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Cui Q.,Beijing University of Chemical Technology | Guo F.,Beijing University of Chemical Technology | Li Q.-S.,Beijing University of Chemical Technology | Wang K.,Daqing Petrochemical Company | And 3 more authors.
Xiandai Huagong/Modern Chemical Industry | Year: 2015

The design principle and structure characteristics of BH-type high efficiency packing and its application in the pre-cooling tower of coal to olefins are introduced. The improved composite filler tower has achieved the expected goals, including improving the separating effect and the purity of the product, expanding the production and quality, and reducing energy consumption. © 2015, China National Chemical Information Center. All right reserved.

Li Q.-S.,Beijing University of Chemical Technology | Li Q.-S.,Beijing Century Robust Technology Co. | Cao L.-L.,Beijing University of Chemical Technology | Pan L.-D.,Beijing University of Chemical Technology | And 2 more authors.
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | Year: 2011

Based on the operating data of process, the dynamic response of PID loop is obtained automatically. Through giving the insensitive loss function and model reliability function, the multiple models of the control loop are achieved. These multiple models which represent as process transfer function are obtained by the closed-loop identification method. Based on the acquired multiple models, the internal model controller with multi-model structure is built. The internal model controller, which has a perfect effect for all the models, is obtained by optimal stochastic algorithm. Without field testing, the new method can complete the model identification and parameter tuning circularly under the normal production conditions of petrochemical plants. The practical engineering application proves that the method is effective.

Li Q.,Beijing University of Chemical Technology | Li Q.,Beijing Century Robust Technology Co. | Zhang Y.,Petrochina | Cao L.,Beijing University of Chemical Technology | And 2 more authors.
Huagong Xuebao/CIESC Journal | Year: 2011

A dual model RBF (radial basis function) neural network was proposed in this paper. One is used for self-learning, which learns one time a day. The other is used for on-line correcting, which is the running model currently. Both the self-learning model and the on-line correcting model are corrected six times every day and should track the current conditions of the system quickly. At the same time, the accuracy of the two models should be compared. If the accuracy of the on-line correcting model is less than the one of the self-learning model, the latter becomes the new currently running model instead of the old one. Otherwise, the currently model is maintained. To solve the problem of neural network large prediction errors, a network algorithm analysis is given and the influence factors of the network prediction accuracy are found. At last, an improved algorithm of RBF neural network modeling is proposed, which combines K-means clustering method with the recursive descent algorithm. Simulation and practical application proved the effectiveness of the improved method. © All Rights Reserved.

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