摘要
装置产品收率的估算是前期全厂方案设计的重要环节,利用神经网络技术进行装置收率预测的效率高于传统的人工估算,也是石化项目前期设计信息化的发展方向之一。基于开源语言Python和PHP的石化项目设计前期神经网络系统,建立了一个适用于石化项目设计前期阶段的MIP工艺流化床催化裂化(FCC)装置产品收率预测的组合模型。结果表明,其预测结果与FCC装置产品收率一致。
The product yield estimation is a crucial part of the whole plant design in the early stage of petrochemical engineering.Previous research has shown the neural network technologies can perform higher efficient yield prediction than traditional manual estimation and is one of the main trends of the petrochemical engineering informatization in the early stage.In this paper,we designed and preliminarily implemented a petrochemical neural network system using Python and PHP as the implementation means,and established a combination model for the yield prediction of fluid catalytic cracking(FCC)unit(MIP process)applicable to the early stage of petrochemical design.The results show that the yields of MIP process predicted by the neural network models are consistent with the actual yields in production process.
作者
刘洋
苑丹丹
李浩
高雪颖
Liu Yang;Yuan Dandan;Li Hao;Gao Xueying(SINOPEC Engineering Incorporation,Beijing 100101)
出处
《石油炼制与化工》
CAS
CSCD
北大核心
2021年第3期87-92,共6页
Petroleum Processing and Petrochemicals
