Technical Papers
Jun 22, 2023

Scheduling of Straight Multiproduct Pipelines Considering the Contamination Control

Publication: Journal of Pipeline Systems Engineering and Practice
Volume 14, Issue 4

Abstract

Batch interface tracking and contamination control are the key technologies for the operation and management of multiproduct pipelines. Existing studies only focused on sequence arrangement or segment stoppage to control the generated contamination, and few considered this problem comprehensively and systematically. This study fully considers the contamination control process and develops a scheduling model for straight pipelines to minimize the cost of contamination loss caused by segment stoppage. The objective of this model is to minimize the restart cost of idle segments and the penalty cost due to improper interface placement during segment stoppage. Three types of constraints for contamination control are proposed, namely operation control, flow rate control, and stoppage control. Three real-world multiproduct pipelines are used as examples to validate the proposed method. Compared with the actual operation schemes, the method helps to reduce the number of stoppage operations by 20%–50%, and decrease the probability that the interface is improperly located by 40%–67%. Therefore, this work can reduce the cost of contamination treatment and bring greater benefits to pipeline operators.

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Data Availability Statement

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (52242211). The authors are grateful to all study participants.

References

Bamoumen, M., S. Elfirdoussi, L. Ren, and N. Tchernev. 2023. “An efficient GRASP-like algorithm for the multi-product straight pipeline scheduling problem.” Comput. Oper. Res. 150 (Feb): 106082. https://doi.org/10.1016/j.cor.2022.106082.
Cafaro, D. C., and J. Cerdá. 2004. “Optimal scheduling of multiproduct pipeline systems using a non-discrete MILP formulation.” Comput. Chem. Eng. 28 (10): 2053–2068. https://doi.org/10.1016/j.compchemeng.2004.03.010.
Cafaro, D. C., and J. Cerdá. 2012. “Rigorous scheduling of mesh-structure refined petroleum pipeline networks.” Comput. Chem. Eng. 38 (Mar): 185–203. https://doi.org/10.1016/j.compchemeng.2011.11.007.
Cafaro, D. C., J. Cerdá, and E. C. Research. 2009. “Optimal scheduling of refined products pipelines with multiple sources.” Ind. Eng. Chem. Res. 48 (14): 6675–6689. https://doi.org/10.1021/ie900015b.
Cafaro, V. G., D. C. Cafaro, C. A. Méndez, and J. Cerdá. 2015. “Optimization model for the detailed scheduling of multi-source pipelines.” Comput. Ind. Eng. 88 (Oct): 395–409. https://doi.org/10.1016/j.cie.2015.07.022.
Castro, P. M. 2017. “Optimal scheduling of multiproduct pipelines in networks with reversible flow.” Ind. Eng. Chem. Res. 56 (34): 9638–9656. https://doi.org/10.1021/acs.iecr.7b01685.
Castro, P. M., and H. Mostafaei. 2019. “Batch-centric scheduling formulation for treelike pipeline systems with forbidden product sequences.” Comput. Chem. Eng. 122 (Mar): 2–18. https://doi.org/10.1016/j.compchemeng.2018.04.027.
Chen, H., L. Zuo, C. Wu, and Q. Li. 2019. “An MILP formulation for optimizing detailed schedules of a multiproduct pipeline network.” Transp. Res. Part E Logist. Transp. Rev. 123 (Mar): 142–164. https://doi.org/10.1016/j.tre.2019.01.012.
Chen, H., L. Zuo, C. Wu, L. Wang, F. Diao, J. Chen, and Y. Huang. 2017. “Optimizing detailed schedules of a multiproduct pipeline by a monolithic MILP formulation.” J. Pet. Sci. Eng. 159 (Nov): 148–163. https://doi.org/10.1016/j.petrol.2017.09.036.
de Souza Filho, E. M., L. Bahiense, and V. J. M. Ferreira Filho. 2013. “Scheduling a multi-product pipeline network.” Comput. Chem. Eng. 53 (Jun): 55–69. https://doi.org/10.1016/j.compchemeng.2013.01.019.
Harjunkoski, I., C. T. Maravelias, P. Bongers, P. M. Castro, S. Engell, I. E. Grossmann, J. Hooker, C. Méndez, G. Sand, and J. Wassick. 2014. “Scope for industrial applications of production scheduling models and solution methods.” Comput. Chem. Eng. 62 (Mar): 161–193. https://doi.org/10.1016/j.compchemeng.2013.12.001.
Herrán, A., J. M. de la Cruz, and B. de Andrés. 2010. “A mathematical model for planning transportation of multiple petroleum products in a multi-pipeline system.” Comput. Chem. Eng. 34 (3): 401–413. https://doi.org/10.1016/j.compchemeng.2009.11.014.
Li, Z., Y. Liang, Q. Liao, N. Xu, J. Zheng, and H. Zhang. 2021. “Scheduling of a branched multiproduct pipeline system with robust inventory management.” Comput. Ind. Eng. 162 (Dec): 107660. https://doi.org/10.1016/j.cie.2021.107760.
Liao, Q., P. M. Castro, Y. Liang, and H. Zhang. 2019a. “Computationally efficient MILP model for scheduling a branched multiproduct pipeline system.” Ind. Eng. Chem. Res. 58 (13): 5236–5251. https://doi.org/10.1021/acs.iecr.8b06490.
