Multi-Objective Linear-Regression-Based Optimization Of A Hybrid Solar-Gas Turbine Combined Cycle With Absorption Inlet-Air Cooling Unit

Gas turbine (GT) power plants suffer from sensitivity to ambient-air temperature, high fuel consumption, and a high amount of waste heat dumped into the ambient. Various solutions were proposed to solve these drawbacks, which could simultaneously solve at most two problems, usually, at the expense of the third. In this work, however, a novel integration consisting of cascaded solar heat exchangers and a combined cycle power plant CCPP was used to simultaneously address the GTs shortcomings. Parabolic trough collectors PTCs were used to initially preheat the air at the combustion chamber inlet. The collectors were then used to drive an absorption inlet-air cooling cycle that will control the air temperature at the compressor's inlet. Simple design point analysis of the proposed integration showed an increase in thermal efficiency by 7.2% and a 27.7 MW increase in power output. A linear-regression LR based optimization was then performed to generate highly accurate polynomial equations for predicting the system's performance. These equations were then used in a Genetic Algorithm GA multi-objective optimization MOO with efficiency and capital cost as conflicting objectives. The optimum thermal efficiency was found to be in the range of 53.1 - 61.11% corresponding to a capital cost of the novel system in the range of 86.5-158.3 Mil$. A favorable optimum point for both objectives was found at an efficiency of 57.86% corresponding to a capital cost of 131.8 Mil$. Feasibility in terms of the payback period (PBP) was explored for the optimum range; resulting in a PBP equal to 2.8 years, an implication of a feasible novel cycle.