
Data-driven modeling approaches to support wastewater treatment plant operation
Drrenmatt, David
Jrme
Gujer, Willi
Artificial neural network modelling of a large-scale wastewater treatment plant
operation.
Gl, Dnyamin
Dursun, Skr
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Ebook: applied fuzzy aritmetics
Designing and building real environmental decision support systems
Poch, Manel
Comas, Joaquim
Rodrguez-Roda,
Ignasi
Snchez-Marr,Miquel
Corts, Ulises
Decision Making support system in multi objective issues of quality management in the
field of information technology
Semenova,
Smirnova,
Tushavin
Evaluation of multivariate linear regression and artificial neural networks in prediction of
water quality parameters Zare Abyaneh,
Hamid
Environmental decision support systems (EDSS) development Challenges and best
practices
McIntosh, B.S.
Ascough, J.C.Twery, M.
Chew, J.
Elmahdi, A.
Haase, D.
Harou, J.J.
Hepting, D.
Cuddy, S.
Jakeman, A.J.
Chen, S.
Kassahun, A.
Lautenbach, S.
Matthews, K.
Merritt, W.
Quinn, N.W.T.
Rodriguez-Roda, I.
Sieber, S.
Stavenga, M.
Sulis, A.
Ticehurst, J.
Volk, M.
Wrobel, M.
van Delden, H.
El-Sawah, S.
Rizzoli, A.
Voinov, A.
A sensor-software based on a genetic algorithm-based neural fuzzy system for
modeling and simulating a wastewater treatment process Huang, Mingzhi
Ma, Yongwen
Wan, Jinquan
Chen, Xiaohong
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Knowledge discovery with clustering based on rules by states: A water treatment
application
Gibert, K.Rodrguez-Silva, G.
Rodrguez-Roda, I.
Where are we in wastewater treatment plants data management? A review and a
proposal
Poch, Manel
Comas, Joaquim
Porro, Jos
Garrido-Baserba,
Manel
Corominas, Lluis
Pijuan, Maite
An Efficiency-Centred Hierarchical Method to Assess Performance of Wastewater
Treatment Plants
Chen, Z.*, Zayed,
T.
Qasem, A.
Design, Development and Implementation of a Robust Decision Support ExpertSystem (branDEC) in Multi Criteria Decision Making
Jha, N.K.
Kumar, R.
Kumari, A.
Bepari, B.
Evaluation of process conditions triggering emissions of green-house gases from a
biological wastewater treatment system.
Rodriguez-
Caballero, A
Aymerich, I
Poch, M
Pijuan, M
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Macrotags Tags Note Related paper Evaluation
DSS & Optimization
Decision support; IND-
NIMBUS; Interactive
methods; Multicriteria
optimization; Simulation-
based optimization;
Wastewater treatmentplanning; wastewater
treatment planning
It works on multi-
objective
optimization, it uses
NINBUS technique
8,5
Benchmarking
Benchmarking; Energy-
Generating Resources;
Environmental
Monitoring; Greenhouse
Effect; Japan; Waste
Disposal; Fluid; Waste
Disposal; Fluid: methods
Important for
benchmarking
approach and for
reference values
7.5
Benchmarking
Benchmarking;
Conservation of EnergyResources; South
Australia
Important for
benchmarkingapproach and for
reference values
7.5
DSS & Optimization
Important for
literature, plant
performance
function
development,
operational cost,
Pareto front
9
WWTP generic
Bioreactors; Climate;
Sewage; Sewage:
chemistry; WasteDisposal; Fluid; Waste
Disposal; Fluid: methods
reference quality
data by massbalance
[40] 5
Benchmarking
Important for
statistical analysis of
KPIs
IWA_Performa
nce indicators;
Austrian
benchmarking
system
8,5
Benchmarking
benchmarking; cost-
effectiveness;
mathematical modelling;
Monte carlo simulation;probabilistic
Important for
calculation of quality
KPIs
7
DSS & Optimizationdata envelopment analysis;
dea
DEA approach,
Pareto frontier and
meta-frontier to
compare different
technologies
8
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DSS & WWTPNot so much
info5
WWTP generic out of our topic 0
WWTP & data analysisdata analysis using
mass balance8
DSS & Optimization
activated sludge model
MC simulation on
Benchmark
Simulation Model
no. 2(BSM2)
6.5
WWTP & Fuzzy Logic da rivedere 8
DSS & Optmization
It define the function
Operational Cost
depending on VOL
Maybe it is possible
apply some
optimization for the
linear function
(SIMPLEX ?)
