化工进展 ›› 2022, Vol. 41 ›› Issue (11): 5737-5745.DOI: 10.16085/j.issn.1000-6613.2022-0173

• 化工过程与装备 • 上一篇    下一篇

工业制SO3气体浓度实时检测及多元非线性回归建模

孔祥旭(), 张玮(), 胡恒, 于嘉朋, 张坤, 徐娜   

  1. 太原理工大学化学工程与技术学院,山西 太原 030024
  • 收稿日期:2022-01-27 修回日期:2022-04-14 出版日期:2022-11-25 发布日期:2022-11-28
  • 通讯作者: 张玮
  • 作者简介:孔祥旭(1998—),男,硕士研究生,研究方向为化工过程智能检测与系统建模。E-mail:1366283609@qq.com
  • 基金资助:
    国家自然科学基金(22178241);化学工程联合国家重点实验室开放课题(SKL-ChE-21A01)

Real-time detection of industrial SO3 gas concentration and multivariate nonlinear regression modeling

KONG Xiangxu(), ZHANG Wei(), HU Heng, YU Jiapeng, ZHANG Kun, XU Na   

  1. College of Chemical Engineering and Technology, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
  • Received:2022-01-27 Revised:2022-04-14 Online:2022-11-25 Published:2022-11-28
  • Contact: ZHANG Wei

摘要:

工业接触法制SO3气体浓度高、腐蚀性强、交叉影响大,高精度实时检测困难,且原料流量与SO3浓度的关系尚不明确,参数调节依赖经验。针对上述问题,采用AO2电化学氧传感器设计了一种工业制SO3气体浓度的实时检测系统,并构建了输入空气、SO2流量与输出SO3浓度的多元非线性回归模型。SO3气体浓度实时检测系统主要包括AO2电化学氧传感器、传感器调理电路、AD620放大电路及ADS1256 A/D转换电路,通过检测SO3发生装置输入空气与输出混合气体中O2浓度的毫伏级变化,间接测量SO3浓度,避免了其他敏感气体的交叉影响,实验结果表明检测系统的检测时间为27s,灵敏度为111mV/%,偏差小于0.18%,稳定性良好,证明了检测系统的有效性;利用响应面法建立了预测SO3浓度的三次多项式非线性回归模型,对该模型进行方差分析后结果显著,测试后均方误差(MSE)为0.0007174,相关系数(R2)为0.9929,拟合度好、预测精度高。

关键词: 三氧化硫浓度, 实时检测, 仪器仪表, 模型, 预测

Abstract:

In industry, SO3 gas prepared by oxidation of SO2 is of high concentration, strong corrosion and great cross-influence, and difficulty by high precision real-time detection. Moreover, the relationship between input flow and SO3 concentration is not clear, which leads to that the parameter adjustment depends on experience. Aim at the above problems, a real-time system for detection of industrial SO3 gas concentration was designed by AO2 electrochemical oxygen sensor, and a multivariate nonlinear regression model with input air, SO2 flow and output SO3 concentration was established. The real-time detection system of SO3 gas concentration mainly included AO2 electrochemical oxygen sensor, sensor conditioning circuit, AD620 amplifying circuit and ADS1256 A/D conversion circuit. It indirectly measured SO3 concentration by detecting the millivolt level change of O2 concentration in input air and the output mixture gas from SO3 generator. In this way, the cross influence of other sensitive gases was avoided. The experimental results showed that the detection time of the system was 27s, the sensitivity was 111mV/%, the deviation was less than 0.18% and the stability was good, which demonstrated the effectiveness of the detection system. A cubic polynomial nonlinear regression model for predicting SO3 concentration was established by response surface method. The results of variance analysis for this model were very significant with the mean square error (MSE) of the model of 0.0007174 and the correlation coefficient (R2) of 0.9929, which proved that the model had good fitting degree and high prediction precision.

Key words: SO3 gas concentration, real-time detection, instrumentation, model, prediction

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