Work place: Head of Artificial Intelligent and Autonomous Systems Engineering Technology Program, Project Manager of Joint Aviation Command (JAC) Program, Abu Dhabi Polytechnic, IAT, Abu Dhabi, UAE
Research Interests: Big Data, Neural Networks, Machine Learning, Intelligent Systems, Big Data Analytics
Dr. Murad Al Shibli, Associate Professor, has completed his Ph.D. in 2006 from Concordia University in Canada. Works as the Head of Autonomous Systems Engineering Technology at Abu Dhabi Polytechnic in UAE as well as the Project Manager of Joint Aviation Command Program. Holds more than 25 years of industrial and academic experience in Canada, Jordan and UAE, at Concordia University, German Jordanian University, American University of Sharjah, UAEU University, and Abu Dhabi Polytechnic affiliated with Institute of Applied Technology. Established several new programs in AI, Autonomous Systems, Electromechanical Engineering Technology and Mechatronics Systems at several universities. Worked as the chair of the Mechatronics Program at German Jordanian University. Published more than 50 publications in international journals and conferences. Registered 6 provisional patents in USA. Active researcher in artificial intelligent systems, neural networks, machine learning and big data, robotics, autonomous systems, drones, control systems, mechatronics, control and space systems, and technical education paradigm. Member of IEEE, AIAA, ISA, NASA-TELCOM-ROBEXS, ICREPQ, Round Table Group, and Autonomous Canada.
DOI: https://doi.org/10.5815/ijisa.2019.06.01, Pub. Date: 8 Jun. 2019
Utilization of the sustainable and renewable sea wave energy has recently received special attention by the virtue of being a free, clean and zero-carbon footprint power source. This paper presents a novel approach to model, design, analyze and control a sea wave electric power generating system using an artificial intelligent nonlinear auto regressive with external input neural networks (NARX-NN). Modeling design, and analysis of an electro-mechanical power-generating system using linear permanent magnet generator attached to a dual spring-mass-damper platforms is introduced. The purpose of this proposed generator is to convert sea and ocean wave kinetic energy into a useful electrical power generated as a result of the linear motion core through an electromagnetic stator. One of the direct applications of the sea wave generator is to install one or more units on shipboard to contribute to its power utility needs whether it is moving or floating. The dynamical stability and compensator control of the spring-mass damper generator platform is analyzed along with its associated electric power. Faraday’s law based results show that the output induced voltage ranges from -60 to 60 volts (120 volts p-p). Moreover, artificial intelligent nonlinear auto-regressive neural networks are used to train, validate, and test the sea wave electric generator output. Two-layer NN are used to train the dynamical input-output relationship of the proposed system using one hidden layer that contains of 10 neurons. Two delays are used, one for motion input and one for voltage output. The NARX-NN training demonstrates that the network is being trained efficiently and tracks the actual sea wave electric generator output with a very low mean-square-error performance response without the need to measure the variables.[...] Read more.
DOI: https://doi.org/10.5815/ijisa.2019.04.01, Pub. Date: 8 Apr. 2019
Nowadays sustainable underground geothermal energy resources have received special attention thanks for being characterized as clean, zero-carbon footprint, reliable, and free source of renewable energy that can run all year long and around the clock. Barren desert lands, which make up 33% and contribute to almost 30 Million km² of global land surface area, is increasingly seen as supply of green energy but not yet efficiently and globally utilized although it can save up to 70% compared to traditional HVAC systems bills. This paper presents a novel artificial intelligent machine learning and big data algorithm to analyze and control geothermal heat pump system (GHP). In particular, the main objective of this research is to model, design, analyze, control and optimize the performance of desert underground GTH system based on thermodynamics laws and AI machine learning. As a case study, the analysis and design of desert GHP is performed based on the annual weather data collected for Al Ain city in UAE. By selecting a horizontal layout, the design analysis results show that GHP unit needs a 66 m total trench length with a cooling capacity estimated of 12.4 kW, heat pump COP of 2.8 and 1.6 for the system COP with 30.3 L/min water flow rate. Similar results for the heating system are obtained as well. Furthermore, financial calculations show the GHP system is very economic and competitive comparing with the traditional cooling/heating systems. It is figured out that the annual cost of the GHP system costs around $1676 compared with $7992 if air-cooled chiller and boiler are used. To maintain the geothermal system for one life cycle (usually 20 years) it needs to spend only $14,659 compared with $109,944 in case HVAC system is utilized. The overall life cycle cost in case of the desert GHP system does not exceed (45%) of the traditional HVAC system ($81,881 compared to $181,974). One of the direct applications is use this proposed desert GHP to cool the roof water tank for domestic and personal usage. Furthermore, artificial intelligent and big data machine learning is executed to analyze the weather conditions related to the GHP performance based on huge number of thermal observations recorded for the years 2015-2018. Moreover, the mean switch-off control hours of the GHP is examined by developing a supervised learning predictive model. For the purpose of validation a four ton Bosch GHP unit is selected as a benchmark. Switch-off control hours per month for the entire geothermal data set are demonstrated by using a linear regression model that help to guide the controller to switch-on/switch-off the system without having the need for the real data measurement. One primary outcome obtained is the ability to optimize the GHP performance, save primary input energy and operation periods. Furthermore, the results interprets that almost one third of the year is in a switched-off saving mode (33%), compared to 67% in switch-on mode. This smart big data control will lead to a life-cycle saving of $27,020. This AI saving strategy is found to be competitive and leading compared to other schemes. It is worthy to recommend linking GHP controller with real-time radar or weather station that will fed the system with real data conditions which would lead to improving its performance and dispense costly measuring sensors.[...] Read more.
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