The aim of this work is to develop an axi-symmetric two dimensional model based on a coupled simplified computational fluid dynamics (CFD) and Lagrangian method to predict the air flow patterns and drying of particles. Then using this predictive tool to design more efficient spray dryers. The approach to this is to model what particles experience in the drying chamber with respect to air temperature and humidity. These histories can be obtained by combining the particles trajectories with the air temperature/humidity pattern in the spray dryer. Results are presented and discussed in terms of the air velocity, temperature, and humidity profiles within the chambers and compared for drying of a 42.5% solids solution in a spray chamber 2.22m in diameter with a cylindrical top section 2.00m high and a bottom cone section 1.725m high.
A simple, rapid and sensitive spectrophotometric method has been developed for the determination of captopril in aqueous solution. The method is based on reaction of captopril with 2,3-dichloro 1,4- naphthoquinon(Dichlone) in neutral medium to form a stable yellow colored product which shows maximum absorption at 347 nm with molar absorptivity of 5.6 ×103 L.mole-1. cm-1. The proposed method is applied successfully for determination of captopril in commercial pharmaceutical tablets.
Crop coefficient for cherries was evaluated by measure the water consumption in Michigan State to find its variation with time as the plant growth. Crop coefficients value (Kc) for cherries were predicated by Michigan State University (MSU) and also by Food and Agriculture Organization (FAO) according to consume of water through the season. In this paper crop coefficients for cherries are modified accordingly to the actual measurements of soil moisture content. Actual evapotranspiration (consumptive use) were measured by the soil moisture readings using Time Domain Reflectometers (TDR), and compared with the actual potential evapotranspiration that calculated by using modified Penman-Monteith equation which depends on metrological statio
... Show MoreLong memory analysis is one of the most active areas in econometrics and time series where various methods have been introduced to identify and estimate the long memory parameter in partially integrated time series. One of the most common models used to represent time series that have a long memory is the ARFIMA (Auto Regressive Fractional Integration Moving Average Model) which diffs are a fractional number called the fractional parameter. To analyze and determine the ARFIMA model, the fractal parameter must be estimated. There are many methods for fractional parameter estimation. In this research, the estimation methods were divided into indirect methods, where the Hurst parameter is estimated fir
... Show MorePiracy on phonograms is now, rightly, the crime of the electronic age. Despite the protection sought by States to provide for such registrations, whether at the level of national legislation or international agreements and conventions, but piracy has been and continues to pose a significant threat to the rights of the producers of those recordings, especially as it is a profitable way for hackers to get a lot of money in a way Illegal, which is contrary to the rules of legitimate competition. Hence, this research highlights the legal protection of producers of phonograms in light of the Iraqi Copyright Protection Act No. (3) of 1971, as amended.
Use of computer simulation to quantify the effectiveness of blowing agents can be an effective tool for optimizing formulations and for the adopting of new blowing agents. This paper focuses on a mass balance on blowing agent during foaming including the quantification of the amount that stays in the resin, the amount that ends up in the foam cells, and the pressure of the blowing agent in the foam cells. Experimental data is presented both in the sense of developing the simulation capabilities and the validating of simulation results.
One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p
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