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Books on the topic 'Optimisation de simulations'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Effiong, Edward Edem. The design, simulation, and optimisation of distillation columns. University of Salford, 1988.

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2

Ling, Ling. Computer based approaches to managerial decision making: Optimisation, heuristics, and simulation. Aston University. Aston Business School, 1995.

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3

Kothdiwala, Ahmed Farouk. Simulation and optimisation of asymmetric and symmetric compound parabolic concentratingsolar collectors. The Author], 1996.

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4

Desideri, Umberto, Giampaolo Manfrida, and Enrico Sciubba, eds. ECOS 2012. Firenze University Press, 2012. http://dx.doi.org/10.36253/978-88-6655-322-9.

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The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors

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5

Choy, Tommy King Lun. On the optimisation of manufacturing systems through fuzzied genetic algorithms based simulation. typescript, 1998.

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6

C, Ng Amos H., Deb Kalyanmoy, and SpringerLink (Online service), eds. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer-Verlag London Limited, 2011.

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7

Coulbeck, B. Description and applications of water distribution system simulation and optimisation programs GINAS & GIPOS. Leicester Polytechnic, 1985.

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8

Process plant simulation. Oxford University Press, 2004.

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9

Mondol, Jayanta Deb. Long-term performance analysis, simulation, optimisation and economic analysis of a building-integrated photovoltaic system. The Author], 2004.

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10

Robyns, Benoit. Vector Control of Induction Machines: Desensitisation and Optimisation Through Fuzzy Logic. Springer London, 2012.

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11

Tallinn, Estonia) International Conference on Operational Research (2006. Simulation and optimisation in business and industry: International conference on operational research, May 17-20, 2006, Tallinn, Estonia. Technologija, 2006.

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12

D, Karaboga, ed. Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks. Springer London, 2000.

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13

Rimkus, Wolfgang. Simulation and optimisation of the Interior High Pressure (IHP) manufacturing process using the Finite Element Method (FEA). University of Wolverhampton, 2000.

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14

Introduction to stochastic search and optimization: Estimation, simulation, and control. J. Wiley, 2003.

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15

Hay, Lee Loo, ed. Stochastic simulation optimization: An optimal computing budget allocation. World Scientific, 2011.

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16

1950-, Cipriano Aldo, and Ordys A. W. 1956-, eds. Optimisation of industrial processes at supervisory level: Application to control of thermal power plants. Springer, 2002.

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17

Repole, Donato. Research of Parallel Computing Neuro-fuzzy Networks for Unmanned Vehicles. RTU Press, 2021. http://dx.doi.org/10.7250/9789934226922.

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The Doctoral Thesis illustrates the author’s research in the field of VHDL based ‘neuro-fuzzy controllers’. The Thesis examines a novel software tool for the high-level ‘neuro-fuzzy controller’ description capable of executing controller simulations, optimisation tasks, performing learning / training tasks, and exporting the controller in VHDL code. The author introduces a design strategy that is looking for developing solutions for complex controller architecture of mobile robotic vehicles (of any nature) or even for multiple industrial application. This work enables further investigative res

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18

Genetic algorithms in optimisation, simulation and modelling. IOS Press, 1994.

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19

DIEULEVEULT, D. E. Amplificateurs Bf et Rf: Optimisation Par Simulation. ISTE Editions Ltd., 2018.

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20

Sauer, Wilfried, Gerald Weigert, Sebastian Werner, Martin Oppermann, and A. Rudd. Electronics Process Technology: Production Modelling, Simulation and Optimisation. Springer, 2007.

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21

Sauer, Wilfried, Gerald Weigert, Sebastian Werner, et al. Electronics Process Technology: Production Modelling, Simulation and Optimisation. Springer, 2010.

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22

Sauer, Wilfried, Gerald Weigert, Sebastian Werner, et al. Electronics Process Technology: Production Modelling, Simulation and Optimisation. Springer, 2006.

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23

Electronics process technology: Production modelling, simulation and optimisation. Springer, 2006.

