Predicting particle collection performance of a wet electrostatic precipitator under varied conditions with artificial neural networks

Optimal environmental sampling conditions for electrostatic aerosol-to-hydrosol collection of airborne viruses

Due to adverse effects of viral outbreaks on human health, accurate detection of airborne pathogens is essential. Among many methods available for bioaerosol sampling, electrostatic precipitation (ESP) has been used to directly collect bioaerosols as hydrosols. The performance of an ESP sampler depends on its design, operational and environmental parameters such as air relative humidity (RH), air temperature, sampling liquid type and liquid temperature. Thus, it is essential to identify and maintain optimal conditions throughout sampling process to operate the sampler at its highest capacity. This study provides crucial insights into parameters that affect the collection efficiency of the aerosol-to-hydrosol ESP sampler and its virus recovery. The results indicate that air temperature does not affect collection efficiency, meanwhile, air RH, sampling liquid temperature, and salt concentration are the main parameters that significantly affect collection efficiency. Likewise, when deionized water is used as sampling liquid, hydrogen peroxide concentration increases proportionally with increasing air RH, resulting in significant decrease of virus viability. Consequently, for ESP samplers similar to our study, the following conditions are recommended: air RH of 55-65%, air and sampling liquid temperature of 37 °C, and a mixture of 10-20 mM ascorbic acid in PBS as sampling liquid.

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

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