The development of a genetic method to optimize the flue gas desulfurization process

Abstract

Sulfur dioxide is one of the most commonly found gases, which contaminates the air, damages human health and the environment. To decrease the damage, it is important to control the emissions on power stations, as the major part of sulfur dioxide in atmosphere is produced during electric energy generation on power plants. The present work describes flue gas desulfurization process optimizing strategy using data mining. The optimisation modified genetic method of flue gas desulfurization process based on artificial neural network was developed. It affords to represent the time series characteristics and factual efficiency influence on desulfurization and increase its precision of prediction. The vital difference between this developed genetic method and other similar methods is in using adaptive mutation, that uses the level of population development in working process. It means that less important genes will mutate in chromosome more probable than high suitability genes. It increases accuracy and their role in searching. The comparison exercise of developed method and other methods was done with the result that new method gives the smallest predictive error (in the amount of released SO2) and helps to decrease the time in prediction of efficiency of flue gas desulfurization. The results afford to use this method to increase efficiency in flue gas desulfurization process and to decrease SO2 emissions into the atmosphere