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Title: Maximum power point tracking of photovoltaic systems.
Creator: Bennett, Thomas, Zilouchian, Ali, Graduate College
Date Issued: 2011-04-08
PURL: http://purl.flvc.org/fcla/dt/3164452
Subject Headings: Photovoltaic power systems, Solar energy, DC-to-DC converters
Format: Document (PDF)

Title: Technology and Design of a Fuel Cell - Photo-Voltaic Powered Vehicle with an Energy Storage System.
Creator: Augustin, Windy, Carvalho Dias, Thiago, Flit, Mike, Bennett, Thomas, Zilouchian, Ali
Abstract/Description: FAU's Office of Undergraduate Research and Inquiry hosts an annual symposium where students engaged in undergraduate research may present their findings either through a poster presentation or an oral presentation.
Date Issued: 2011
PURL: http://purl.flvc.org/fau/fd/FA00005427
Format: Document (PDF)

Title: Application of fuzzy logic for the solution of inverse kinematics and hierarchical controls of robotic manipulators.
Creator: Howard, David William., Florida Atlantic University, Zilouchian, Ali
Abstract/Description: In this thesis work, hierarchical control techniques will be used for controlling a robotic manipulator. The hierarchical control will be implemented with fuzzy logic to improve the robustness and reduce the run time computational requirements. Hierarchical control will consist on solving the inverse kinematic equations using fuzzy logic to direct each individual joint. A commercial Micro-robot with three degrees of freedom will be used to evaluate this methodology. A decentralized fuzzy...
Show moreIn this thesis work, hierarchical control techniques will be used for controlling a robotic manipulator. The hierarchical control will be implemented with fuzzy logic to improve the robustness and reduce the run time computational requirements. Hierarchical control will consist on solving the inverse kinematic equations using fuzzy logic to direct each individual joint. A commercial Micro-robot with three degrees of freedom will be used to evaluate this methodology. A decentralized fuzzy controller will be used for each joint, with a Fuzzy Associative Memories (FAM) performing the inverse kinematic mapping in a supervisory mode. The FAM determines the inverse kinematic mapping which maps the desired Cartesian coordinates to the individual joint angles. The individual fuzzy controller for each joint will generate the required control signal to a DC motor to move the associated link to the new position. The proposed hierarchical fuzzy controller will be compared to a conventional PD controller.
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Date Issued: 1997
PURL: http://purl.flvc.org/fcla/dt/15407
Subject Headings: Robotics, Fuzzy logic, Robots--Kinematics
Format: Document (PDF)

Title: Prediction of crude oil product quality parameters using neural networks.
Creator: Bawazeer, Khalid Ahmed., Florida Atlantic University, Zilouchian, Ali
Abstract/Description: Inferential analysis using neural networks technology is being proposed for the Ras Tanura Refinery crude fractionation section. Plant data for a three month operation period is analyzed in order to construct a neural network model with backpropagation training algorithm. The proposed neural network model can predict various properties associated with crude oil products. The simulation results for modeling Naphtha 95% cut point and Naphtha Reid vapor pressure properties are analyzed. A fuzzy...
Show moreInferential analysis using neural networks technology is being proposed for the Ras Tanura Refinery crude fractionation section. Plant data for a three month operation period is analyzed in order to construct a neural network model with backpropagation training algorithm. The proposed neural network model can predict various properties associated with crude oil products. The simulation results for modeling Naphtha 95% cut point and Naphtha Reid vapor pressure properties are analyzed. A fuzzy neural network model is also proposed that takes into account the fuzziness in both process variables and the corresponding product quality parameter. The training algorithm is derived based on the backpropagation technique. The results of the proposed study can ultimately enhance the on-line prediction of crude oil product quality parameters for crude fractionation processes in the Ras Tanura Oil Refinery.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/15302
Subject Headings: Petroleum products--Analysis, Petroleum products--Testing, Petroleum industry and trade--Quality control, Neural networks (Computer science)
Format: Document (PDF)

