Farouq S. Mjalli

Deep oxidative desulfurization of liquid fuels

Abstract Increase in energy demand and consumption has been accompanied by a corresponding increase in sulfur emissions. These pollutants have both health and economic consequences. Furthermore, it significantly reduces the efficiency of advanced emission control systems of diesel engines, thereby indirectly causing more harm to the environment. This resulted in stringent sulfur emission limit down to about 15 ppm or less and in turn served as an incentive for research into alternative sulfur reduction technologies. Although feasible improvements to hydrodesulfurization are currently under investigation, adsorptive, extractive, oxidative and biodesulfurization have also been studied in recent years. Oxidative desulfurization appears to be one of the most promising desulfurization technologies due to its broadness and compatibility with other technologies such as extractive, adsorptive and biodesulfurization. The advent of ionic liquids as extraction solvents has made this even more so. This work, therefore, reviews the different approaches and investigations carried out on oxidative desulfurization while identifying research gaps and giving important recommendations.

Modeling and Sensitivity Analysis of Acoustic Release of Doxorubicin from Unstabilized Pluronic P105 Using an Artificial Neural Network Model

This paper models steady state acoustic release of Doxorubicin (Dox) from Pluronic P105 micelles using Artificial Neural Networks (ANN). Previously collected release data were compiled and used to train, validate, and test an ANN model. Sensitivity analysis was then performed on the following operating conditions: ultrasonic frequency, power density, Pluronic P105 concentration, and temperature. The model showed that drug release was most efficient at lower frequencies. The analysis also demonstrated that release increases as the power density increases. Sensitivity plots of ultrasound intensity revealed a drug release threshold of 0.015 W/cm2 and 0.38 W/cm2 at 20 and 70 kHz, respectively. The presence of a power density threshold provides strong evidence that cavitation plays an important role in acoustically activated drug release from polymeric micelles. Based on the developed model, Dox release is not a strong function of temperature, suggesting that thermal effects do not play a major role in the physical mechanism involved. Finally, sensitivity plots of P105 concentration indicated that higher release was observed at lower copolymer concentrations.

Optimizing the use of ultrasound to deliver chemotherapeutic agents to cancer cells from polymeric micelles

Abstract In this study, we present an artificial neural network (ANN) model that attempts to predict the dynamic release of doxorubicin (Dox) from P105 micelles under different ultrasonic power densities at 20xa0kHz. The goal is to utilize the developed ANN model in optimizing the ultrasound application to achieve a target drug release at the tumor site by controlling power density and ultrasound duration via an ANN-based model predictive control. The parameters of the controller are then tuned to achieve good reference signal tracking.

Forecasting of ozone pollution using artificial neural networks

Purpose – The objective of this study is to develop and validate a neural‐based modelling methodology applicable to site‐specific short‐ and medium‐term ozone concentration forecasting. A novel modelling technique utilizing two feed forward artificial neural networks (FFNN) is developed to improve the performance of time series predictions.Design/methodology/approach – Air pollution and meteorological data were collected for one year in two locations in Kuwait. The hourly averages of the data were processed to generate a covariance matrix and analyzed to generate the principal component method. A two‐FFNN model is then used to predict the actual data.Findings – The newly developed model improves the prediction accuracy over the conventional method. Owing to the presence of noise and other minor disturbances in the data, shorter‐range modelling gives better modelling results.Originality/value – A novel modelling technique is developed to predict the time series of zone concentration.

Using artificial neural networks and model predictive control to optimize acoustically assisted Doxorubicin release from polymeric micelles.

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.

Two Phase Dynamic Model for Gas Phase Propylene Copolymerization in Fluidized Bed Reactor

A two-phase model is proposed for describing the dynamics of a fluidized bed reactor used for polypropylene production. In the proposed model, the fluidized bed is divided into an emulsion phase and bubble phase where the bubble phase flow pattern is assumed to be plug flow and the emulsion phase is considered to be perfectly mixed. Similar previous models consider the reaction in the emulsion phase only. In this work the contribution of reaction in the bubble phase is considered and its effect on the overall polypropylene production is investigated. The kinetic model combined with hydrodynamic model in order to develop a comprehensive model for gas-phase propylene copolymerization reactor. Simulation profiles of the proposed model were compared with those of well mixed model for the emulsion phase temperature. The simulated temperature profile showed a lower rate of change compared to the previously reported models due to lower polymerization rate. Model simulation showed that about 13% of the produced polymer comes from the bubble phase and this considerable amount of polymerization in the bubbles should not be neglected in any modeling attempt.

