27 resultados para mathematical model

em Universidade Federal do Rio Grande do Norte(UFRN)


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Currently the uncertain system has attracted much academic community from the standpoint of scientific research and also practical applications. A series of mathematical approaches emerge in order to troubleshoot the uncertainties of real physical systems. In this context, the work presented here focuses on the application of control theory in a nonlinear dynamical system with parametric variations in order and robustness. We used as the practical application of this work, a system of tanks Quanser associates, in a configuration, whose mathematical model is represented by a second order system with input and output (SISO). The control system is performed by PID controllers, designed by various techniques, aiming to achieve robust performance and stability when subjected to parameter variations. Other controllers are designed with the intention of comparing the performance and robust stability of such systems. The results are obtained and compared from simulations in Matlab-simulink.

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Modeling transport of particulate suspensions in porous media is essential for understanding various processes of industrial and scientific interest. During these processes, particles are retained due to mechanisms like size exclusion (straining), adsorption, sedimentation and diffusion. In this thesis, a mathematical model is proposed and analytical solutions are obtained. The obtained analytic solutions for the proposed model, which takes pore and particle size distributions into account, were applied to predict the particle retention, pore blocking and permeability reduction during dead-end microfiltration in membranes. Various scenarios, considering different particle and pore size distributions were studied. The obtained results showed that pore blocking and permeability reduction are highly influenced by the initial pore and particle size distributions. This feature was observed even when different initial pore and particle size distributions with the same average pore size and injected particle size were considered. Finally, a mathematical model for predicting equivalent permeability in porous media during particle retention (and pore blocking) is proposed and the obtained solutions were applied to study permeability decline in different scenarios

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This paper presents metaheuristic strategies based on the framework of evolutionary algorithms (Genetic and Memetic) with the addition of Technical Vocabulary Building for solving the Problem of Optimizing the Use of Multiple Mobile Units Recovery of Oil (MRO units). Because it is an NP-hard problem, a mathematical model is formulated for the problem, allowing the construction of test instances that are used to validate the evolutionary metaheuristics developed

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This work aims to "build" rostering urban bus crews to minimize the cost of overtime. For this purpose a mathematical model was developed based on case study in an urban transport company in the metropolitan region of Natal. This problem is usually known in the literature as the Crew Scheduling Problem (CSP) and classified as NP-hard. The mathematical programming takes into account constraints such as: completion of all trips, daily and maximum allowable range of home and / or food. We used the Xpress-MP software to implement and validate the proposed model. For the tested instances the application of the model allowed a reduction in overtime from 38% to 84%

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The progressing cavity pump artificial lift system, PCP, is a main lift system used in oil production industry. As this artificial lift application grows the knowledge of it s dynamics behavior, the application of automatic control and the developing of equipment selection design specialist systems are more useful. This work presents tools for dynamic analysis, control technics and a specialist system for selecting lift equipments for this artificial lift technology. The PCP artificial lift system consists of a progressing cavity pump installed downhole in the production tubing edge. The pump consists of two parts, a stator and a rotor, and is set in motion by the rotation of the rotor transmitted through a rod string installed in the tubing. The surface equipment generates and transmits the rotation to the rod string. First, is presented the developing of a complete mathematical dynamic model of PCP system. This model is simplified for use in several conditions, including steady state for sizing PCP equipments, like pump, rod string and drive head. This model is used to implement a computer simulator able to help in system analysis and to operates as a well with a controller and allows testing and developing of control algorithms. The next developing applies control technics to PCP system to optimize pumping velocity to achieve productivity and durability of downhole components. The mathematical model is linearized to apply conventional control technics including observability and controllability of the system and develop design rules for PI controller. Stability conditions are stated for operation point of the system. A fuzzy rule-based control system are developed from a PI controller using a inference machine based on Mandami operators. The fuzzy logic is applied to develop a specialist system that selects PCP equipments too. The developed technics to simulate and the linearized model was used in an actual well where a control system is installed. This control system consists of a pump intake pressure sensor, an industrial controller and a variable speed drive. The PI control was applied and fuzzy controller was applied to optimize simulated and actual well operation and the results was compared. The simulated and actual open loop response was compared to validate simulation. A case study was accomplished to validate equipment selection specialist system

