摘要
在石油化工行业中,温度是生产过程中一个关键的操作参数,确保仪表设备工作在安全的温度范围十分重要。通过分析伴热温度对生产过程的影响,利用现代化的仪表和数据分析技术,开展关于仪表伴热温度的自学习模型的研究,通过伴热自学习模型测试与验证,实现仪表伴热温度精准预测,帮助优化能效和提升设备运行稳定性,节约能源以及提升生产效率。
In petrochemical industry,temperature is a key operating parameter in the production process,it is inevitably important to ensure that instruments operate within a safe temperature range.By analyzing the influence of the heat tracing temperature for production process,and utilizing modern instruments and data analysis techniques,study on self-learning models heat tracing temperature of for instrument is developed,through testing and verification of the self-learning model for heat tracing,to achieve precise prediction of heat tracing temperature of instrument,and help to optimize energy efficiency and equipment operation stability,help optimizing energy efficiency,improving equipment operating stability,saving energy and enhancing efficiency.
作者
李洪滨
Li Hongbin(Sinopec Tianjin Company,Tianjin,300271,China)
出处
《石油化工自动化》
2025年第3期20-22,共3页
Automation in Petro-chemical Industry
关键词
仪表伴热
自学习
模型算法
温度监测
instrument heat tracing
self-learning
model algorithm
temperature monitoring
