Hassen Taher Dorrah

This paper uncovers the mystery of consolidity, an inner property of systems that was amazingly hidden. Consolidity also reveals the secrecy of why strong stable and highly controllable systems are not invulnerable of falling and collapsing. Consolidity is measured by its Consolidity Index, defined as the ratio of overall changes of output parameters over combined changes of input and system parameters, all operating in fully fuzzy environment. Under this notion, systems are classified into consolidated, quasi-consolidated, neutrally consolidated, unconsolidated, quasi-unconsolidated and mixed types. The strategy for the implementation of consolidity is elaborated for both natural and man-made existing systems as well as the new developed ones. An important critique arises that the by-product consolidity of natural or built-as-usual system could lead to trapping such systems into a completely undesired unconsolidity. This suggests that the ample number of conventional techniques that do not take system consolidity into account should gradually be changed, and adjusted with improved consolidity-based techniques. Four Golden Rules are highlighted for handling system consolidity, and applied to several illustrative case studies. These case studies cover the consolidity analysis of the Drug Concentration problem, Predator-Prey Population problem, Spread of Infectious Disease problem, AIDS Epidemic problem and Arm Race model. It is demonstrated that consolidity changes are contrary (opposite in sign) to changes of both stability and controllability. This is a very significant result showing that our present practice of stressing on building strong stable and highly controllable systems could have already jeopardized the consolidity behavior of an ample family of existing real life systems. It is strongly recommended that the four Golden Rules of consolidity should be enforced as future strict regulations of systems modeling, analysis, design and building of different disciplines of sciences. It can be stated that with the mystery of consolidity uncovered, the door is now wide open towards the launching of a new generation of systems with superior consolidity in various sciences and disciplines. Examples of these disciplines are basic sciences, evolutionary systems, engineering, astronautics, astronomy, biology, ecology, medicine, pharmacology, economics, finance, commerce, political and management sciences, humanities, social sciences, literature, psychology, philosophy, mass communication, and education.

During the past decade, pond aeration systems have been developed which will sustain large quantities of fish and invertebrate biomass. Dissolved Oxygen (DO) is considered to be among the most important water quality parameters in fish culture. Fishponds in aquaculture farms are usually located in remote areas where grid lines are at far distance. Aeration of ponds is required to prevent mortality and to intensify production, especially when feeding is practical, and in warm regions. To increase pond production it is necessary to control dissolved oxygen. Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. This paper presents a new design of diffused aeration system using fuel cell as a power source. Also fuzzy logic
control Technique (FLC) is used for controlling the speed of air flow rate from the blower to air piping connected to the pond by adjusting blower speed. MATLAB SIMULINK results show high performance of fuzzy logic control (FLC).

The goal is to control the trajectory of the flight path of six degree of freedom flying body model using fractional PID. The design of fractional PID controller for the six degree of freedom flying body is described. The parameters of fractional PID controller are optimized by particle swarm optimization (PSO) method. In the optimization process, various objective functions were considered and investigated to reflect both improved dynamics of the missile system and reduced chattering in the control signal of the controller

Automation of generating hardware description language code of neural networks models can highly decrease time of implementation those networks into a digital devices, thus significant money savings. To implement the neural network into hardware design, it is required to translate generated model into device structure. VHDL language is used to describe those networks into hardware. VHDL code has been proposed to implement ANNs as well as to present simulation results with floating point arithmetic of the earth station and the satellite power systems using ModelSim® PE 6.6 simulator tool. Integration between MATLAB® and VHDL is used to save execution time of computation. The results shows that a good agreement between MATLAB and VHDL and a fast and flexible feed forward NN which is capable of dealing with floating point arithmetic operations; minimum number of CLB slices; and good speed of performance. FPGA synthesis results are obtained with view RTL schematic and technology schematic from Xilinix tool. Minimum number of utilized resources is obtained by using Xilinix VERTIX5.

This paper establishes the foundation of new systems’ Consolidity Theory using the Arithmetic Fuzzy Logic-Based Representation approach for investigating the internal behavior of systems operating in fully fuzzy environment. Consolidated systems are defined as being stable at the original state, but due to fuzzy variations in their inputs or parameters tend to react accordingly in a manner leading to maintaining their consolidity and strength, or vice versa. Under the new theory, systems are classified into consolidated, neutrally consolidated or unconsolidated type based on their output fuzziness reaction to combined input and parameters fuzziness action. The systems’ Consolidity Theory is demonstrated by several examples of mathematical functions of different dimensionalities, control theory and Predator-Prey populations’ dynamics. The suggested Consolidity Theory is illustrated to be an effective tool for revealing the inner property of systems and predicting their hidden behavior when operating in fully fuzzy environment. Monitoring and control of systems’ consolidity through forward and backward fuzziness tracking are suggested during systems’ operation, for avoiding their drifting to possible unwanted unconsolidated domains. It is shown that the analysis will lead at the end to determining the system’s consolidity index that could be regarded as a general basic internal property of the system. Such systems’ consolidity concept can also be defined far from fuzzy logic, and is applicable to the analysis and design of various types of linear, nonlinear, multivariable, dynamic, etc., systems in real life in the fields of basic sciences, evolutionary systems, engineering, biology, medicine, economics, finance, political and management sciences, social sciences, humanities, and education.