Liao, Q., P. M. Castro, Y. Liang, and H. Zhang. 2019b. “New batch-centric model for detailed scheduling and inventory management of mesh pipeline networks.” Comput. Chem. Eng. 130 (Nov): 106568. https://doi.org/10.1016/j.compchemeng.2019.106568.
Liao, Q., Y. Liang, N. Xu, H. Zhang, J. Wang, and X. Zhou. 2018a. “An MILP approach for detailed scheduling of multi-product pipeline in pressure control mode.” Chem. Eng. Res. Des. 136 (Aug): 620–637. https://doi.org/10.1016/j.cherd.2018.06.016.
Liao, Q., H. Zhang, Y. Wang, W. Zhang, and Y. Liang. 2018b. “Heuristic method for detailed scheduling of branched multiproduct pipeline networks.” Chem. Eng. Res. Des. 140 (Dec): 82–101. https://doi.org/10.1016/j.cherd.2018.10.003.
Liao, Q., H. Zhang, T. Xia, Q. Chen, Z. Li, and Y. Liang. 2019c. “A data-driven method for pipeline scheduling optimization.” Chem. Eng. Res. Des. 144 (Apr): 79–94. https://doi.org/10.1016/j.cherd.2019.01.017.
Liao, Q., H. Zhang, N. Xu, Y. Liang, and J. Wang. 2018c. “A MILP model based on flowrate database for detailed scheduling of a multi-product pipeline with multiple pump stations.” Comput. Chem. Eng. 117 (Sep): 63–81. https://doi.org/10.1016/j.compchemeng.2018.05.002.
Magatão, L., L. V. R. Arruda, and F. Neves. 2004. “A mixed integer programming approach for scheduling commodities in a pipeline.” Comput. Chem. Eng. 28 (1–2): 171–185. https://doi.org/10.1016/S0098-1354(03)00165-0.
Meira, W. H. T., L. Magatão, F. Neves Jr., L. V. R. Arruda, J. P. Vaqueiro, S. Relvas, and A. P. Barbosa-Póvoa. 2021. “A solution framework for the long-term scheduling and inventory management of straight pipeline systems with multiple-sources.” Comput. Oper. Res. 127 (Mar): 105143. https://doi.org/10.1016/j.cor.2020.105143.
Meira, W. H. T., L. Magatão, S. Relvas, A. P. Barbosa-Póvoa, F. Neves, and L. V. R. Arruda. 2018. “A matheuristic decomposition approach for the scheduling of a single-source and multiple destinations pipeline system.” Eur. J. Oper. Res. 268 (2): 665–687. https://doi.org/10.1016/j.ejor.2018.01.032.
Moradi, S., S. A. MirHassani, and F. Hooshmand. 2019. “Efficient decomposition-based algorithm to solve long-term pipeline scheduling problem.” Pet. Sci. 16 (Oct): 1159–1175. https://doi.org/10.1007/s12182-019-00359-3.
Mostafaei, H., and P. M. Castro. 2016. “Continuous-time scheduling formulation for straight pipelines.” AIChE J. 63 (6): 1923–1936. https://doi.org/10.1002/aic.15563.
Mostafaei, H., P. M. Castro, and A. Ghaffari-Hadigheh. 2015. “A novel monolithic MILP framework for lot-sizing and scheduling of multiproduct treelike pipeline networks.” Ind. Eng. Chem. Res. 54 (37): 9202–9221. https://doi.org/10.1021/acs.iecr.5b01440.
Mostafaei, H., P. M. Castro, and A. Ghaffari-Hadigheh. 2016. “Short-term scheduling of multiple source pipelines with simultaneous injections and deliveries.” Comput. Oper. Res. 73 (Sep): 27–42. https://doi.org/10.1016/j.cor.2016.03.006.
Rejowski, R., and J. M. Pinto. 2003. “Scheduling of a multiproduct pipeline system.” Comput. Chem. Eng. 27 (8–9): 1229–1246. https://doi.org/10.1016/S0098-1354(03)00049-8.
Rejowski, R., and J. M. Pinto. 2004. “Efficient MILP formulations and valid cuts for multiproduct pipeline scheduling.” Comput. Chem. Eng. 28 (8): 1511–1528. https://doi.org/10.1016/j.compchemeng.2003.12.001.
Relvas, S., A. P. F. D. Barbosa-Póvoa, and H. A. Matos. 2009. “Heuristic batch sequencing on a multiproduct oil distribution system.” Comput. Chem. Eng. 33 (3): 712–730. https://doi.org/10.1016/j.compchemeng.2008.10.012.
Relvas, S., S. N. Boschetto Magatão, A. P. F. D. Barbosa-Póvoa, and F. Neves. 2013. “Integrated scheduling and inventory management of an oil products distribution system.” Omega 41 (6): 955–968. https://doi.org/10.1016/j.omega.2013.01.001.
Relvas, S., H. A. Matos, A. P. F. D. Barbosa-Povoa, and J. Fialho. 2007. “Reactive scheduling framework for a multiproduct pipeline with inventory management.” Ind. Eng. Chem. Res. 46 (17): 5659–5672. https://doi.org/10.1021/ie070214q.
Zhang, H., Y. Liang, Q. Liao, Y. Shen, and X. Yan. 2018. “A self-learning approach for optimal detailed scheduling of multi-product pipeline.” J. Comput. Appl. Math. 327 (Jan): 41–63. https://doi.org/10.1016/j.cam.2017.05.040.
Zhang, H., Y. Liang, Q. Liao, M. Wu, and X. Yan. 2017. “A hybrid computational approach for detailed scheduling of products in a pipeline with multiple pump stations.” Energy 119 (Jan): 612–628. https://doi.org/10.1016/j.energy.2016.11.027.
Zheng, J., J. Du, Y. Liang, Q. Liao, Z. Li, H. Zhang, and Y. Wu. 2021. “Deeppipe: A semi-supervised learning for operating condition recognition of multi-product pipelines.” Process Saf. Environ. Prot. 150 (Jun): 510–521. https://doi.org/10.1016/j.psep.2021.04.031.
Zheng, J., C. Wang, Y. Liang, Q. Liao, Z. Li, and B. Wang. 2022. “Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines.” Energy 259 (Nov): 125025. https://doi.org/10.1016/j.energy.2022.125025.