Title: Realization and implementation of separable-in-denominator two-dimensional digital filter.
Creator: Huang, Ziqiang., Florida Atlantic University, Zilouchian, Ali
Abstract/Description: In this thesis, a partial fraction expansion of a separable-in-denominator 2-D transfer function is given. Based on this expansion, several novel realizations of separable-in-denominator 2-D filter are provide. These realizations have the properties of highly parallel structure and improved throughput delay. The performance figures are given in the tables. A method of evaluation of quantization error of separable-in-denominator 2-D filter is also derived by using the residue method. Formulas...
Show moreIn this thesis, a partial fraction expansion of a separable-in-denominator 2-D transfer function is given. Based on this expansion, several novel realizations of separable-in-denominator 2-D filter are provide. These realizations have the properties of highly parallel structure and improved throughput delay. The performance figures are given in the tables. A method of evaluation of quantization error of separable-in-denominator 2-D filter is also derived by using the residue method. Formulas for calculation of roundoff noise of proposed structures are provided. Two programs which can be used to calculate the roundoff noise of proposed structure are listed in the Appendix. To run the programs, we need only to input the constant coefficients of expanded transfer function. At last, an optimal block realization of separable-in-denominator 2-D filter is discussed and the criterion for the absence of limit cycles for a second-order 2-D block is given.
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Date Issued: 1992
PURL: http://purl.flvc.org/fcla/dt/14879
Subject Headings: Real-time data processing, Image processing--Digital techniques, Electric filters, Digital--Computer programs
Format: Document (PDF)

Title: Two-dimensional approximation and learning control of robot manipulators.
Creator: Gautam, Ashutosh., Florida Atlantic University, Zilouchian, Ali
Abstract/Description: In this thesis, a novel two-dimensional learning control scheme for robot manipulators is proposed. The convergence of the scheme for a general n-degree of freedom robot is shown. In the next part of the thesis, an algorithm for the approximation of a two-dimensional causal, recursive, separable-in-denominator (CRSD) filter, using the impulse response and autocorrelation data, is presented. The stability of the designed filter is discussed and it is shown that the approximated filter is...
Show moreIn this thesis, a novel two-dimensional learning control scheme for robot manipulators is proposed. The convergence of the scheme for a general n-degree of freedom robot is shown. In the next part of the thesis, an algorithm for the approximation of a two-dimensional causal, recursive, separable-in-denominator (CRSD) filter, using the impulse response and autocorrelation data, is presented. The stability of the designed filter is discussed and it is shown that the approximated filter is always stable. The simulation results for the approximation technique as well as the two-dimensional learning control scheme are also included in the thesis.
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Date Issued: 1989
PURL: http://purl.flvc.org/fcla/dt/14559
Subject Headings: Control theory, Manipulators (Mechanism), Robots
Format: Document (PDF)

Title: Optimal Energy Scheduling of a Hybrid Microgrid Considering Environmental Aspects.
Creator: Moradi, Hadis, Abtahi, Amir, Zilouchian, Ali, Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: Lower costs of clean energy generation, the need for a more secure grid, and environmental concerns are leading to create more opportunities for integration of renewable energy resources utilization in the power systems. The recent concept of Microgrid (MG), as a part of the development of smart grid, is required in order to integrate the renewable sources in the utility grid. An MG is described as a small-scale distribution grid that consists of diversified Distributed Energy Resources (DERs...
Show moreLower costs of clean energy generation, the need for a more secure grid, and environmental concerns are leading to create more opportunities for integration of renewable energy resources utilization in the power systems. The recent concept of Microgrid (MG), as a part of the development of smart grid, is required in order to integrate the renewable sources in the utility grid. An MG is described as a small-scale distribution grid that consists of diversified Distributed Energy Resources (DERs), Battery Energy Storage Systems (BESSs), and local flexible loads that typically can either be operated in islanded or grid-connected modes. The optimal utilization control of such an MG system is a challenging task due to the complexity of coordination among the DERs, BESSs and load management possibilities. Therefore, in this dissertation, optimal component sizing and operation of MGs under different operational strategies is proposed. MGs typically consist of Photovoltaic (PV) systems, wind turbines as well as microgas turbines, fuel cells, batteries and other dispatchable generating units. Firstly, a methodology to perform the optimal component sizing for DERs in islanded/grid-tied modes is developed. The proposed optimal algorithm aims to determine the appropriate configuration among a set of components by taking into consideration the system’s constraints. An Iterative optimization technique is proposed in order to minimize the annual cost of energy and cost of emissions including CO2, SO2, and NOx. A case study from South Florida area, given the local weather data and load demand is investigated for the modeling verification. Using the results from optimal component sizes, a day-ahead optimization problem for the operation of an MG under different scenarios is introduced. Also, the objective function is formulated as a constrained non-linear problem. The uncertainties of stochastic variables (solar radiation, wind speed, and load) are modeled and renewable generations and load demand are forecasted. An advanced dynamic programing procedure is proposed to assess various operational policies. The simulation results show the efficiency of the proposed method.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00005014
Subject Headings: Dissertations, Academic -- Florida Atlantic University
Format: Document (PDF)

