Project Coordinator
AARC Ltd
Total years of experience :9 years, 2 Months
project to assist the Ministry of Electricity on a World Bank - financed project for the Corporatization Roadmap for Ministry of Electricity (MoE) Directorates (CRMED).
project to assist the Ministry of Finance on a World Bank-financed project for the Design, Supply, Installation and Implementation of an Integrated Financial Management Information System (IFMIS).
a. Generating leads.
b. Meeting or exceeding sales goals.
c. Negotiating all contracts with prospective clients.
d. Helping determine pricing schedules for quotes, promotions, and negotiations.
e. Preparing weekly and monthly reports.
f. Giving sales presentations to a range of prospective clients.
g. Coordinating sales efforts with marketing programs.
h. Understanding and promoting company programs.
i. Obtaining deposits and balance of payment from clients.
j. Preparing and submitting sales contracts for orders.
k. Visiting clients and potential clients to evaluate needs or promote products and services.
l. Maintaining client records.
m. Answering client questions about credit terms, products, prices and availability.
In presentation was doing to it sector and also in administration and translate to direct manger
In prepared table of equipment for the company projects after obtain information from civil, electrical and mechanical engineers. Also, help both the account of company in entering information of the projects and admin in his work too.
I was working in search about tender in website and newspaper of (The Ministry of Industry and Minerals, Ministry of Oil, Ministry of Commerce, Ministry of Environment, the Ministry of Health ... etc.) and find the tenders that related to my field sector after that prepared all needed document of tender (Commercial and technical offer) addition all that Writing, printing and answer the official book of the ministries and companies.
Hardness – Strength Correlation Properties of Carbon Steel and Aluminium Alloys Using Feed Forward Artifical Neural Network In this thesis, that aims to build two mathematical model to predict mechanical properties of low/medium carbon steel and AA 2219 Al alloy. The first model that have different heat treated low and medium carbon steels with as received state and second model have different aging time of AA 2219 Al alloy. The samples were tested using hardness and tensile testing, two models were predicted based on feed forward neural networks were developed using back propagation based on gradient descent learning. For first model, the influence of (0.1, 0.25, and 0.5) carbon percentages and heat treatment on the mechanical properties of low and medium carbon steel was considered. The heat treatments were quenching for one hour at different temperatures (850, 910 and 950 ᵒC) then cooling in water and tempering for one hour also at (300, 500, and 750 ᵒ C) then cooling in air. The behavior of mechanical properties of the specimen was estimated utilizing Vickers hardness testing and tensile test. The values of both tests for the quenched specimens were relatively higher than those of the as received samples. The data obtained then were feed to neural network for predict the mechanical properties of low/medium carbon steel. In neural networks were used three layers, first layers take the carbon percentage, quenching temperature and tempering temperature as inputs, and second layers take yield strength, tensile strength and Vickers hardness as outputs, and last one hidden layers. The correlation coefficients for the three responses versus the experimental data were 0.9815, 0.9602 and 0.9764 for yield strength, tensile strength and Vickers hardness, respectively. The second model is proposed to predict the ageing time from the required mechanical properties (yield strength, tensile strength, ductility, and average hardness) of AA2219 Al alloy. The network takes the mentioned mechanical properties as inputs and return the ageing time as output. The results showed that the proposed model can be used to find the proper ageing time for the required mechanical properties. The correlation coefficient r for this model response against the experimental data was 0.9905. The achievement of two artificial neural networks models were very good and showed that models could be utilized for predicting the mechanical properties of low/medium carbon steel at various temperatures as functions of carbon percentage and heat treatment, also for aging time for AA2219 Al alloy.