Shankar Narasimhan S

Dynamic simulation models can be used along with flow and pressure measurements, for on-line leak detection and identification in gas pipeline networks. In this two part paper, a methodology is proposed for detecting and localizing leaks occurring in gas pipelines. The main features of the proposed methodology are: (i) it is applicable to both single pipelines and pipeline networks and (ii) it considers non-ideal gas mixtures. In order to achieve the desired computational efficiency for on-line deployment, an efficient state estimation technique based on a transfer function model, previously developed by the authors, is embedded in a hypothesis testing framework. In Part-I of this paper, a detailed description of the methodology is presented, and its performance is evaluated using simulations on two illustrative pipeline systems. The proposed method is shown to perform satisfactorily even with noisy measurements and during transient conditions, provided there is sufficient redundancy in the measurements. © 2010 Elsevier Ltd.

In Part-I of this two part paper, a method is proposed for on-line leak detection and identification in gas pipeline networks using flow and pressure measurements. Simulations on two illustrative networks were used to demonstrate the applicability of the proposed method. In this paper, the performance of the proposed leak detection and identification methodology was evaluated using experiments with compressed air on a laboratory scale network. The on-line applicability of the proposed methodology was demonstrated through field level leak detection tests carried out on a 204.7. km long pipeline in India, supplying natural gas to a power plant. The laboratory and field tests demonstrated that the proposed methodology can be used for quick on-line detection of leaks, and locating the leaks reasonably accurately. © 2010 Elsevier Ltd.

Estimation of pipe roughness coefficients is an important task to be carried out before any water distribution network model is used for online applications such as monitoring and control. In this study, a combined state and parameter estimation model for water distribution networks is presented. Typically, estimation of roughness coefficient for each individual pipe is not possible due to non-availability of sufficient number of measurements. In order to address this problem, a formal procedure based on K-means clustering algorithm is proposed for grouping the pipes which are likely to have the same roughness characteristics. Also, graph-theoretic concepts are used to reduce the dimensionality of the problem and thereby achieve significant computational efficiency. The performance of the proposed model is demonstrated on a realistic urban water distribution network. © 2009 Springer Science+Business Media B.V.