Title: Design and implementation of intelligent control methodologies for reverse osmosis plants.
Creator: Jafar, Mutaz M., Florida Atlantic University, Zilouchian, Ali, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This dissertation presents the design, implementation and application of soft computing methodologies to Reverse Osmosis (RO) desalination technology. A novel intelligent control scheme based on the integration of Neural Network (NN) and Fuzzy Logic (FL) is presented to optimize plants' performance. In the first part of the research work, two optimal NN predictive models, based on backpropagation and Radial Basis Function Networks (RBFN), were developed for three types of RO feed intakes. The...
Show moreThis dissertation presents the design, implementation and application of soft computing methodologies to Reverse Osmosis (RO) desalination technology. A novel intelligent control scheme based on the integration of Neural Network (NN) and Fuzzy Logic (FL) is presented to optimize plants' performance. In the first part of the research work, two optimal NN predictive models, based on backpropagation and Radial Basis Function Networks (RBFN), were developed for three types of RO feed intakes. The predictive models utilized actual operating data for the three RO plants in order to predict system recovery, total dissolved solids and ion product concentration in brine stream A predictive model is proposed based on redistributed receptive fields of RBFN. The proposed algorithm utilizes integration of supervised learning of centers and unsupervised learning of output layer weights. Extensive simulations are presented to demonstrate the effectiveness of the proposed method for generalization on prediction of nonlinear input-output mappings. In the second part of the study, the design of FL control strategy for direct seawater RO system is carried out. The real-time controller design is based on integration of sensory information, predicted outputs, mathematical calculations, and expert knowledge of the process to yield a constant recovery, constant salt rejection and minimum scaling under variable operating conditions. To implement the designed methodology, a 250/800 Gallon per Day (GPD) prototype RO plant with direct Atlantic Ocean intake is constructed at FAU Gumbo Limbo research laboratory. Two types of membrane modules were used for this study: Spiral Wound (SW) and Hollow Fine Fiber (HFF). The prototype plant indeed demonstrated the effectiveness and optimum performance of the proposed design under variable operating conditions. The system achieved a constant recovery of 30% and salt passage of 1.026% while ion product concentration for six major salts were kept below their solubility limits at all time. The implementation of the proposed intelligent control methodology achieved a 4% increase in availability and a 50% reduction in manpower requirements, as well as reduction in overall chemical consumption of the plant. Therefore, it is expected that the cost of producing fresh water from seawater desalination will be decreased using the proposed intelligent control strategy.
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Date Issued: 2000
PURL: http://purl.flvc.org/fcla/dt/12650
Subject Headings: Saline water conversion--Reverse osmosis process, Intelligent control systems
Format: Document (PDF)

Title: Modeling of reverse osmosis plants using system identification and neural networks.
Creator: Saengrung, Anucha, Florida Atlantic University, Zilouchian, Ali, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Modeling of two reverse osmosis plants at FAU Gumbo Limbo facility and at the city of Boca Raton are investigated. System identification as well as artificial neural networks are utilized to carried out the tasks. The data for a six months operational period of both plants are utilized. The prediction error method and subspace method are utilized to estimate state-space model while the auto regression with extra input (ARX) model is estimated by using the least square method and the...
Show moreModeling of two reverse osmosis plants at FAU Gumbo Limbo facility and at the city of Boca Raton are investigated. System identification as well as artificial neural networks are utilized to carried out the tasks. The data for a six months operational period of both plants are utilized. The prediction error method and subspace method are utilized to estimate state-space model while the auto regression with extra input (ARX) model is estimated by using the least square method and the approximately optimal four-stage instrumental variable method. The training algorithms for artificial neural networks are based on backpropagation and radial basis network function (RBNF). The implementation of each methodology is performed step by step and finally, the results from both methodologies are analyzed and discussed. The results of the proposed study indicate that both system identification and neural networks algorithms can predict the outputs of both RO plants with the acceptable accuracy. Among all methodologies utilized in the thesis, the least square method for the auto regression with the extra input (ARX) model, can provide the best prediction for both RO plants.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12963
Subject Headings: Saline water conversion--Reverse osmosis process, System identification, Neural networks (Computer science)
Format: Document (PDF)

