Global Optimisation of Gasoline Pool Blending Using Constraint Partitioning
Aliyu Musa Aliyu *
Department of Chemical Engineering, Federal University of Technology Minna, PMB 65, Minna, Niger State, Nigeria and Oil and Gas Engineering Centre, Cranfield University, Bedfordshire MK43 0AL, United Kingdom
Sadiq Muhammad Munir
Department of Chemical Engineering, Federal University of Technology Minna, PMB 65, Minna, Niger State, Nigeria and Department of Materials Engineering, Monash University, Victoria 3800, Australia
Musa Umaru
Department of Chemical Engineering, Federal University of Technology Minna, PMB 65, Minna, Niger State, Nigeria
Ibrahim Aris Mohammed
Department of Chemical Engineering, Federal University of Technology Minna, PMB 65, Minna, Niger State, Nigeria
Oyewole Adedipe
Department of Mechanical Engineering, Federal University of Technology Minna, PMB 65 Minna, Niger State, Nigeria and Offshore Renewable Energy Centre, Cranfield University, Bedfordshire MK43 0AL, United Kingdom
Baba Yahaya Danjuma
Oil and Gas Engineering Centre, Cranfield University, Bedfordshire MK43 0AL, United Kingdom
Adegboyega Ehinmowo
Oil and Gas Engineering Centre, Cranfield University, Bedfordshire MK43 0AL, United Kingdom
Solomon Alagbe
Department of Chemical Engineering, Ladoke Akintola University of Technology, PMB 4000, Ogbomoso, Nigeria
*Author to whom correspondence should be addressed.
Abstract
Aims: A hybrid Nonlinear Programming–Simulated Annealing method has been applied to solving the constrained offline gasoline recipe optimisation problem using constraint partitioning.
Methodology: The method was demonstrated by applying it to a small blending case study with eighteen independent variables where one of the variables was used as a link variable between the two sub-problems of the partitioned non-convex problem. It is noted that this can in theory be extended to larger tightly constrained problems with more link variables e.g. whole refineries where the models involve huge numbers of nonlinear equations and many process units.
Results: The approach exhibited good performance representing significant savings against both a derivative-based NLP method used alone and a Mixed Integer Non-Linear Programming method. This performance was examined by way of a sensitivity analysis of the simulated annealing parameters.
Conclusion: The convergence times were in minutes and are realistic for short-term recipe optimisation.
Keywords: Gasoline blending, simulated annealing, constraint partitioning, stochastic optimisation