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24

Julius Kruttschnitt Mineral Research Centre University of Queensland. and University of Cape Town, eds. The optimisation of mineral processes by modelling and simulation. 1999.

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25

Pivokonský, Martin, Kateřina Novotná, Lenka Čermáková, and Radim Petříček, eds. Jar Tests for Water Treatment Optimisation. IWA Publishing, 2022. http://dx.doi.org/10.2166/9781789062694.

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Abstract The book is intended as a handbook providing detailed instructions for the correct conducting of jar tests, which are needed for the optimisation of the coagulation/flocculation process. It contains the essential theoretical background of coagulation/flocculation, including a description of the influence of different parameters on the coagulation efficiency of various impurities (e.g. pH value and type/dose of coagulant), and floc properties and their separation (e.g. mixing intensity, mixing time, but also type/concentration of coagulant and impurities). The principle of jar tests is

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26

Deb, Kalyanmoy, Lihui Wang, and Amos H. C. Ng. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer, 2014.

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27

Deb, Kalyanmoy, Lihui Wang, and Amos H. C. Ng. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer, 2011.

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28

(Editor), Hans-Joachim Bungartz, and Michael Schäfer (Editor), eds. Fluid-Structure Interaction: Modelling, Simulation, Optimisation (Lecture Notes in Computational Science and Engineering). Springer, 2006.

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29

Ordinal Optimization: Soft Optimization for Hard Problems (International Series on Discrete Event Dynamic Systems). Springer, 2007.

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30

Analyse numérique et optimisation: Une introduction à la modélisation mathématique et à la simulation numérique. 2nd ed. Les éditions de l'École Polytechnique, 2012.

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31

IEA Annex 25 real time simulation of HVAC systems for building optimisation, fault detection and diagnosis: Building optimisation and faul diagnosis system concept. Technical Research Centre of Finland, Laboratory of Heating and Ventilation, 1993.

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32

Fluid-Structure Interaction: Modelling, Simulation, Optimisation (Lecture Notes in Computational Science and Engineering Book 53). Springer, 2007.

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33

Davis, Campbell. The simulation and optimisation of manpower requirements for multi-project organisations subject to uncertain workload. 1987.

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34

Methodes et modeles de la recherche operationnelle (Collection Gestion : serie : politique generale, finance et marketing). Economica, 1998.

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35

Spall, James C. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control. Wiley & Sons, Incorporated, John, 2008.

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36

Spall, James C. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control. Wiley & Sons, Incorporated, John, 2005.

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37

Belia, Evangelia, Lorenzo Benedetti, Bruce Johnson, et al., eds. Uncertainty in Wastewater Treatment Design and Operation. IWA Publishing, 2021. http://dx.doi.org/10.2166/9781780401034.

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Uncertainty in Wastewater Treatment Design and Operation aims to facilitate the transition of the wastewater profession to the probabilistic use of simulators with the associated benefits of being better able to take advantage of opportunities and manage risk. There is a paradigm shift taking place in the design and operation of treatment plants in the water industry. The market is currently in transition to use modelling and simulation while still using conventional heuristic guidelines (safety factors). Key reasons for transition include: wastewater treatment simulation software advancements

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38

(Editor), J. Stender, E. Hillebrand (Editor), and J. Kingdon (Editor), eds. Genetic Algorithms in Optimisation, Simulation and Modelling, (Frontiers in Artificial Intelligence and Applications , Vol 23) (Frontiers in Artificial Intelligence and Applications , Vol 23). IOS Press, 1994.

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39

Flexible Robot Manipulators: Modelling, simulation and control. The Institution of Engineering and Technology, 2017.

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40

Saez, Doris A., Aldo Cipriano, and Andrzej W. Ordys. Optimisation of Industrial Processes at Supervisory Level: Application to Control of Thermal Power Plants (Advances in Industrial Control). Springer, 2001.

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41

Al-Alusi, Abdul Sattar Ahmad. The practical use of dynamic optimisation in operational research.: A study of the role of system dynamics simulation and heuristic optimization in operational research and its application as an advanced technique for Policy Design in Defence Models. 1988.

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