Application Of Data Mining Technology In Desulfurization Monitoring Intelligent Environmental Protection Systems

As the resource-conserving and environment-friendly society is identified as a long-term plan for the national economic and social development strategy,the environmental protection is becoming increasingly important.With the development of social economy in this country,environmental problems become more and more challenging,and the traditional way of environment management has been difficult to meet the requirements of environmental conservation from the general public.As a result,it has become a consensus to resort to the tools of information technology for environmental monitoring and control.Currently,owing to the environmental protection department personnel shortage,and lack of supervision and capability,the environment conservation is in serious danger.Therefore,it is imminent to use modern science and technology to improve environmental regulation ability.It is desirable to apply data mining,model technology,artificial intelligence and other advanced technology in an integrated way,to achieve intelligent environmental decision-making.Among the pollutants in the power industry emissions,sulfur dioxide so far has the most serious impact on the environment.In the desulfurization process,it is common to have complex data relationships,high real-time,the huge amount of monitoring data.In order to deal with these data,and to facilitate better environmental protection and pollution control over the total emissions,data mining has become an important research topic.The theme of this thesis is to analyze correlation between data,via data mining technology and data association rules,to improve overall decision-making capabilities,and then to provide a reliable basis for intelligent environmental protection.This thesis first introduces the intelligent environmental protection system,including its composition and meaning.Then,we describe the building of intelligent environmental systems,especially the main measures this country has taken.Thirdly,we introduce data mining technology for use in desulfurization monitoring,including concepts,technologies and basic steps of data mining.Finally,we select the data association rules,to be applied to desulurization monitoring data for data mining,and propose parameters fluctuation model,and parameters predictive model.We verify the proposed algorihms using actual monitoring data of desulurization processes,and illustrate that the application of the models has achieved good performance in desulfurization monitoring.