Kuwait City, Kuwait

Australian College of Kuwait

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Kuwait City, Kuwait

The Australian College of Kuwait , which was established as Kuwait’s first private technical college, is proud to offer one of the finest vocational education programs in the region. With a vision of “Enabling Human Potential within a Culture of Care”, the College has established partnerships with some of the top international vocational education institutions to offer a unique learning product to our students. Our students are advantaged by studying the best vocational courses from experienced instructors, with the aim of enabling our students to graduate with the required skill sets to thrive in their careers. Wikipedia.


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Yap W.K.,Charles Darwin University | Karri V.,Australian College of Kuwait
Applied Energy | Year: 2011

This paper demonstrates the use of artificial neural networks virtual sensors in emissions prediction and control for a gasoline engine. Tailpipe emissions and engine parameters were first measured experimentally to form a comprehensive database for network training and testing. Individual predictive models were constructed using the optimization layer-by-layer neural network. Simulation results demonstrated that the networks, as virtual sensors, can accurately predict the engine parameters and emissions quantitatively and qualitatively with RMS errors below 9%. The second part of this paper then presents a virtual sensor control model which is the combination of the two individual emissions and engine predictive models developed previously. The main objective of this part is to control the exhaust emissions within the desired limits by predicting optimum engine parameters with the use of artificial neural network virtual sensors. Results showed that the emissions levels were successfully controlled within the defined limits, with maximum tolerance of 6%. This first part of this paper demonstrated that with the use of artificial neural network virtual sensors, emissions and engine parameters can be accurately predicted. Hence with accurate virtual sensors, emissions were then controlled within the desired limits by optimizing the engine parameters. This proposed work demonstrated a viable and accurate methodology in emissions predictive and control. By applying virtual sensor models, the need additional, cumbersome and costly measuring and monitoring devices can be eliminated. © 2011 Elsevier Ltd.


Yap W.K.,Charles Darwin University | Karri V.,Australian College of Kuwait
Applied Soft Computing Journal | Year: 2013

This paper presents a comparison of predictive models for the estimation of engine power and tailpipe emissions for a 4 kW gasoline scooter. This study forms a benchmark toward establishing an online emissions control and monitoring system to bring the emissions to within specific limits. Three emissions predictive models were investigated in this study; direct and series artificial neural network (ANN) models and a MATLAB dynamic model. The direct models takes variables lambda, throttle position, engine and vehicle speed to predict the engine power and the emissions CO, CO2 and HC. The series model first takes the mentioned input to predict the engine power and consequently using the engine power as the fifth input to predict the emissions. For the ANN models, two multilayered networks were compared and analyzed; the backpropagation (BP) and optimization layer-by-layer (OLL) algorithms. The predictive accuracy for each algorithm were compared and it was found that the OLL network is the most accurate with a maximum mean relative error (MRE) of 1.78% and 1.38% for the direct and series predictive model respectively. Comparative results showed that the series neural network model gives the most accurate predictions, with MRE of 0.63% and 0.47% for the engine power and emissions respectively. The series neural network model can be seen as generic virtual power and emissions sensors, substituting costly and cumbersome hardware. Simple obtainable process parameters together with the series neural network will contribute immensely in control and tuning of emissions for real-time vehicular applications. © 2012 Elsevier B.V.


Yap W.K.,Charles Darwin University | Karri V.,Australian College of Kuwait
Expert Systems with Applications | Year: 2012

This paper presents a two-stage emissions predictive model developed by investigating common feedforward neural network models. The first stage model involves predicting engine parameters power and tractive forces and the predicted parameters are used as inputs to the second stage model to predict the vehicle emissions. The following gasses were predicted from the tailpipe emissions for a scooter application; CO, CO2, HC and O2. Three feedforward neural network models were investigated and compared in this study; backpropagation, optimization layer-by-layer and radial basis function networks. Based on the experimental setup, the neural network models were trained and tested to accurately predict the effect of the engine operating conditions on the emissions by varying the number of hidden nodes. The selected optimization layer-by-layer network proved to be the most accurate and reliable predictive tool with prediction errors of ±5%. The effect of the engine operating conditions on the tailpipe emissions for a scooter is shown to display similar qualitative and quantitative trends between the simulated and the experimental data. This study provides a better understanding in effects of engine process parameters on tailpipe emissions for the scooter as well as for general vehicular applications. © 2011 Elsevier Ltd. All rights reserved.


Yap W.K.,Charles Darwin University | Karri V.,Australian College of Kuwait
Journal of Solar Energy Engineering, Transactions of the ASME | Year: 2012

This paper presents a comparative study in predicting the monthly average solar radiation for Darwin, Australia (latitude 12.46 deg S longitude 130.84 deg E). The city of Darwin, Northern Territory (NT), has the highest and most consistent sunshine duration among all the other Australian states. This unique climate presents an opportunity for photovoltaic (PV) applications. Reliable and accurate predictions of solar radiation enable potential site locations, which exhibit high solar radiations and sunshine hours, to be identified for PV installation. Three predictive models were investigated in this study-the linear regression (LR), Angstrom-Prescott-Page (APP), and the artificial neural network (ANN) models. The mean global solar radiation coupled with the climate data (mean minimum and maximum temperatures, mean rainfall, mean evaporation, and sunshine fraction) obtained from the Australian Bureau of Meteorology (BoM) formed the basis of the dataset. Using simple and easily obtainable climate data presents an added advantage by reducing model complexity. Predictive results showed the root mean square errors (RMSEs) obtained were 6.72, 13.29, and 8.11 for the LR, APP, and ANN models, respectively. The predicted solar exposure from the LR model was then compared with the satellite-derived data to assess the accuracy of the LR method. © 2012 American Society of Mechanical Engineers.


