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Advanced pipeline leak detection and localization techniques are needed to reduce greenhouse gas emissions from hydrocarbon transportation pipelines. Developing effective leak detection and localization methods is challenging due to the spatiotemporal dynamics of process variables, the presence of process/measurement disturbances and constraints, and the limited measurement data. To address this issue, this manuscript proposes a novel moving horizon estimation design for pipeline leak detection, constrained estimation of leak size and location by using an infinite-dimensional pipeline hydraulic model. Based on the mass and momentum balance laws and the Cayley–Tustin time-discretization method, an infinite-dimensional discrete-time pipeline hydraulic model is proposed considering (unknown but bounded) disturbance and leak. By introducing a coordinate transformation, we decouple the leak size and location estimation problems. The implementable discrete-time moving horizon estimator and observer are designed for constrained leak size and location estimation. The effectiveness of the proposed designs is validated via simulation examples. • An infinite-dimensional discrete-time pipe leak model with disturbance is proposed. • A moving horizon estimator (MHE) is designed for constrained leak size estimation. • Discrete-time observer and MHE are designed for leak localization. • Numerical simulation studies are performed to verify the proposed methods. [ABSTRACT FROM AUTHOR]

This article addresses the plant-model mismatch detection problem for linear multiple-input and multiple-output systems operating under the constrained dynamic matrix control (DMC) with the assumption of unknown noise models. An autocovariance-based mismatch detection method that uses sum-of-norms regularization is proposed, aiming to detect parameter jumps and estimate the noise model separately. The intention of introducing regularization is not only to be able to segment the mismatch so that the mismatch is piece-wise constant in time, but also to make the method robust to colored noise. Moreover, a method to alleviate mis-detection caused by unknown operating conditions is proposed. We show that the method can detect significant jumps in parameters and thus provide a priori knowledge for system re-identification and timing of updating the model. Finally, the feasibility of the proposed method under closed-loop conditions is analyzed from a stochastic perspective and demonstrated with illustrative examples. • Mismatch detection via autocovariance and sum-of-norms regularization. • The noise model is individually estimated using the regularization term. • To cope with "special" cases, the output prediction error method is introduced. [ABSTRACT FROM AUTHOR]