Title: Model reduction of large-scale systems using perturbed frequency-domain balanced structure.
Creator: Zadegan, Abbas Hassan., Florida Atlantic University, Zilouchian, Ali, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Model reduction of large-scale systems over a specified frequency range of operation is studied in this research and reported in this dissertation. Frequency-domain balanced structures with integration of singular perturbation are proposed for model reduction of large-scale continuous-time as well as discrete-time systems. This method is applied to both open-loop as well as closed-loop systems. It is shown that the response of reduced systems closely resemble that of full order systems within...
Show moreModel reduction of large-scale systems over a specified frequency range of operation is studied in this research and reported in this dissertation. Frequency-domain balanced structures with integration of singular perturbation are proposed for model reduction of large-scale continuous-time as well as discrete-time systems. This method is applied to both open-loop as well as closed-loop systems. It is shown that the response of reduced systems closely resemble that of full order systems within a specified frequency range of operation. Simulation experiments for the model reduction of several large-scale, continuous and discrete-time systems demonstrate the superiority of the proposed technique over the previously available methods.
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Date Issued: 2004
PURL: http://purl.flvc.org/fcla/dt/12114
Subject Headings: System analysis, Large scale systems--Mathematical models, System design, Control theory, Mathematical optimization
Format: Document (PDF)

Title: Implementation of digital filters with LabVIEW.
Creator: Landrin, Thomas., Florida Atlantic University, Zilouchian, Ali, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In this thesis, realization and implementation of one-dimensional (1-D) and two-dimensional (2-D) recursive digital filters using LabVIEW are presented. A number of direct and state-space realizations for 1-D filters are implemented either as in general form or second-order modules. Implementation programs are provided and simulation results are presented to show the effectiveness of the proposed method. In addition, several realizations for 2-D separable-in-denominator filters are proposed....
Show moreIn this thesis, realization and implementation of one-dimensional (1-D) and two-dimensional (2-D) recursive digital filters using LabVIEW are presented. A number of direct and state-space realizations for 1-D filters are implemented either as in general form or second-order modules. Implementation programs are provided and simulation results are presented to show the effectiveness of the proposed method. In addition, several realizations for 2-D separable-in-denominator filters are proposed. These realizations have the properties of highly parallel structure and improved throughput delay. The performance as well as the implementation of 2-D filters using LabVIEW is also presented.
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Date Issued: 2004
PURL: http://purl.flvc.org/fcla/dt/13183
Subject Headings: LabVIEW, Computer programming, Computer graphics, Electric filters, Digital--Design and construction, Recursive functions--Data processing, Signal processing--Digital techniques
Format: Document (PDF)

Title: Identification and approximation of one-dimensional and two-dimensional digital filters.
Creator: Wang, Dali., Florida Atlantic University, Zilouchian, Ali, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In this dissertation, identification and approximation of one-dimensional (1-D) and two-dimensional (2-D) recursive digital filters are addressed. In the identification phase, a novel Neural Network (NN) structure is proposed which provides the state-space model of 1-D filters based upon input-output data. The state space identification technique is also extended to 2-D digital filters and several comparison studies are performed. In the approximation phase, frequency-domain balanced...
Show moreIn this dissertation, identification and approximation of one-dimensional (1-D) and two-dimensional (2-D) recursive digital filters are addressed. In the identification phase, a novel Neural Network (NN) structure is proposed which provides the state-space model of 1-D filters based upon input-output data. The state space identification technique is also extended to 2-D digital filters and several comparison studies are performed. In the approximation phase, frequency-domain balanced structures for 1-D as well as 2-D digital filters are proposed. The model reduction technique is based on the conceptual view point of balancing the controllability and observability Grammians of a digital filter in an arbitrary frequency range of operation. Finally, the interrelations between these two phases are presented. Extensive simulation experiments are presented to demonstrate the effectiveness of proposed methods.
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Date Issued: 1998
PURL: http://purl.flvc.org/fcla/dt/12555
Subject Headings: Digital filters (Mathematics), Signal processing--Digital technique, Electric filters, Digital
Format: Document (PDF)