Ho T.,University of Tasmania | Karri V.,Australian College of Kuwait
International Journal of Hydrogen Energy | Year: 2011

This paper presents the optimising control technique for a Toyota Corolla four-cylinder, 1.8-L hydrogen powered car. Based on the extensive experimental tuning data, statistical two stage models and calibration generation methodology are carried out, in which ignition timing, injection timing, injection duration and corresponding lambda value (indicate air to fuel ratio) are chosen as control variables while engine output torque and exhaust NOx emissions are chosen as performance index functions. The trade-off study is employed to optimise performance of hydrogen engine by considering different optimisation objectives at different engine operating states. Those engine operating states are defined by the throttle position and opening speed of throttle, except start and idle load states that need the auxiliary control parameters to be added in. Each value of ignition advance, lambda, injection duration and injection end angle are tested and the hydrogen engine is found to have good drivability and reliable on road optimisation. This work is a step towards establishing optimising control methodology of hydrogen powered car via application of advanced power train techniques while saving time, money and limiting damage for innovative hydrogen engine in early experimental fine tuning process. © 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights.


Ho T.,University of Tasmania | Karri V.,Australian College of Kuwait
International Journal of Hydrogen Energy | Year: 2010

Many studies of renewable energy have shown hydrogen is one of the major green energy in the future. This has lead to the development of many automotive application of using hydrogen as a fuel especially in internal combustion engine. Nonetheless, there has been a slow growth and less knowledge details in building up the prototype and control methodology of the hydrogen internal combustion engine [1]. In this paper, The Toyota Corolla 4 cylinder, 1.8l engine running on petrol was systematically modified in such a way that it could be operated on either gasoline or hydrogen at the choice of the driver. Within the scope of this project, several ancillary instruments such as a new inlet manifold, hydrogen fuel injection, storage system and leak detection safety system were implemented. Attention is directed towards special characteristics related to the basic tuning of hydrogen engine such as: air to fuel ratio operating conditions, ignition timing and injection timing in terms of different engine speed and throttle position. Based on the experimental data, a suite of neural network models were tested to accurately predict the effect of different engine operating conditions (speed and throttle position) on the hydrogen powered car engine characteristics. Predictions were found to be ±3% to the experimental values for all of case studies. This work provided better understanding of the effect of hydrogen engine characteristic parameters on different engine operating conditions. © 2010 Published by Elsevier Ltd on behalf of Professor T. Nejat Veziroglu.


Becker S.,University of Tasmania | Karri V.,Australian College of Kuwait
International Journal of Hydrogen Energy | Year: 2010

Predictive models were built using neural network based Adaptive Neuro-Fuzzy Inference Systems for hydrogen flow rate, electrolyzer system-efficiency and stack-efficiency respectively. A comprehensive experimental database forms the foundation for the predictive models. It is argued that, due to the high costs associated with the hydrogen measuring equipment; these reliable predictive models can be implemented as virtual sensors. These models can also be used on-line for monitoring and safety of hydrogen equipment. The quantitative accuracy of the predictive models is appraised using statistical techniques. These mathematical models are found to be reliable predictive tools with an excellent accuracy of ±3% compared with experimental values. The predictive nature of these models did not show any significant bias to either over prediction or under prediction. These predictive models, built on a sound mathematical and quantitative basis, can be seen as a step towards establishing hydrogen performance prediction models as generic virtual sensors for wider safety and monitoring applications. © 2010 Published by Elsevier Ltd on behalf of Professor T. Nejat Veziroglu.


Ho T.,Australian College of Kuwait | Karri V.,Australian College of Kuwait
International Journal of Hydrogen Energy | Year: 2011

This paper presents a research work on intelligent two-stage modelling system to estimate a hydrogen internal combustion engine performances including: engine torque and oxides of nitrogen emissions. In the created models, the ignition timing is chosen as a local input, while the engine speed, throttle position, injection duration, injection end angle and lambda are chosen as global inputs. While previous papers [1-4] included tuning procedures and hydrogen engine performances, intelligent emissions prediction of hydrogen car, and two-stage modelling of torque, this paper carries on from those observations to develop a completed two-stage modelling system of the converted hydrogen engine. More details on individual two-stage models are provided based on data recorded during the fine tuning process on dynamometer. This work is a step towards establishing intelligent two-stage modelling of hydrogen powered car via application of response surface methodology with hydrogen engine in the loop simulation and testing. © 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.


Al-Ka'Bi A.,Australian College of Kuwait
Proceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS | Year: 2015

The Multiple-input Multiple-Output (MIMO) wireless optical communications are studied in previous work. The spatial discrete multi-tone (SDMT) modulation technique is investigated in terms of its channel model and its capacity. This paper focuses on the capability of SDMT spatial modulation to combat the low pass spatial channel, where, a dynamic range compression technique was applied to exploit unused spatial frequency bins to reduce the peak value of output signals, thereby reducing clipping noise. © 2015 IEEE.


Serafino L.,Australian College of Kuwait
Journal of Theoretical Biology | Year: 2016

In this paper I will reflect on the intellectual rationale underlying the origin of life scientific research efforts by reconsidering some of its conceptual premises and difficulties. © 2016 Elsevier Ltd.

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