Power Consumption Prediction for Dynamic Adjustment in Hydrocracking Process Based on State Transition Algorithm and Support Vector Machine

  • Xiao-Fang Chen
  • Ying-Can Qian
  • Ya-Lin WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)


Power consumption is an important part of energy consumption in hydrocracking, which occupies about 43%–47% of the total energy consumption. In the daily production management, the real-time power consumption is manually recorded from the voltmeter. However, it is difficult to collect the power consumption especially in the dynamic adjustment. In this paper, a power consumption prediction model is proposed for dynamic adjustment in the hydrocracking process, which is based on state transition algorithm (STA) and support vector machine (SVM). A SVM regression model is developed to map the complex nonlinear relationship between power parameters and the power consumption in the dynamic adjustment of hydrocracking, and the state transition algorithm is used to optimize the parameters of SVM regression model. The experimental results demonstrate that the prediction accuracy of the model is close to the fitting accuracy and the modeling time is reduced.


Hydrocracking Dynamic adjustment Power consumption prediction model State transition algorithm-support vector machine (STA-SVM) 



This work is supported by the Major Program of the National Natural Science Foundation of China (61590921); the Program of the National Natural Science Foundation of China (61374156); the Fundamental Research Funds for the Central Universities of Central South University (2017zzts707).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina

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