Title: Modeling and analysis of aluminum/air fuel cell.
Creator: Leon, Armando J., Zilouchian, Ali, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The technical and scientific challenges to provide reliable sources energy for US and global economy are enormous tasks, and especially so when combined with strategic and recent economic concerns of the last five years. It is clear that as part of the mix of energy sources necessary to deal with these challenges, fuel cells technology will play critical or even a central role. The US Department of Energy, as well as a number of the national laboratories and academic institutions have been...
Show moreThe technical and scientific challenges to provide reliable sources energy for US and global economy are enormous tasks, and especially so when combined with strategic and recent economic concerns of the last five years. It is clear that as part of the mix of energy sources necessary to deal with these challenges, fuel cells technology will play critical or even a central role. The US Department of Energy, as well as a number of the national laboratories and academic institutions have been aware of the importance such technology for some time. Recently, car manufacturers, transportation experts, and even utilities are paying attention to this vital source of energy for the future. In this thesis, a review of the main fuel cell technologies is presented with the focus on the modeling, and control of one particular and promising fuel cell technology, aluminum air fuel cells. The basic principles of this fuel cell technology are presented. A major part of the study consists of a description of the electrochemistry of the process, modeling, and simulations of aluminum air FC using Matlab Simulink™. The controller design of the proposed model is also presented. In sequel, a power management unit is designed and analyzed as an alternative source of power. Thus, the system commutes between the fuel cell output and the alternative power source in order to fulfill a changing power load demand. Finally, a cost analysis and assessment of this technology for portable devices, conclusions and future recommendations are presented.
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Date Issued: 2013
PURL: http://purl.flvc.org/fau/fd/FA0004032
Subject Headings: Biomass energy, Electrocatalysis, Electrolytic capacitors -- Materials, Fuel cells -- Materials, MATLAB, Nanostructured materials, Renewable energy sources
Format: Document (PDF)

Title: Achieving Higher Receiver Satisfaction using Multicast-Favored Bandwidth Allocation Protocols.
Creator: Yousefizadeh, Hooman, Zilouchian, Ali, Ilyas, Mohammad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In recent years, many protocols for efficient Multicasting have been proposed. However, many of the Internet Service Providers (ISPs) are reluctant to use multicastenabled routers in their networks. To provide such incentives, new protocols are needed to improve the quality of their services. The challenge is to find a compromise between allocating Bandwidth (BW) among different flows in a fair manner, and favoring multicast sessions over unicast sessions. In addition, the overall higher...
Show moreIn recent years, many protocols for efficient Multicasting have been proposed. However, many of the Internet Service Providers (ISPs) are reluctant to use multicastenabled routers in their networks. To provide such incentives, new protocols are needed to improve the quality of their services. The challenge is to find a compromise between allocating Bandwidth (BW) among different flows in a fair manner, and favoring multicast sessions over unicast sessions. In addition, the overall higher level of receiver satisfaction should be achieved. In this dissertation, we propose three new innovative protocols to favor multicast sessions over unicast sessions. Multicast Favored BW Allocation- Logarithmic (MFBA-Log) and Multicast Favored BW Allocation-Linear (MFBALin) protocols allocate BW proportional to the number of down stream receivers. The proposed Multicast Reserved BW Allocation (MRBA) protocol allocates part of the BW in the links only to multicast sessions. Simulation results show the increase in the overall level of Receiver Satisfaction in the network.
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Date Issued: 2006
PURL: http://purl.flvc.org/fau/fd/FA00012581
Subject Headings: Multicasting (Computer networks), Computer network protocols, Computer algorithms
Format: Document (PDF)

Title: Derivation and identification of linearly parametrized robot manipulator dynamic models.
Creator: Xu, Hua., Florida Atlantic University, Roth, Zvi S., Zilouchian, Ali, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The dissertation focuses on robot manipulator dynamic modeling, and inertial and kinematic parameters identification problem. An automatic dynamic parameters derivation symbolic algorithm is presented. This algorithm provides the linearly independent dynamic parameters set. It is shown that all the dynamic parameters are identifiable when the trajectory is persistently exciting. The parameters set satisfies the necessary condition of finding a persistently exciting trajectory. Since in...
Show moreThe dissertation focuses on robot manipulator dynamic modeling, and inertial and kinematic parameters identification problem. An automatic dynamic parameters derivation symbolic algorithm is presented. This algorithm provides the linearly independent dynamic parameters set. It is shown that all the dynamic parameters are identifiable when the trajectory is persistently exciting. The parameters set satisfies the necessary condition of finding a persistently exciting trajectory. Since in practice the system data matrix is corrupted with noise, conventional estimation methods do not converge to the true values. An error bound is given for Kalman filters. Total least squares method is introduced to obtain unbiased estimates. Simulations studies are presented for five particular identification methods. The simulations are performed under different noise levels. Observability problems for the inertial and kinematic parameters are investigated. U%wer certain conditions all L%wearly Independent Parameters derived from are observable. The inertial and kinematic parameters can be categorized into three parts according to their influences on the system dynamics. The dissertation gives an algorithm to classify these parameters.
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Date Issued: 1992
PURL: http://purl.flvc.org/fcla/dt/12291
Subject Headings: Algorithms, Manipulators (Mechanism), Robots--Control systems
Format: Document (PDF)

