基于深度学习的催化裂化装置产品收率预测  

FCC UNIT PRODUCT YIELD PREDICTION MODEL BASED ON DEEP LEARNING

作　　者：周宇阳 Zhou Yuyang(SINOPEC Engineering Incorporation,Beijing,100101)

机构地区：[1]中国石化工程建设有限公司,北京100101

出　　处：《石油化工设计》2023年第1期44-51,I0003,共9页Petrochemical Design

摘　　要：催化裂化装置对炼厂生产效益关系重大,准确预测并优化其产品收率和生焦产率对提高装置效益,改善全厂总流程具有重要意义。通过采用深度学习中梯度树(GBDT)算法和机器学习中神经网络(ANN)算法,基于系统内多家炼厂的催化裂化装置生产数据,建立了收率预测模型,总结了针对生产数据的数据处理经验。结果表明:基于深度学习的梯度树算法在预测效率、准确性和稳定性更好,使用人工智能方法能基于大数据准确预测装置产品收率,有助于开展基于数据模型的装置操作优化和全厂总流程优化,提高全厂经济效益。Catalytic cracking unit has a significant impact on refinery production efficiency.Accurate prediction and optimization of its product yield and coke yield is important to improve the efficiency of the unit and the overall process flow of the refinery.In this paper,a yield prediction model was developed based on the production data from the FCC units in several refineries of SINOPEC by applying Gradient Boosting Decision Tree(GBDT)algorithm in deep learning algorithm and the neural network(ANN)algorithm to summarize the data processing experience for production data.The results show that the gradient tree algorithm based on deep learning performs better in prediction efficiency,accuracy and stability.Artificial intelligence methods can accurately predict product yield based on big data,help to carry out unit operation optimization and plant-wide overall process flow optimization based on data model,and improve plant-wide economic efficiency.

关 键 词：深度学习 催化裂化 人工智能 操作优化 

分 类 号：TE96[石油与天然气工程—石油机械设备] TP18[自动化与计算机技术—控制理论与控制工程]