organizzazione bibliografia

8/10/2019 organizzazione bibliografia

1/21


2/21

Sheet1

26

39

40

41

42

43

48

49

50.00

51

52

Page 2


3/21

Sheet1

53

54

55

56

57.00

58

59.00

Page 3


4/21


5/21

Sheet1

69

70

71.00

72

73

74.00

75

76

Page 5


6/21

Sheet1

78

77

79

80

81

82

Page 6


7/21

Sheet1

83

84

86

87

88

Page 7


8/21

Sheet1

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.

Page 8


9/21

Sheet1

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

Page 9


10/21

Sheet1

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

Page 10


11/21

Sheet1

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.

Page 11


12/21

Sheet1

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

Page 12


13/21

Sheet1

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

Page 13


14/21

Sheet1

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

Page 14


15/21

Sheet1

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

Page 15


16/21

Sheet1

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

Page 16


17/21

Sheet1

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

Page 17


18/21

Sheet1

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

Page 18


19/21

Sheet1

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;


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

Page 19


20/21

Sheet1

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;


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

Page 20


21/21

Sheet1

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;


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

Page 21

organizzazione bibliografia

Documents

Transcript of organizzazione bibliografia