Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters

Abstract

A comparative analysis for improving the efficiency of 100MW Delta IV Ughelli gas turbine power plant is performed. The study used non-dominated sorting genetic and pattern search algorithms to minimize the objective function by optimally adjusting the operating parameters (decision variables). The adjusted operating variables were compressor inlet temperature (T₁), compressor pressure ratio (r_p), compressor isentropic efficiency (ɳ_ic), turbine isentropic efficiency (ɳ_it), turbine exhaust temperature (T₄) and air mass flow rate (m_a), fuel mass flow rate (m_f) and fuel supply temperature (T_f). The ambient temperature and pressure were held constant at 304K and 1.01325bar respectively because of location limitation. The optimization code was written in Matlab programming language. The decision variables (constraints) were obtained randomly within the admission range. The GA and PS optimal values of the decision variables were obtained by minimizing the objective function. The determined GA and PS optimum operating variables have the same values which were compressor pressure ratio (r_n) = 9.76, compressor isentropic efficiency (ɳ_ic) = 86.40%, turbine isentropic efficiency (ɳ_it) = 89.12%, combustion chamber outlet temperature (T₃) = 1481.8K, air mass flow rate = 530kg/s, fuel mass flow rate = 7.00kg/s. The total exergy destruction cost rate (_D) for PS and GAvaries by +0.00004% and the total investment cost rate for PS and GAvaries by +0.00038%. The results show that there is slight increase in total exergy destruction cost rate and total capital investment cost rate in PS optimum when compared to GA optimum. This shows that GA is better than PS as an optimization algorithm.