Overview for Pipeline Scheduling
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-9899-7_1
Reference25 articles.
1. Q. Liao, Y. Liang, N. Xu, H. Zhang, J. Wang, and X. Zhou, “An MILP approach for detailed scheduling of multi-product pipeline in pressure control mode,” Chemical Engineering Research and Design, vol. 136, pp. 620–637, 2018/08/01/ 2018, doi: https://doi.org/10.1016/j.cherd.2018.06.016.
2. X. Zhou et al., “Future scenario of China’s downstream oil supply chain: Low carbon-oriented optimization for the design of planned multi-product pipelines,” Journal of Cleaner Production, vol. 244, p. 118866, 2020/01/20/ 2020, doi: https://doi.org/10.1016/j.jclepro.2019.118866.
3. X. Zhou et al., “A hybrid time MILP model for the pump scheduling of multi-product pipelines based on the rigorous description of the pipeline hydraulic loss changes,” Computers & Chemical Engineering, vol. 121, pp. 174–199, 2019/02/02/ 2019, doi: https://doi.org/10.1016/j.compchemeng.2018.10.001.
4. L. Fan et al., “A deep reinforcement learning-based method for predictive management of demand response in natural gas pipeline networks,” Journal of Cleaner Production, vol. 335, p. 130274, 2022/02/10/ 2022, doi: https://doi.org/10.1016/j.jclepro.2021.130274.
5. L. Yu, S. Wang, and Q. Xu, “Optimal scheduling for simultaneous refinery manufacturing and multi oil-product pipeline distribution,” Computers & Chemical Engineering, vol. 157, p. 107613, 2022/01/01/ 2022, doi: https://doi.org/10.1016/j.compchemeng.2021.107613.
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