Both the increasing demand for reduced operating costs and the drive to better understand chemical processes has led to an increased focus on the optimisation of chemical engineering applications. Many of these mathematical problems are highly non-linear and numerically stiff with large parameter spaces that further lead to non-convex objective functions.
Meta-heuristic evolutionary optimization techniques have been identified as a promising approach for solving these challenging optimization problems, as they do not consider single initial guesses but ranges within the parameter search space. In such evolutionary optimization procedures, a population of possible solutions is progressively modiﬁed via appropriate operators, so as to identify the individuals that exhibit the best performance e.g. in describing an experimental dataset or minimizing some cost related objective function.
A class of evolutionary algorithms that exhibits many similarities to the widely researched genetic algorithms is that of Particle Swarm Optimization (PSO). The two methodologies share many common features, both initializing a population of possible solutions in similar ways, further utilizing “fitness” functions to determine the quality of each possible solution. Moreover, both consider generations of solutions, meaning that they repeat a predetermined set of operations for specific times. It has been demonstrated that PSO methods can reach solutions of equal or better quality to genetic algorithms and a wide variety of other optimization methods, many times at a reduced computational cost.
Aim of the current project is to investigate the performance of such methods extensively in a variety of chemical engineering applications to identify optimal implementation areas and approaches. Initial fields of research will be in relation to kinetic parameter determination and batch processing plant scheduling problems. A wider panel of process design and control problems encountered in chemical engineering can also be eventually considered, such as real-time PID controllers tuning, refrigeration cycles optimization, etc.
The successful candidate should have, or expect to have an Honours Degree at 2.1 or above (or equivalent) in Chemical Engineering with knowledge of Mathematical optimization methods, Process design and control, Programming in MATLAB or similar.
Zhang, B., et al., 2013, Industrial and Engineering Chemistry Research, 52, pp. 17074-17086.
Kumar, V., Balasubramanian, P., 2009, Fuel, 88, pp. 2171–2180.
Khan, M., Lee, M., 2013, Energy, 49, pp. 146-155
Wu, X., et al., 2014, Journal of Natural Gas Science and Engineering, 21, pp. 10-18.
Fang, H., et al., Z., 2011, Energy Conversion and Management, 52, pp. 1763-1770.
Liua, B., et al., 2012, Computers and Chemical Engineering, 34, pp. 518-528.
Formal applications can be completed online: http://www.abdn.ac.uk/postgraduate/apply. You should apply for PhD in Engineering, to ensure that your application is passed to the correct College for processing. Please ensure that you quote the project title and supervisor on the application form.
Informal inquiries can be made to Dr P Kechagiopoulos, ([email protected]) with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Graduate School Admissions Unit ([email protected]).