A multiscale adaptive framework based on convolutional neural network:Application to fluid catalytic cracking product yield prediction  

作　　者：Nan Liu Chun-Meng Zhu Meng-Xuan Zhang Xing-Ying Lan 

机构地区：[1]College of Artificial Intelligence,China University of Petroleum(Beijing),Beijing,102249,China [2]State Key Laboratory of Heavy Oil Processing,China University of Petroleum(Beijing),Beijing,102249,China

出　　处：《Petroleum Science》2024年第4期2849-2869,共21页石油科学（英文版）

摘　　要：Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators.While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables,it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes.In light of this,a novel Multiscale Multi-radius Multi-step Convolutional Neural Network(Msrt Net)is proposed for mining spatiotemporal multiscale information.First,the industrial data from the Fluid Catalytic Cracking(FCC)process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)extract the multi-energy scale information of the feature subset.Then,convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data.Finally,a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output.Msrt Net is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process(TEP).Subsequently,the performance of Msrt Net is evaluated in predicting product yield for a 2.80×10^(6) t/a FCC unit,taking diesel and gasoline yield as examples.In conclusion,Msrt Net can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30%in prediction error compared to other time-series models.Furthermore,its robustness and transferability underscore its promising potential for broader applications.

关 键 词：Fluid catalytic cracking Product yield Data-driven modeling Multiscale prediction Data decomposition Convolution neural network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TE624.41[自动化与计算机技术—控制科学与工程]