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The traditional processes for treatment of hazardous waste are questionable for it generates other wastes that adversely affect people s health. As an attempt to minimize these problems, it was developed a system for treatment of hazardous waste by thermal plasma, a more appropriate technology since it produces high temperatures, preventing the formation of toxic pollutants to human beings. The present work brings out a solution of automation for this plant. The system has local and remote monitoring resources to ensure the operators security as well as the process itself. A special attention was given to the control of the main reactor temperature of the plant as it is the place where the main processing occurs and because it presents a complex mathematical model. To this, it was employed cascaded controls based on Fuzzy logic. A process computer, with a particular man-machine interface (MMI), provides information and controls of the plant to the operator, including by Internet. A compact PLC module is in charge of the central element of management automation and plant control which receives information from sensors, and sends it to the MMI

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This work presents a proposal for a voltage and frequency control system for a wind power induction generator. It has been developed na experimental structure composes basically by a three phase induction machine, a three phase capacitor and a reactive static Power compensator controlled by histeresys. lt has been developed control algorithms using conventional methods (Pl control) and linguistic methods (using concepts of logic and fuzzy control), to compare their performances in the variable speed generator system. The control loop was projected using the ADJDA PCL 818 model board into a Pentium 200 MHz compu ter. The induction generator mathematical model was studied throught Park transformation. It has been realized simulations in the Pspice@ software, to verify the system characteristics in transient and steady-state situations. The real time control program was developed in C language, possibilish verify the algorithm performance in the 2,2kW didatic experimental system

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The present work describes the use of a mathematical tool to solve problems arising from control theory, including the identification, analysis of the phase portrait and stability, as well as the temporal evolution of the plant s current induction motor. The system identification is an area of mathematical modeling that has as its objective the study of techniques which can determine a dynamic model in representing a real system. The tool used in the identification and analysis of nonlinear dynamical system is the Radial Basis Function (RBF). The process or plant that is used has a mathematical model unknown, but belongs to a particular class that contains an internal dynamics that can be modeled.Will be presented as contributions to the analysis of asymptotic stability of the RBF. The identification using radial basis function is demonstrated through computer simulations from a real data set obtained from the plant

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A hierarchical fuzzy control scheme is applied to improve vibration suppression by using an electro-mechanical system based on the lever principle. The hierarchical intelligent controller consists of a hierarchical fuzzy supervisor, one fuzzy controller and one robust controller. The supervisor combines controllers output signal to generate the control signal that will be applied on the plant. The objective is to improve the performance of the electromechanical system, considering that the supervisor could take advantage of the different techniques based controllers. The robust controller design is based on a linear mathematical model. Genetic algorithms are used on the fuzzy controller and the supervisor tuning, which are based on non-linear mathematical model. In order to attest the efficiency of the hierarchical fuzzy control scheme, digital simulations were employed. Some comparisons involving the optimized hierarchical controller and the non-optimized hierarchical controller will be made to prove the efficiency of the genetic algorithms and the advantages of its use

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The planar circuits are structures that increasingly attracting the attention of researchers, due the good performance and capacity to integrate with other devices, in the prototyping of systems for transmitting and receiving signals in the microwave range. In this context, the study and development of new techniques for analysis of these devices have significantly contributed in the design of structures with excellent performance and high reliability. In this work, the full-wave method based on the concept of electromagnetic waves and the principle of reflection and transmission of waves at an interface, Wave Concept Iterative Procedure (WCIP), or iterative method of waves is described as a tool with high precision study microwave planar circuits. The proposed method is applied to the characterization of planar filters, microstrip antennas and frequency selective surfaces. Prototype devices were built and the experimental results confirmed the proposed mathematical model. The results were also compared with simulated results by Ansoft HFSS, observing a good agreement between them.

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Slugging is a well-known slugging phenomenon in multiphase flow, which may cause problems such as vibration in pipeline and high liquid level in the separator. It can be classified according to the place of its occurrence. The most severe, known as slugging in the riser, occurs in the vertical pipe which feeds the platform. Also known as severe slugging, it is capable of causing severe pressure fluctuations in the flow of the process, excessive vibration, flooding in separator tanks, limited production, nonscheduled stop of production, among other negative aspects that motivated the production of this work . A feasible solution to deal with this problem would be to design an effective method for the removal or reduction of the system, a controller. According to the literature, a conventional PID controller did not produce good results due to the high degree of nonlinearity of the process, fueling the development of advanced control techniques. Among these, the model predictive controller (MPC), where the control action results from the solution of an optimization problem, it is robust, can incorporate physical and /or security constraints. The objective of this work is to apply a non-conventional non-linear model predictive control technique to severe slugging, where the amount of liquid mass in the riser is controlled by the production valve and, indirectly, the oscillation of flow and pressure is suppressed, while looking for environmental and economic benefits. The proposed strategy is based on the use of the model linear approximations and repeatedly solving of a quadratic optimization problem, providing solutions that improve at each iteration. In the event where the convergence of this algorithm is satisfied, the predicted values of the process variables are the same as to those obtained by the original nonlinear model, ensuring that the constraints are satisfied for them along the prediction horizon. A mathematical model recently published in the literature, capable of representing characteristics of severe slugging in a real oil well, is used both for simulation and for the project of the proposed controller, whose performance is compared to a linear MPC

