The accurate online estimation of unsteady flow state provides important operation information for product pipelines real-time scheduling. In practice, affected by the parameter drift and observation noises, traditional estimation methods based on the first principle can hardly provide accurate results within acceptable time. The nonlinear and fast transient characteristics of pipeline flow make it difficult to realize on-line adaptive modification of model parameters. This paper proposes a methodology with multi-level adaptive adjustment to realize the unsteady flow state estimation of product pipelines. In order to meet the requirements of computational efficiency and accuracy simultaneously, we first introduce mode-free adaptive control method as linear compensation of the reduced order unsteady flow model which is obtained by frequency response and difference transforming method. The partial form dynamic linearization method has been adopted to design the adaptive control with minimum deviation between the measured result and the model output result as the objective. To further improve the adaptability of established model, the model parameters are online adjusted by using the recursive least squares with forgetting factor method. The relationship between the two adaptive adjustment methods of adaptive control and model parameter adjustment is achieved by using a two-layer adjustment framework, in which the deviation between the model output and the observed value is used as the threshold. The uncertainty of the model and the interference of observation noise can be eliminated by adopting Kalman filter to the modified state space model and the accuracy flow parameter values can be obtained as new observation of input parameters become available. The effectiveness of the proposed methodology is evaluated through a real pipeline and the robustness of the adaptive model is verified in some unfavorable conditions, including the imprecision of model initial parameters and measurement noise. The results show that the proposed method can obtain the accuracy flow state estimation of a product pipeline even under the interference of parameter drift and observation noise.


Kalman Filter and Model-Free Adaptive Control Theory Applied to the Unsteady Flow State Estimation of Product Pipelines

Division / Sponsors: 
Pipeline Systems Division

Paper Proceedings: https://asmedigitalcollection.asme.org/IPC/IPC2020/volume/84447

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