Title: Design and Implementation of a Solar Platform and a Fuel Cell System on an Electric Utility Vehicle.
Creator: Saelzer, Max W., Zilouchian, Ali, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In thesis work, an innovative design and implementation of a "trybrid" system is carried out. This new "trybrid" system is the continuation of the solar electric vehicle which was designed and developed as an undergraduate independent study under the supervision of Dr. Roger Messenger. The work done on this thesis consists in the implementation of the existing solar electric vehicle outfitted with a fuel cell system. For this, two fuel cell systems were analyzed as possible candidates for the...
Show moreIn thesis work, an innovative design and implementation of a "trybrid" system is carried out. This new "trybrid" system is the continuation of the solar electric vehicle which was designed and developed as an undergraduate independent study under the supervision of Dr. Roger Messenger. The work done on this thesis consists in the implementation of the existing solar electric vehicle outfitted with a fuel cell system. For this, two fuel cell systems were analyzed as possible candidates for the retrofit. Also, different hydrogen storage methods are analyzed in terms of efficiency and safety. This thesis also covers the control system utilized to manage the energy from the two different energy sources. Special attention is devoted to the method of determining the state of charge of the batteries which controls fuel cell turn on time. This first prototype shows the different functionalities and optimized features that maximize the solar energy production, and determines exactly when to turn on the fuel cell for minimum hydrogen usage.
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Date Issued: 2006
PURL: http://purl.flvc.org/fau/fd/FA00012546
Subject Headings: Electric power systems, Hybrid electric vehicles, Fuel cells--Technological innovations, Automobile industry and trade--Forecasting
Format: Document (PDF)

Title: Modeling and Control of Proton Exchange Membrane (PEM) Fuel Cell System.
Creator: Saengrung, Anucha, Zilouchian, Ali, Abtahi, Amir, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This dissertation presents the design, implementation and application of soft computing methodologies to Proton Exchange Membrane (PEM) Fuel Cell systems. In the first part of the research work, two distinct approaches for the modeling and prediction of a commercial PEM fuel cell system are presented. Several Simulink models are constructed from the electrochemical models of the PEM fuel cells. The models have been simulated in three dimension (3-D) space to provide the visual understanding...
Show moreThis dissertation presents the design, implementation and application of soft computing methodologies to Proton Exchange Membrane (PEM) Fuel Cell systems. In the first part of the research work, two distinct approaches for the modeling and prediction of a commercial PEM fuel cell system are presented. Several Simulink models are constructed from the electrochemical models of the PEM fuel cells. The models have been simulated in three dimension (3-D) space to provide the visual understanding of fuel cell behaviors. In addition, two optimal predictive models, based on back-propagation (BP) and radial basis function (RBF) neural networks are developed. Experimental data as well as pre-processing data are utilized to determine the accuracy and speed of the proposed prediction algorithms. Extensive simulation results are presented to demonstrate the effectiveness of the proposed method on prediction of nonlinear input-output linear input-output mapping. In the second part of the study, the design and implementation of several fuzzy logic controllers (FLCs) as well as classical controllers are carried out. The proposed real-time controller design is based on the integration of sensory information, Labview programming, mathematical calculation, and expert knowledge of the process to yield optimum output power performance under variable load condition. The implementations of the proposed controllers are carried out for a commercial PEM fuel system at FA U Fuel Cell Laboratory. The performance of the proposed controllers pertaining to the oxygen (02) flow rate optimization as well as the actual fuel cell output power under a variable load bank are compared and investigated. It was found the Fuzzy Logic Controller design provide a simple and effective approach for the implementation of the fuel cell systems.
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Date Issued: 2008
PURL: http://purl.flvc.org/fau/fd/FA00012577
Subject Headings: Proton exchange membrane fuel cells--Design and construction, Proton exchange membrane fuel cells--Computer simulation, Fuel cells--Design and construction
Format: Document (PDF)

