Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Mosul Reservoir

Simulation of Mosul Dam Reservoir Operation for Irrigation and Hydropower Generation

Baraa Jebbo; Taymoor A. Awchi

Engineering and Technology Journal, 2019, Volume 37, Issue 1C, Pages 64-69
DOI: 10.30684/etj.37.1C.10

The current research aims to build a simulation model to study the effect of Jazeera irrigation projects (North, East and South) water requirements on hydroelectric power generation from Mosul dam hydropower plant. The simulation was applied from January 1988 to December 2006 on a monthly basis. A simulation model was built utilizing (HEC-ResSim 3.0) Package, which showed high efficiency in representing and simulating the actual operation of the reservoir. Simulation model operation has been carried out through five scenarios with two cases of priority; the first was to maximize the hydroelectric power generation, and the second was to minimize the shortage in fulfilling the water requirements of Jazeera irrigation projects. The results showed that when the priority is given to meet the irrigation water requirements, the water deficit is decreased but the hydropower generation deficit is increased, and when the priority is to maximize the hydroelectric power generation, the hydropower generation increases, but the irrigation water is decreased. The study concluded that when the East and South Jazeera irrigation projects are completed and operated along with North Jazeera project, the reservoir would not be able to meet their requirements, in addition to an obvious shortage in hydroelectric power generation.

Reservoir Operation by Artificial Neural Network Model ( Mosul Dam –Iraq, as a Case Study)

Thair S.K; Ayad S. M; Hasan H.M

Engineering and Technology Journal, 2015, Volume 33, Issue 7, Pages 1697-1714

Reservoir operation forecasting plays an important role in managing water resources systems. Artificial Neural Network (ANN) model was applied for Mosul-Dam reservoir which is located on Tigris River, which the objectives of water resources development and flood control. Feed-forward multi-layer perceptions (MLPs) are used and trained with the back-propagation algorithm, as they have a high capability of data mapping. The data set has a period of 23 years from 1990 to 2012..The Input data were inflow (It), evaporation (Et), rainfall (Rt), reservoir storage (St) and outflow (Ot). The best convergence after more than 1000 trials was achieved for the combination of inflow (It), inflow (It-1), inflow (It-2), evaporation (Et), reservoir storage (St), rainfall (Rt), outflow (Ot-1) and outflow (Ot-2) with error tolerance, learning rate, momentum rate, number of cycles and number of hidden layers as 0.001, 1, 0.9,50000 and 9 respectively. The coefficient of determination (R2) and MAPE were (0.972) and (17.15) respectively. The results of ANN models for the training, testing and validation were compared with the observed data. The predicted values from the neural networks matched the measured values very well. The application of ANN technique and the predicted equation by using the connection weights and the threshold levels, assist the reservoir operation decision and future updating, also it is an important Model for finding the missing data. The ANN technique can accurately predict the monthly Outflow.