Ahmmed S. Ibrehem

Mathematical Model and Advanced Control for Gas-phase Olefin Polymerization in Fluidized-bed Catalytic Reactors

In this study, the developments in modeling gas-phase catalyzed olefin polymerization fluidized-bed reactors (FBR) using Ziegler-Natta catalyst is presented. The modified mathematical model to account for mass and heat transfer between the solid particles and the surrounding gas in the emulsion phase is developed in this work to include site activation reaction. This model developed in the present study is subsequently compared with well-known models, namely, the bubble-growth, well-mixed and die constant bubble size models for porous and non porous catalyst. The results we obtained from the model was very close to the constant bubble size model, well-mixed model and bubble growth model at the beginning of the reaction but its overall behavior changed and is closer to the well-mixed model compared with the bubble growth model and constant bubble size model after half an hour of operation. Neural-network based predictive controller are implemented to control the system and compared with the conventional PID controller, giving acceptable results.

Advance mathematical model to study and analyse the effects of total quality management (TQM) and operational flexibility on hospital performance

Total quality management (TQM) and operational flexibility are powerful and evolving management tools that are being implemented in many organisations worldwide, particularly in healthcare services. However, such implementation is yet to be seen in developing nations, particularly in health care. In an attempted response to such a gap, this paper provides a literature review on the principles and scope of TQM and operational flexibility in the healthcare industry and proposes a mathematical model, employing artificial neural networks, to study and analyse the implementation of TQM and operational flexibility dimensions towards improving hospital performance and reducing costs and medical errors. This study is the first of its kind in the field. The results yielded a very high degree of accuracy (more than 99.2%) in relating TQM variables and operational flexibility dimensions (inputs) to hospital performance (output), implying a highly accurate and strong model that can exactly determine the weaknesses in a hospitals performance and can define and pinpoint poor application of TQM and operations flexibility, particularly upon the development of system identification for this model.

Parameter addition in interaction of glucose and insulin for type 1 diabetes

Recently, in silico testing which is managed via computer simulation, is preferably performed on Diabetes patients rather than to be evaluated directly on their body due to the benefit of patient safety and accelerated progress obtained from the convenient testing. However, it is still difficult to have an accurate and efficient Diabetes therapy through this simulation method. Therefore, continuous investigation and research are necessary so as to enhance a reliable Diabetes treatment in future. In this work, we propose an improved Diabetic model that emphasizes on detailed inter-relation of glucose and insulin reaction referring to the Hovorka model. This study was undertaken to improve Diabetes treatment in particular for Type 1 patient by ensuring all parameters involved in glucose-insulin interaction do include in the Diabetic model equations. The equations in Hovorka model have been modified in glucose subsystem, plasma insulin concentration and insulin subsystem while the other equations remain unchanged. As a result, we managed to have a good correlation on interactions between the parameters in glucose-insulin intervention. We hope this correlation can contribute to the field of Diabetes therapy enhancement so as to help the patients to achieve a near normal blood glucose level and manage to control their healthy life style.

System Identification In Modified Diabetic Model For Nanochip Controller

Keeping pace with emerging technologies, artificial pancreas is highly recommended to be used as an alternate way to solve blood glucose level problem for Type 1 diabetes patients. It is aimed to develop an embedded nanochip controller in order to regulate the blood glucose level within the safety range. However, due to the lack of effectiveness in algorithm, the blood glucose level in patients body is still not achieving the optimum level. The function of the algorithm, which is the heart of the device, needs to be analyzed in order to ensure the device can be fully utilized. Therefore, system identification technique is applied with objective to study the interrelation among all parameters and variables in the modified diabetic model. As a consequence, the results derived from the method, give us better comprehension in determining which parameters give higher effects on the glucose and insulin system. Thereupon, the main factors in the system are able to be recognized through system identification technique. In this study, parameter tmax_I gave highest effect percentage with 66.89% at interaction with insulin,I. On the whole, system identification is very useful to see clear picture of interrelation and correlation in glucose and insulin system.