This work presents the fundamentals and application of transient model-based leak detection and localization technique for crude oil pipelines. The dynamic parameters involved in this model such as pressure, flow and temperature were acquired by SCADA (Supervisory Control and Data Acquisition) system. The characteristic changes in the flowmechanics and thermodynamics along a given length of pipeline were used in detecting, localizing and determining the flow rate of the leak. Measurement of pressure, temperature and flow data at both the inlet and outlet of the pipeline were used in formulating the equations obtained from the inconsistency in the continuity and law of conservation of momentum equations. This model located a leak incident in a horizontal pipeline of length 2000m and diameter 0.3556m carrying Nigeria bonny light crude oil from the Nigeria Petroleum Development Company Limited, Olomoro flowstation into UPS (Ughelli Pump Station) truck line. But the leak located by the model at 1088.12m from the inlet is 11.88m behind the actual leak position of 1100m as discovered during the pipeline leak remedial works.
This research work studies several forecasting techniques to predict future income generation and its implications on the Nissan Urvan income generation in Anambra State Transport sector .This paper presents three forecasting models (time series decomposition method, winter's method and Arima method) to analyze data income generation of Anambra State Transport Sector over the period of 2005-2019, it is important to know the trend in Anambra Transport Sector to elicit patterns of incomes generated. However, the models used were based on applicable methodology to facilitate accurate and faster analysis of data. The results reveal that the future income generated for the products will continuously decrease with time. Having observed the values obtained from the models used, it is recommended that the company should employ time series decomposition forecasting model because it gives more accurate result with less error.
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