INTELLIGENT COMPUTING FOR PROCESS OPTIMISATION
(Funding source and collaborators: EU, Fluent, INSPIRE partners)
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Anna Kononova pmak@leeds.ac.uk Tel: +44 0113 343 2481 Research Interest: Advanced Computational Intelligence Optimisation Techniques for Engineering Applications
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Working on the efficient choice of proper computational intelligence techniques for solving various real-world applications, including inverse problem of chemical kinetics. At the current stage of the research two competitive Computational Intelligence approaches for solving the problem of retrieving chemical reaction rate parameters for the combustion process of a hydrogen/nitrogen/oxygen mixture for two different types of the domains are proposed. Moreover, some additional studies on the problem domain have been performed. Much emphasis is given to the question of algorithmic design. |
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Bogdan Wojtowicz pmbww@leeds.ac.uk Tel: +44 0113 343 3824 Research Interest: Genetic Algorithms |
The ultimate objective of my research is to develop and examine a maximally universal, efficient and reliable optimisation tool which can be used for identification or decision making purposes or to include as a module to the software package for Thermo-Analysis (TEA) and Exergy Life-Cycle Analysis (ELCA). The complexity of today's optimisation problems is so high that a hybrid version of the evolutionary approach and an efficient deterministic local search algorithm is required. Global search methods such as genetic algorithms, evolution strategies or other methods such as simulated annealing which avoid the problems associated with the local optima, lack of continuity or impossibility to use gradients of the goal function are the base of further development. |
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Dr Kevin Hughes |
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Prof Derek Ingham |
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Professor MC Pourkashanian |