Adsorptive removal of residual catalyst from palm biodiesel: application of response surface methodology

In this work, the residual potassium hydroxide catalyst was removed from palm n oil-based methyl esters using an adsorption technique. The produced biodiesel n was initially purified through a water washing process. To produce a n biodiesel with a better quality and also to meet standard specifications (EN n 14214 and ASTM D6751), batch adsorption on palm shell activated carbon was n used for further catalyst removal. The Central Composite Design (CCD) of the n Response Surface Methodology (RSM) was used to study the influence of n adsorbent amount, time and temperature on the adsorption of potassium n species. The maximum catalyst removal was achieved at 40°C using 0.9 g n activated carbon for 20 h adsorption time. The results from the Response n Surface Methodology are in a good agreement with the measured values. The n absolute error in prediction at the optimum condition was 3.7%, which is n reasonably accurate. This study proves that adsorption post-treatment n techniques can be successfully employed to improve the quality of biodiesel n fuel for its effective use on diesel engines and to minimize the usage of n water.

NEURAL NETWORK–BASED HEAT AND MASS TRANSFER COEFFICIENTS FOR THE HYBRID MODELING OF FLUIDIZED REACTORS

The complex flow patterns induced in fluidized bed catalytic reactors and the competing parameters affecting the mass and heat transfer characteristics make the design of such reactors a challenging task to accomplish. The models of such processes rely heavily on predictive empirical correlations for the mass and heat transfer coefficients. Unfortunately, published empirical-based correlations have the common shortcoming of low prediction efficiency compared with experimental data. In this work, an artificial neural network approach is used to capture the reactor characteristics in terms of heat and mass transfer based on published experimental data. The developed ANN-based heat and mass transfer coefficients relations were used in a conventional FCR model and simulated under industrial operating conditions. The hybrid model predictions of the melt-flow index and the emulsion temperature were compared to industrial measurements as well as published models. The predictive quality of the hybrid model was superior to other models. This modeling approach can be used as an alternative to conventional modeling methods.

Forecasting Influent-Effluent Wastewater Treatment Plant Using Time Series Analysis and Artificial Neural Network Techniques

We consider the problem of predicting the future behavior of wastewater treatment plant quality indicators by creating prediction models using historical plant data. One of the main aims of this work is to be able to predict plant operational situations in advance so that corrective actions can be taken in time. Sets of historical plant data, such as BOD, COD and TSS were collected for a local wastewater treatment plant in Doha, the capital of the State of Qatar. These variables characterize the performance of any wastewater treatment plant and can be considered as quality indicators of the plant performance. Data were collected over a period of 4 years for the influent and effluent streams of the station. The plant influent and effluent predictions were performed using different techniques. These include time-series analysis, where the ARIMA (Autoregressive Integrated Moving Average) model was implemented in this case, and two Artificial Neural Networks (ANN) algorithms, namely Adaptive Linear Neuron networks (ADALINE) and Multi-layer Feedforward (ML-FF) neural networks. The predictions from the three techniques were presented and compared. The ML-FF model predictions proved to be more reliable than that of the equivalent ARIMA predictions followed by the ADALINE predictions, particularly for the finial effluent stream variables.

Electrochemical Generation of Superoxide Ion in Ionic Liquid 1-(3-Methoxypropyl)-1-Methylpiperidinium Bis (Trifluoromethylsulfonyl) Imide

In this work, the superoxide ion was generated and analysed electrochemically using cyclic voltammetry (CV) techniques from oxygen dissolved in a room-temperature ionic liquid, 1-(3-methoxypropyl)-1-methylpiperidinium bis (trifluoromethylsulfonyl) imide, at atmospheric pressure. It was found that the generated superoxide ion was stable which indicates its possible use for further useful applications. To the best of our knowledge, this is the first time a piperidinium based IL has been used for the electrochemical generation of O2.