Analysis of the pareto front of a multi-objective optimization problem for a fossil fuel power plant

This paper examines the Pareto front of a simple fossil fuel power plant using a common third-order model. This front is first examined analytically. Then the power plant model is transferred over to a steady-state model using a static neural network and the front is estimated using various geometric and heuristic approaches. This paper is the first of the two stages to eliminate the need for a human to construct a single objective cost function from multiple objectives for use in the power plant optimization. To generate the Pareto front of the power plant, three different optimization techniques are explored, normal-boundary intersection utilizing differential equation, multi-objective particle swarm optimization, and multi-objective evolutionary algorithm optimization.