Research On Industrial Air Pollution Control Of Urumqi City Under Uncertainty

With the rapid development of economy and industry, energy consumption of Urumqi is increasing rapidly. Sulfur dioxide coming from the burning of fossil fuels is discharged to the surrounding atmospheric environment. Now, Urumqi is becoming one of the most sulfur dioxide polluted cities in China, and thus researches related to industrial air pollution control is imminent for the city. In recent years, air quality management models have been studied and further applied in many regional air pollutants emission reduction works.This study developed an interval-parameter air quality management programming model (IAQM) based on uncertainty analysis for Urumqi. Interval programming approaches were introduced into the modeling framework to deal with uncertain parameters. The objective of the model is to approach a minimum capital cost for running the desulfurization systems of various factories in Urumqi, and obtain the pollution control plan. The optimization results indicated that, the industrial enterprises far away from the city center showed a low SO2reduction request; in contrast, the factories closed to downtown need to adopt high-efficient desulfurization equipments to reduce the air pollution to the surrounding environment. With the time going, more and more factories have to use high-efficient desulfurization technologies due to the increased raw material consumption and SO2emissions. Meanwhile, the importance of recovering desulphurization by-products was discussed under different conditions. It was implied that recovery and reuse the by-products generated during the desulfurization processes can not only reimburse the economic cost of flue gas desulfurization, but also enhance the environmental benefits.This research developed an IAQM approach for planning industrial air quality management systems in Urumqi under uncertainty. It can quantitatively reflect the characteristics of various kinds of uncertainties in air quality management systems. The case study showed that the model can not only suggest different pollution control plans various by-products marketing conditions, but also predict SO2reduction under continuously increased energy consumption conditions in the future.