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Simulation of SESAME’s Synchrotron Storage Ring for the Pressure Predictions in Vacuum System

Makahleh Firas, Attar Hani, Manasrah Ahmad, Nassar Anas, Amer Ayman, Alsaqoor Sameh, Borowski Gabriel, Alahmer Ali

Advances in Science and Technology. Research Journal

2023

Vol. 17, no 6

164--170

Many particle accelerators rely on maintaining low pressures to ensure efficient operation, minimize beam losses, and reduce radiation background. To ensure a beam lifetime of 1–20 hours for the Synchrotron-light for Experimental Science and Applications in the Middle East (SESAME) vacuum system, an ideal average dynamic pressure of 1×10-9 mbar was targeted. This pressure was intended to be maintained while running the accelerator at a current of 400 mA after a cumulative dose of 100 Ah. In this study, a MATLAB code was employed to develop a series of one-dimensional equations that simulate the behavior of the vacuum system within the SESAME storage ring. The proposed model was then compared with the results generated by the VACCALC software and the Particle Monte Carlo (TPMC) MOLFLOW code, establishing a comprehensive assessment framework. The collected data from the model was subsequently compared with the recorded static and dynamic pressure measurements obtained during more than 1000 Ah of beam conditioning at 2.5 GeV. In results, the projected and actual values of dynamic pressures exhibited a satisfactory degree of agreement across the investigated range of beam conditioning doses, with a consistency factor exceeding 2 after a 100 Ah dose.

Estimation of Water Disinfection by Using Data Mining

Bashayreh Esra'a, Manasrah Ahmad, Alkhalil Shahnaz, Abdelhafez Eman

Ecological Engineering & Environmental Technology

2021

Vol. 22, iss. 1

109--116

In this study, the Artificial Neural Network (ANN) models and multiple linear regression techniques were used to estimate the relation between the concentration of total coliform, E. coli and Pseudomonas in the wastewater and the input variables. Two techniques were used to achieve this objective. The first is a classical technique with multiple linear regression models, while the second one is data mining with two types of ANN (Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The work was conducted using (SPSS) software. The obtained estimated results were verified against the measured data and it was found that data mining by using the RBF model has good ability to recognize the relation between the input and output variables, while the statistical error analysis showed the accuracy of data mining by using the RBF model is acceptable. On the other hand, the obtained results indicate that MLP and multiple linear regression have the least ability for estimating the concentration of total coliform, E. coli and pseudomonas in wastewater.