Abstract: This paper describes an automated event detection and location system for water distribution pipelines which is based upon low-cost sensor technology and signature analysis by an Artificial
Neural Network (ANN). The development of a low cost failure sensor which measures the opacity or cloudiness of the local water
flow has been designed, developed and validated, and an ANN based system is then described which uses time series data produced by
sensors to construct an empirical model for time series prediction and
classification of events. These two components have been installed,
tested and verified in an experimental site in a UK water distribution
system. Verification of the system has been achieved from a series of
simulated burst trials which have provided real data sets. It is concluded that the system has potential in water distribution network
management.
Abstract: Methane is the second most important greenhouse gas
(GHG) after carbon dioxide. Amount of methane emission from
energy sector is increasing day by day with various activities. In
present work, various sources of methane emission from upstream,
middle stream and downstream of oil & gas sectors are identified and
categorised as per IPCC-2006 guidelines. Data were collected from
various oil & gas sector like (i) exploration & production of oil & gas
(ii) supply through pipelines (iii) refinery throughput & production
(iv) storage & transportation (v) usage. Methane emission factors for
various categories were determined applying Tier-II and Tier-I
approach using the collected data. Total methane emission from
Indian Oil & Gas sectors was thus estimated for the year 1990 to
2007.