摘要
为了更深入研究臭氧发生过程的输入输出关系,提出使用神经网络建立臭氧发生模型。通过200g/h型号的DBD板式臭氧发生器实验平台获取大量实验数据,采取BP神经网络对板式臭氧发生器建立臭氧浓度预测模型。实验结果表明,BP神经网络能够建立较准确的模型,研究结果为进一步完善系统控制提供理论基础。
In order to study the input-output relationship of ozone generation process,a neural network model is proposed in this paper.A large number of experimental data are obtained through the DBD plate type ozone generator experiment platform of 200g/h model.BP neural network was used to establish the ozone concentration prediction model for plate type ozone generator.The experimental results show that BP neural network can establish a more accurate model,and the research results provide a theoretical basis for further improving the system control.
出处
《工业控制计算机》
2020年第7期12-13,16,共3页
Industrial Control Computer
基金
国家自然科学基金“基于CPS框架的烟气脱硫过程智能优化控制”(61873006)
北方工业大学“毓青人才支持计划”资助。
关键词
介质阻挡放电
臭氧发生模型
BP神经网络
dielectric barrier discharge
ozone generation model
BP neural network