Title: Fuzzy identification of processes on finite training sets with known features.
Creator: Diaz-Robainas, Regino R., Florida Atlantic University, Huang, Ming Z., Zilouchian, Ali, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: A methodology is presented to construct an approximate fuzzy-mapping algorithm that maps multiple inputs to single outputs given a finite training set of argument vectors functionally linked to corresponding scalar outputs. Its scope is limited to problems where the features are known in advance, or equivalently, where the expected functional representation is known to depend exclusively on the known selected variables. Programming and simulations to implement the methodology make use of...
Show moreA methodology is presented to construct an approximate fuzzy-mapping algorithm that maps multiple inputs to single outputs given a finite training set of argument vectors functionally linked to corresponding scalar outputs. Its scope is limited to problems where the features are known in advance, or equivalently, where the expected functional representation is known to depend exclusively on the known selected variables. Programming and simulations to implement the methodology make use of Matlab Fuzzy and Neural toolboxes and a PC application of Prolog, and applications range from approximate representations of the direct kinematics of parallel manipulators to fuzzy controllers.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/12487
Subject Headings: Fuzzy algorithms, Set theory, Logic, Symbolic and mathematical, Finite groups, Representations of groups
Format: Document (PDF)

Title: FACILITATING PEER-TO-PEER ENERGY TRADING THROUGH COOPERATIVE GAMES AND FUZZY INFERENCE SYSTEMS.
Creator: Lopez, Hector, Zilouchian, Ali, Abtahi, Amir, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: This dissertation proposes a utility-centric peer-to-peer (P2P) energy trading framework as an alternative to traditional net metering, aiming to resolve conflicts between distributed energy resource owners and utilities. It advocates for practical software services and dynamic payment mechanisms tailored to prosumer needs, offering an alternative to reducing net metering incentives. Additionally, it explores game theory principles to ensure equitable compensation for prosumer cooperation,...
Show moreThis dissertation proposes a utility-centric peer-to-peer (P2P) energy trading framework as an alternative to traditional net metering, aiming to resolve conflicts between distributed energy resource owners and utilities. It advocates for practical software services and dynamic payment mechanisms tailored to prosumer needs, offering an alternative to reducing net metering incentives. Additionally, it explores game theory principles to ensure equitable compensation for prosumer cooperation, driving the adoption of P2P energy markets. It also builds on demand-side payment mechanisms like NRG-X-Change by adapting it to provide fair payment distribution to prosumer coalitions. The interoperable energy storage systems with P2P trading also presented battery chemistry detection using neural network models. A fuzzy inference system is also designed to facilitate prosumers' choice in participating in P2P markets, providing flexibility for energy trading preferences. The simulation results demonstrated the effectiveness of the proposed design schemes.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014425
Subject Headings: Energy, Fuzzy systems, Cooperative game theory, Electrical engineering
Format: Document (PDF)

FAU Theses and Dissertations (17)	+ -
FAU Graduate Student Research (1)	+ -
FAU Undergraduate Student Research (1)	+ -

Control theory (2)	+ -
Manipulators (Mechanism) (2)	+ -
Neural networks (Computer science) (2)	+ -
Saline water conversion--Reverse osmosis process (2)	+ -
Algorithms (1)	+ -

Automobile industry and trade--Forecasting (1)	+ -
Biomass energy (1)	+ -
Computer algorithms (1)	+ -
Computer graphics (1)	+ -
Computer network protocols (1)	+ -
Computer programming (1)	+ -
Cooperative game theory (1)	+ -
DC-to-DC converters (1)	+ -
Digital filters (Mathematics) (1)	+ -
Dissertations, Academic -- Florida Atlantic University (1)	+ -
Electric filters, Digital (1)	+ -
Electric filters, Digital--Computer programs (1)	+ -
Electric filters, Digital--Design and construction (1)	+ -
Electric power systems (1)	+ -
Electrical engineering (1)	+ -
Electrocatalysis (1)	+ -
Electrolytic capacitors -- Materials (1)	+ -
Energy (1)	+ -
Finite groups (1)	+ -
Fuel cells -- Materials (1)	+ -
Fuel cells--Design and construction (1)	+ -
Fuel cells--Technological innovations (1)	+ -
Fuzzy algorithms (1)	+ -
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