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There is a growing need to develop new tools to help end users in tasks related to the design, monitoring, maintenance and commissioning of critical infrastructures. The complexity of the industrial environment, for example, requires that these tools have flexible features in order to provide valuable data for the designers at the design phases. Furthermore, it is known that industrial processes have stringent requirements for dependability, since failures can cause economic losses, environmental damages and danger to people. The lack of tools that enable the evaluation of faults in critical infrastructures could mitigate these problems. Accordingly, the said work presents developing a framework for analyzing of dependability for critical infrastructures. The proposal allows the modeling of critical infrastructure, mapping its components to a Fault Tree. Then the mathematical model generated is used for dependability analysis of infrastructure, relying on the equipment and its interconnections failures. Finally, typical scenarios of industrial environments are used to validate the proposal

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This work purposes the application of a methodology to optimize the implantation cost of an wind-solar hybrid system for oil pumping. The developed model is estimated the implantation cost of system through Multiple Linear Regression technique, on the basis of the previous knowledge of variables: necessary capacity of storage, total daily energy demand, wind power, module power and module number. These variables are gotten by means of sizing. The considered model not only can be applied to the oil pumping, but also for any other purposes of electric energy generation for conversion of solar, wind or solar-wind energy, that demand short powers. Parametric statistical T-student tests had been used to detect the significant difference in the average of total cost to being considered the diameter of the wind, F by Snedecor in the variance analysis to test if the coefficients of the considered model are significantly different of zero and test not-parametric statistical by Friedman, toverify if there is difference in the system cost, by being considered the photovoltaic module powers. In decision of hypothesis tests was considered a 5%-significant level. The configurations module powers showed significant differences in total cost of investment by considering an electrical motor of 3 HP. The configurations module powers showed significant differences in total cost of investment by considering an electrical motor of 5 HP only to wind speed of 4m/s and 6 m/s in wind of 3 m, 4m and 5 m of diameter. There was not significant difference in costs to diameters of winds of 3 m and 4m. The mathematical model and the computational program may be used to others applications which require electrical between 2.250 W and 3.750 W. A computational program was developed to assist the study of several configurations that optimizes the implantation cost of an wind-solar system through considered mathematical model

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Ceramics with porous cellular structure, called ceramic foams, have a potential use in several applications, such as: thermal insulation, catalyst supports, filters, and others. Among these techniques to obtain porous ceramics the replication method is an important process. This method consists of impregnation of a sponge (usually polymer) with ceramic slurry, followed by a heat treatment, which will happen the decomposition of organic material and sintering the ceramic material, resulting in a ceramic structure which is a replica of impregnated sponge. Knowledge of the mechanical properties of these ceramics is important for these materials can be used commercially. Gibson and Ashby developed a mathematical model to describe the mechanical behavior of cellular solids. This model wasn´t for describing the ceramics behavior produced by the replica method, because it doesn´t consider the defects from this type of processing. In this study were researched mechanical behavior of porous alumina ceramics obtained by the replica method and proposed modifications to the model of Gibson and Ashby to accommodate this material. The polymer sponge used in processing was characterized by thermogravimetric analysis and scanning electron microscopy. The materials obtained after sintering were characterized by mechanical strength tests on 4-point bending and compression, density and porosity and by scanning electron microscopy. From these results it was evaluated the mechanical strength behavior compared to Gibson and Ashby model for solid cellular structure and was proposed a correction of this model through a factor related to struts integrity degree, which consider fissures present in the structure of these materials besides defects geometry within the struts

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The aim of this study is to create an artificial neural network (ANN) capable of modeling the transverse elasticity modulus (E2) of unidirectional composites. To that end, we used a dataset divided into two parts, one for training and the other for ANN testing. Three types of architectures from different networks were developed, one with only two inputs, one with three inputs and the third with mixed architecture combining an ANN with a model developed by Halpin-Tsai. After algorithm training, the results demonstrate that the use of ANNs is quite promising, given that when they were compared with those of the Halpín-Tsai mathematical model, higher correlation coefficient values and lower root mean square values were observed