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Go to Journal of Pipeline Systems Engineering and Practice
Journal of Pipeline Systems Engineering and Practice
Volume 14Issue 4November 2023

History

Received: Nov 7, 2022
Accepted: Apr 10, 2023
Published online: Jun 22, 2023
Published in print: Nov 1, 2023
Discussion open until: Nov 22, 2023

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Authors

Affiliations

Senior Engineer, Pipechina Oil and Gas Pipeline Control Center, Dongtucheng Rd. No. 5, Chaoyang District, Beijing 100007, PR China. Email: [email protected]
Master’s Student, National Engineering Laboratory for Pipeline Safety, Ministry of Education Key Laboratory of Petroleum Engineering, Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China Univ. of Petroleum (Beijing), Fuxue Rd. No. 18, Changping District, Beijing 102249, PR China. Email: [email protected]
Jinzhou Song [email protected]
Senior Engineer, Pipechina Oil and Gas Pipeline Control Center, Dongtucheng Rd. No. 5, Chaoyang District, Beijing 100007, PR China. Email: [email protected]
Zhengbing Li [email protected]
Ph.D. Student, National Engineering Laboratory for Pipeline Safety, Ministry of Education Key Laboratory of Petroleum Engineering, Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China Univ. of Petroleum (Beijing), Fuxue Rd. No. 18, Changping District, Beijing 102249, PR China. Email: [email protected]
Senior Engineer, Pipechina Oil and Gas Pipeline Control Center, Dongtucheng Rd. No. 5, Chaoyang District, Beijing 100007, PR China. Email: [email protected]
Ph.D. Student, National Engineering Laboratory for Pipeline Safety, Ministry of Education Key Laboratory of Petroleum Engineering, Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China Univ. of Petroleum (Beijing), Fuxue Rd. No. 18, Changping District, Beijing 102249, PR China. Email: [email protected]
Yongtu Liang [email protected]
Professor, National Engineering Laboratory for Pipeline Safety, Ministry of Education Key Laboratory of Petroleum Engineering, Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China Univ. of Petroleum (Beijing), Fuxue Rd. No. 18, Changping District, Beijing 102249, PR China (corresponding author). Email: [email protected]
Hongyang Gao [email protected]
Senior Engineer, Pipechina Oil and Gas Pipeline Control Center, Dongtucheng Rd. No. 5, Chaoyang District, Beijing 100007, PR China. Email: [email protected]
Hengyu Wang [email protected]
Engineer, Pipechina Oil and Gas Pipeline Control Center, Dongtucheng Rd. No. 5, Chaoyang District, Beijing 100007, PR China. Email: [email protected]

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  • Visualization and Analysis of Oil and Gas Pipeline Corrosion Research: A Bibliometric Data-Mining Approach, Journal of Pipeline Systems Engineering and Practice, 10.1061/JPSEA2.PSENG-1605, 15, 3, (2024).

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