Genetic method for optimizing the process of desulfurization of flue gases from sulfur dioxide

Sulfur dioxide is one of the most commonly found gases, which contaminates the air, damages human health and the environment. To reduce the damage, it is important to control the emissions on power stations, as the major part of sulfur dioxide in the atmosphere is produced during electric energy generation on power plants. The present work describes flue gas desulfurization process optimizing strategy using data mining. Determining the relationship between process parameters and the actual efficiency of the absorption process is an important task for improving the performance of flue gas desulfurization plants and optimizing future plants. To predict the efficiency of cleaning from SO2 emissions, a model of wet flue gas desulfurization was developed, which combines a mathematical model and an artificial neural network. The optimization 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 the developed method and other methods was done with the result that the 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 allow to use this method to increase efficiency in flue gas desulfurization process and to reduce SO2 emissions into the atmosphere