Abstract
For the air compressor system in iron and steel enterprises, an optimal scheduling method for it was proposed, where the predicted value of the production load and the equipment capacity are treated as the model constraints, aiming to reduce the optimal economic cost and improve the energy conversion efficiency. In addition, an optimization method, combining the hierarchical search and the adaptive particle swarm optimization algorithm, was proposed to fully consider the performance of the air compressors, resulting in the great improvement of the search efficiency. In order to further accelerate the computation process of the model, a parallel acceleration algorithm based on Spark framework was also designed. The experimental results show that the proposed method exhibits good performance in the optimization of the air compressor group scheduling problem. In addition, under the premise of algorithm stability, good acceleration effect could be obtained by the Spark parallel algorithm.
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Acknowledgements
This work was supported by the National Key R&D Program of China (2017YFA0700300), the National Natural Sciences Foundation of China (61833003, 61533005), the Fundamental Research Funds for the Central Universities (DUT18TD07, DUT20RC(3)013), and the Outstanding Youth Sci-Tech Talent Program of Dalian (2018RJ01).
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Chen, L., Zhang, X., Zhao, J., Chen, L., Wang, W. (2020). Spark Parallel Acceleration-Based Optimal Scheduling for Air Compressor Group. In: Han, M., Qin, S., Zhang, N. (eds) Advances in Neural Networks – ISNN 2020. ISNN 2020. Lecture Notes in Computer Science(), vol 12557. Springer, Cham. https://doi.org/10.1007/978-3-030-64221-1_3
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DOI: https://doi.org/10.1007/978-3-030-64221-1_3
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