Abstract
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.
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Acknowledgments
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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Chen, XF., Qian, YC., Wang, YL. (2017). Power Consumption Prediction for Dynamic Adjustment in Hydrocracking Process Based on State Transition Algorithm and Support Vector Machine. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_9
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DOI: https://doi.org/10.1007/978-3-319-70139-4_9
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