N Abdel-Jabbar

Publications (3)5.14 Total impact

• Article: Neural network modeling and optimization of scheduling backwash for membrane bioreactor

  A. Aidan, N. Abdel-Jabbar, T. H. Ibrahim, V. Nenov, F. Mjalli
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  ABSTRACT: Backwash scheduling for a membrane bioreactor was experimentally examined and theoretically modeled via neural networks. Flux was determined for different backwash and service time runs. Vacuum and backwashing streams pressure for different timing regimes were used to observe and monitor the fouling and a cake layer accumulated on the membrane surface causing a decline in the flux for the submerged membrane bioreactor. Experimental results were employed to develop an artificial neural network model (ANN) to predict the membrane flux as a function of the backwash and service times. Such modeling entails using a fairly large number of experimental data to reconcile model predictions with actual flux measurements in order to validate the ANN model. The ANN model was shown to be accurate in predicting the flux of the membrane and can be utilized to find optimum backwash scheduling strategy for such reactors. KeywordsMembrane biological reactor-Backwash time-Service time-Neural network modeling
  Clean Technologies and Environmental Policy 04/2012; 10(4):389-395. · 1.75 Impact Factor
• Article: Using artificial neural networks and model predictive control to optimize acoustically assisted Doxorubicin release from polymeric micelles.

  Ghaleb A Husseini, Farouq S Mjalli, William G Pitt, Nabil M Abdel-Jabbar
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  ABSTRACT: We have been developing a drug delivery system that uses Pluronic P105 micelles to sequester a chemotherapeutic drug--namely, Doxorubicin (Dox)--until it reaches the cancer site. Ultrasound is then applied to release the drug directly to the tumor and in the process minimize the adverse side effects of chemotherapy on non-tumor tissues. Here, we present an artificial neural network (ANN) model that attempts to model the dynamic release of Dox from P105 micelles under different ultrasonic power intensities at two frequencies. The developed ANN model is then utilized to optimize the ultrasound application to achieve a target drug release at the tumor site via an ANN-based model predictive control. The parameters of the controller are then tuned to achieve good reference signal tracking. We were successful in designing and testing a controller capable of adjusting the ultrasound frequency, intensity, and pulse length to sustain constant Dox release.
  Technology in cancer research & treatment 12/2009; 8(6):479-88. · 2.02 Impact Factor
• Article: A novel approach to increase oral drug absorption.

  N M Idkaidek, N Abdel-Jabbar
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  ABSTRACT: A comprehensive analytical solution that accounts for many factors and assumptions affecting drug concentration profile in the gastrointestinal tract was presented. A sensitivity analysis approach was utilized in order to investigate the importance of different parameters in the model. The partition coefficient is found to be the key parameter. Hence, for drugs stable in the intestinal wall, increasing partition coefficient only can lead to higher drug absorption. However, for drugs unstable in the intestinal wall, increasing partition coefficient and diffusivity is needed to counteract drug instability. On the other hand, the model is essentially insensitive to degradation in the intestinal lumen for degradation half lives greater than 0.7 min at a given intestinal length. However, a high model sensitivity to the rate of luminal degradation is obtained at higher intestinal length values. The proposed model can be used as a guide for oral drug delivery.
  Pharmaceutical Development and Technology 02/2001; 6(2):167-71. · 1.36 Impact Factor