7.5
DSS & Optmization Just an intro 6
DSS & OptmizationModels, Techniques,
Systems and Applicatins
Compplete
handbook about
DSS
9
0.00
DSS & OptmizationCollection of
paper on DSS8
not classified
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DSS not classified
DSS & Optmization
Compplete
handbook about
DSS
Cap 26
ANN & DSS9
not classified
DSS & Optmization
adequate treatment;
decision support;
knowledge-based; small
communities; wastewater
Important for the
knowledge
acquisition and for
related literature
7
DSS & Optmizationenvironmental decision;
environmental scienceNOT USEFUL 0.00
DSS & WWTP
activated sludge; artificialintelligence; automatic
control; expert systems;
wastewater treatment
cfr. Fig 1
Structure for
DSS
8.5
DSS & WWTP
costs; processes;
selection; sequencing;
treatment; wastewater;
water
NOT USEFUL 0.00
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DSS & WWTP
Computer Simulation;
Decision Support
Systems; Management;
Environmental
Monitoring;
EnvironmentalMonitoring: methods;
Models; Theoretical;
Rivers; Spain; Waste
Disposal; Fluid; Waste
Disposal; Fluid: methods;
Water Pollutants;
Chemical; Weather
It gives different
cause of reflection:
sensitivity analysis,Monte Carlo
simulations,
reference models
7
WWTP & Optimizationmultiobjective
optimization
it is an interesting
update of 05 with
GPS-X-INDIBUS
[05] 8
DSS & Opmization
asm1; cost benchmark;
linearization; modelcomplexity reduction
It use simplified
modelling ASM1 6.5
WWTP not classified
not classified
Neural NetworkIntroduzione
alle reti neurali7
Neural Network Introduzionealle reti neurali
6
not classified
WWTP and NeuralNetwork
artificial neural networks;
modeling; performance
evaluation; plant design;simulation speed; time
series; wastewater
treatment plant
ANN used to predictWWTP 7.5
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WWTP and Neural
Network
artificial neural networks
ANN used to predict
WWTP8,5
WWTP and Neural
Network
biochemical oxygendemand; model studies;
neural networks;
optimization; prediction;
suspended solids; waste
water treatment
It use ANN on
WWTP but without
energy
cfr. Fig. 5 with
STEPS OF
MODEL
DEVELOPME
NT PROCESS
8,5
0.00
WWTP & Fuzzy Logic Data cleaning
It uses fuzzy neural
network to detect
error in sensor data
7.5
Artificial intelligence &
DSS
artificial intelligence;
environmental decision
support systems;
problem solving
It works on DSSframework, listing
different alternatives
to develop black-box
functions. Fig. 1 and
Fig.2 are important
Fig. 1 e Fig. 2 8.5
Generic DSS DSS NOT Useful 0.00
WWTP & software
sensor
acquisition; data-driven
modeling; supervisory
control and data;
wastewater treatment
plant
Software sensor 80 & 76 9
WWTP and Neural
Network
Algorithms; Cities;
Computer Simulation;
Computers; Equipment
Design; Models;
Theoretical; Neural
Networks (Computer);
Reproducibility of
Results; Time Factors;
Turkey; Waste Disposal;
Fluid; Waste Disposal;Fluid: methods; Water
Pollutants; Chemical;
Water Pollutants;
Chemical: analysis;
Water Purification; Water
Purification: methods
It uses ANN
approach to predict
the output of a
WWTP. To be used
if we choose thisoption
8
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not classified
DSS & Optmization
DSS, optimization, ANN
It uses ANN in a
DSS to predict
WWTP behaviour
Figura 2 - figur 9.5
DSS & Optmization
It talks about
selection of
alternatives using
fuzzy logic
6.5
WWTP & Neural
Network
ann; bod; cod; mlr;
wastewater treatment
plant
It uses ANN to
predict BOD & COD
by other
measurement 76
7.5
EDSSenvironmental decision
support systems
It debate about
strenght and
weakness of EDSS.
It focuses on real
experiences, it
provides bestpratices and it
suggests the main
features of a
successful EDSS
9
WWTP & Neural
Network
Anoxic/oxic process;
Genetic algorithm; Neural
fuzzy system; On-line
monitoring
It predict COD, NO,
and PO using online
measurementIt
tried with GENETIC
ALGORITMS and
with Fuzzy Neural
Network80
9
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DSS & WWTP
Clustering; Data mining;
Dynamics; Inductive
learning; Knowledge
discovery from data;
Profiles induction; Rules;States; Wastewater
It uses the concept
of classes and
trajctoryit could be
useful in a predictive
model it is also
useful for
KnowledgeDiscovery by data
(KDD) approach
8
DSS & WWTP
Data mining; Heuristic
knowledge; WWTP
management
It is just a literature
review but it is
updated and it
suggest to mix
Euristic and
Qualitative
Knowledge
mixing
heuristic and
qualitative
knowledge
7.5
WWTP
condition rating;
infrastructure; model;
performance index;
wastewater treatment
It gives a
methodology toeasily evaluate the
plant performance
and the performance
of stages alsoit
works just on quality
of effluentit could
be an useful
approach also with
KPI and energy
8
DSS
MCDM techniques;
aggregated/group
decision making.;
decision support expert
system
Currently not
interestingmaybe
later
6
WWTP
Activated sludge; Full-
scale monitoring; Green-
house gas emissions;
Methane; Nitrous oxide;
Wastewater treatment
Currently not
interestingmaybe
later
6
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