Application of neural network modeling and constrained particle swarm optimization of steam production from biomass power plant

Abstract

In this paper, the steam production of the power plant from four biomass types has been investigated via the combination of the neural network (NN) model and constrained particle swarm optimization (CPSO) methods. First, the power plant modeling was accurately developed using NN simulation. Then, the model was applied to the optimization step, where the CPSO (the modified algorithm from the classical PSO) was introduced. The proposed method included real operation constraints to the algorithm, such as biomass feed availability and minimum steam production rate. In the optimization step, the COP has been defined as the cost function to check the present operation’s economic viability. To obtain the minimum production cost (COP), the CPSO suggested feeding wood bark from sources C and D with the amount of 104.1 and 463.8 tons/day, respectively. The result has been extensively validated by the real operation. The calculation framework in this study then applies to various production systems. Besides, by integrating the cost function in the optimization model with other indicators such as environmental impact indices, the output would provide an additional holistic picture of economic and environmental aspects.