(109a) Intelligent Monitoring of Product Interfaces in Pipeline Transport Systems

Pipelines transporting multiple petroleum products remain essential infrastructure for distributing refined fuels from production sites to end-use terminals. These systems operate on a sequential batch delivery strategy, where distinct fuel types are injected in succession. Accurate identification of batch interfaces is critical to ensuring product integrity, minimizing cross-contamination, and optimizing delivery operations. Traditional methods estimate interface positions based on cumulative inlet and outlet volumes [1-3]; however, these volume-based approaches are prone to errors arising from thermally induced expansion, elevation-induced pressure changes, and dynamic operational variability, which alter the fluid’s transport behavior and compromise estimation accuracy [4].

To overcome these limitations, this study presents an advanced framework for high-precision, real-time batch interface tracking in multi-product pipeline systems. The proposed methodology is grounded in an infinite-dimensional transient hydraulic model, formulated from first principles to capture the complex spatiotemporal evolution of pipeline flow [5]. This model incorporates key physical effects—including elevation-induced pressure differentials, unsteady friction, and time-varying viscosity—to represent the underlying transport dynamics with high fidelity. To further enhance estimation robustness, a data-driven correction scheme is integrated to systematically reduce discrepancies between model-based predictions and actual interface locations. The effectiveness of the proposed approach is demonstrated through rigorous case studies and comparative analyses against industrial benchmark data, highlighting its superior accuracy and adaptability under realistic operating conditions.

References:

[1] Blažič, S., Matko, D. and Geiger, G., 2004. Simple model of a multi-batch driven pipeline. Mathematics and computers in simulation, 64(6), pp.617-630.

[2] Li, Z., Liang, Y., Liao, Q., Zhang, B. and Zhang, H., 2021. A review of multiproduct pipeline scheduling: from bibliometric analysis to research framework and future research directions. Journal of Pipeline Science and Engineering, 1(4), pp.395-406.

[3] Korlapati, N.V.S., Khan, F., Noor, Q., Mirza, S. and Vaddiraju, S., 2022. Review and analysis of pipeline leak detection methods. Journal of pipeline science and engineering, 2(4), p.100074.

[4] Zheng, J., Du, J., Liang, Y., Wang, B., Li, M., Liao, Q. and Xu, N., 2023. Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines. Chemical Engineering Research and Design, 191, pp.236-248.

[5] Xie, J., Huang, B. and Dubljevic, S., 2024. Moving horizon estimation for pipeline leak detection, localization, and constrained size estimation. Computers & Chemical Engineering, 188, p.108777.