Renewable energy resources are inexhaustible, clean as compared with conventional resources. Also, it is used to supply regions with no grid, no telephone lines, and often with difficult accessibility by common transport. Satellite earth stations which located in remote areas are the most important application of renewable energy. Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This paper presents the mathematical modeling of satellite earth station power system which is required for simulating the system. Aswan is selected to be the site under consideration because it is a rich region with solar energy. The complete power system is simulated using MATLAB–SIMULINK. An artificial neural network (ANN) based model has been developed for the optimum
Operation of earth station power system. An ANN is trained using a back propagation with Levenberg–Marquardt algorithm. The best validation performance is obtained for minimum mean square error. The regression between the network output and the corresponding target is equal to 96% which means a high accuracy. Neural network controller architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the satellite earth station power system.

This paper provides a method one can model manufacturing processes in hybrid systems framework utilizing simple bond graph to determine the flow of events and differential equation models that describe the system dynamics. Controlling of these systems can be easy to develop. “Modeling and Simulation of thermal Power generation Station for power control” will be presented by using hybrid bond graph approach. This work includes the structure and components of the thermal electrical power generation stations and the importance of hybrid bond graph to model and control complex hybrid system, controlling of power plant will be presented by using Fuzzy P+ID controller.

As energy demands around the world increase, the need for a renewable energy sources that will not harm the environment is increased. The overall objective of renewable energy systems is to obtain electricity with competitive cost and even benefit with respect to other energy sources. The optimal design of renewable energy system can significantly improve the economical and technical performance of power supply. This paper presents the power management control using fuzzy logic control technique. Also, a complete mathematical modeling and MATLAB/Simulink model for the proposed the electrical part of an aquaculture system is implemented to track the system performance. The simulation results show the feasibility of control technique.

Aeration of water by using PEM fuel cell power is not only a new application of the renewable energy, but also, it provides an affordable method to promote biodiversity in stagnant ponds and lakes. This paper presents a new design and control of PEM fuel cell powered by diffused air aeration system for a shrimp farm in Mersa Matruh in Egypt. Also Artificial intelligence (AI) techniques control is used to control the fuel cell output power by controlling input gases flow rate. Moreover the mathematical modeling and simulation of PEM fuel cell is introduced. A comparison study is applied between the performance of fuzzy logic control (FLC) and neural network control (NNC). The results show the effectiveness of NNC over FLC.

The most common use of thermal solar energy has been for water heating systems; this use has been commercialized in many countries in the world. This paper presents a model of a forced circulation solar water heating system for supplying a hot water at a certain temperature for an aquaculture system. The main components of the system are flat plate collector, storage tank, and a biogas auxiliary heater. The optimization problem is carried out using a genetic algorithm, which is one of the modern optimization techniques because of its evolutionary nature; it can handle any kind of objective function and constraint. Genetic algorithms don’t have mathematical requirements about the optimization problem; Also genetic algorithms are very effective in performing a global search (in probability), and provide a great flexibility. The optimal design of flat plate collector area using genetic algorithm is used to optimize the objective function
considering the constraints required for the system. As the genetic algorithm is a discrete optimization tool, the number of variables in principle is unlimited. The economic analysis of such system is evaluated with the life cycle cost method. The collector area is equal to 63 m2, at this value the solar fraction reached 98% which is a very high value. Also, sensitivity analysis to solar radiation variation, air temperature variation, and interest rate has been carried out.

Satellite earth stations which located in remote areas are one of many applications powered by the renewable energy sources. Ground system consists of ground station and control centers working together to support the spacecraft and the data user. Earth station consists of major subsystems, transmitter, receiver, antenna, tracking equipment, terrestrial interface equipment and power supply. Power subsystem is an important part that required for supplying the earth station with electrical power to continue communicating with its remote sensing satellite. This paper deals with simulation and optimal sizing of earth station power system using HOMER software. A combination of two energy sources (solar, and wind) to provide a continuous electric power production is used to determine the optimum system operation. Three system configurations are compared with respect to the total net present cost (NPC) and levelized cost of energy (COE). Also, economical study will be analyzed for energy demand and sensitivity analysis will be performed.