NDLI: Study on soft measurement of SO2 conversion ratio of producing sulfuric acid from metallurgical off-gas based on MLR

Content Provider	IEEE Xplore Digital Library
Author	Ningning Wang Qingrong Huang Lihui Feng
Copyright Year	2013
Description	Author affiliation: Yunnan Copper Ind. Group Co. Ltd., Kunming, China (Qingrong Huang) \|\| Faculity of Inf. Eng. & Autom., Kunming Univ. of Sci. & Technol., Kunming, China (Ningning Wang; Lihui Feng)
Abstract	Producing sulfuric acid from metallurgical off-gas process is a complex system, $SO_{2}$ conversion ratio is a key factor in the process of producing sulfuric acid from metallurgical off-gas, but it is difficult to direct on-line monitoring, is generally obtained by artificial test. Therefore, This article set a smelter's " two convert and two absorption" of producing sulfuric acid from metallurgical process as a study focus, the soft-sensing model of $SO_{2}$ conversion ratio was established by correlation analysis and pre-processing of the field data and multiple linear regression analysis method (MLR) was used. By quantitative analysis of the model fitting and forecasting effect, it indicated that the model has good predictive effect. It can be said that this study will be played an important role in prediction and monitoring online of $SO_{2}$ conversion ratio of producing sulfuric acid from metallurgical off-gas process.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	3091
Ending Page	3094
File Size	238355
Page Count	4
File Format	PDF
ISBN	9781467355339
e-ISBN	9781467355346
DOI	10.1109/CCDC.2013.6561476
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models Correlation Multiple Linear Regression Absorption Heating Linear regression Soft-Sensing Predictive models Producing Sulfuric Acid from Metallurgical Off-gas SO2 Conversion Ratio Mathematical model
Content Type	Text
Resource Type	Article

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