483 research outputs found

    Process Model and Control System for the Glass Fiber Drawing Process

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    Drawing of glass fibers is an important industrial process used for manufacture of a variety of materials ranging from optical communications cables to fiber filter media. A variety of machines exist for performing the drawing function, but all share similar problems with control of the fiber diameters and breakage of the fibers during the extrusion process. In many cases, control systems are not configured to monitor the most critical process variables-- temperature of molten glass in the furnace, but instead use only furnace crown temperature. Upsets in disturbance variables such as ambient temperature are compensated manually by operators, usually only after significant problems with fiber breakage occur. This work seeks to provide better understanding of the effects of important process variables on the key quality and production parameters such as fiber diameter and production rates, and to develop an effective control model to monitor molten glass temperature and winder speed for good production quality even if some disturbance happens.;First an analytical model of the glass fiber based primarily on Glicksman\u27s work was developed, with the addition of a radiative heat transfer component and the addition of temperature-dependent relationships for physical properties of soda-lime glass. The model is valid for fibers in the central attenuation region, where most of fiber attenuation and breakage happens. Parametric studies have been done using the model to evaluate the effects of variation in the ambient temperature and variation of the molten glass depth in the furnace. These studies have shown that even modest changes ambient temperature and molten glass depth can generate significant changes in the final diameter of the glass fibers.;Based on those results, a state space model of the furnace has been constructed and used as the basis of a state reduced-order estimator to provide an accurate estimate of the temperature of the molten glass at the furnace bottom. A LQR controller with a reference input was applied in the model for bottom glass temperature control. A winder speed controller has been developed in parallel in order to compensate for the long time delay between application of burner firing rate changes and the response of the thermal system. Then multivariable control analysis was done on variation of ambient temperature and variation of molten glass depth. The control model manipulates both the winder speed and the burner firing rate, bringing the process back to design conditions even if some disturbance occurs, and allows greater flexibility and more accurate quality control for the glass fiber drawing process

    Contribution to the study and design of advanced controllers : application to smelting furnaces

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    In this doctoral thesis, contributions to the study and design of advanced controllers and their application to metallurgical smelting furnaces are discussed. For this purpose, this kind of plants has been described in detail. The case of study is an Isasmelt plant in south Peru, which yearly processes 1.200.000 tons of copper concentrate. The current control system is implemented on a distributed control system. The main structure includes a cascade strategy to regulate the molten bath temperature. The manipulated variables are the oxygen enriched air and the oil feed rates. The enrichment rate is periodically adjusted by the operator in order to maintain the oxidizing temperature. This control design leads to large temperature deviations in the range between 15ºC and 30ºC from the set point, which causes refractory brick wear and lance damage, and subsequently high production costs. The proposed control structure is addressed to reduce the temperature deviations. The changes emphasize on better regulate the state variables of the thermodynamic equilibrium: the bath temperature within the furnace, the matte grade of molten sulfides (%Cu) and the silica (%SiO2) slag contents. The design is composed of a fuzzy module for adjusting the ratio oxygen/nitrogen and a metallurgical predictor for forecasting the molten composition. The fuzzy controller emulates the best furnace operator by manipulating the oxygen enrichment rate and the oil feed in order to control the bath temperature. The human model is selected taking into account the operator' practical experience in dealing with the furnace temperature (and taking into account good practices from the Australian Institute of Mining and Metallurgy). This structure is complemented by a neural network based predictor, which estimates measured variables of the molten material as copper (%Cu) and silica (%SiO2) contents. In the current method, those variables are calculated after carrying out slag chemistry assays at hourly intervals, therefore long time delays are introduced to the operation. For testing the proposed control structure, the furnace operation has been modeled based on mass and energy balances. This model has been simulated on a Matlab-Simulink platform (previously validated by comparing real and simulated output variables: bath temperature and tip pressure) as a reference to make technical comparisons between the current and the proposed control structure. To systematically evaluate the results of operations, it has been defined some original proposals on behavior indexes that are related to productivity and cost variables. These indexes, complemented with traditional indexes, allow assessing qualitatively the results of the control comparison. Such productivity based indexes complement traditional performance measures and provide fair information about the efficiency of the control system. The main results is that the use of the proposed control structure presents a better performance in regulating the molten bath temperature than using the current system (forecasting of furnace tapping composition is helpful to reach this improvement). The mean square relative error of temperature error is reduced from 0.72% to 0.21% (72%) and the temperature standard deviation from 27.8ºC to 11.1ºC (approx. 60%). The productivity indexes establish a lower consumption of raw materials (13%) and energy (29%).En esta tesis doctoral, se discuten contribuciones al estudio y diseño de controladores avanzados y su aplicación en hornos metalúrgicos de fundición. Para ello, se ha analizado este tipo de plantas en detalle. El caso de estudio es una planta Isasmelt en el sur de Perú, que procesa anualmente 1.200.000 toneladas de concentrado de cobre. El sistema de control actual opera sobre un sistema de control distribuido. La estructura principal incluye una estrategia de cascada para regular la temperatura del baño. Las variables manipuladas son el aire enriquecido con oxígeno y los flujos de alimentación de petróleo. La tasa de enriquecimiento se ajusta perióodicamente por el operador con el fin de mantener la temperatura de oxidación. Este diseño de control produce desviaciones de temperatura en el rango entre 15º C y 30º C con relación al valor de consigna, que causa desgastes del ladrillo refractario y daños a la lanza, lo cual encarece los costos de producción. La estructura de control propuesta esta orientada a reducir las desviaciones de temperatura. Los cambios consisten en mejorar el control de las variables de estado de equilibrio termodinámico: la temperatura del baño en el horno, el grado de mata (%Cu) y el contenido de escoria en la sílice (%SiO2). El diseño incluye un módulo difuso para ajustar la proporción oxígeno/nitrógeno y un predictor metalúrgico para estimar la composición del material fundido. El controlador difuso emula al mejor operador de horno mediante la manipulación de la tasa de enriquecimiento de oxígeno y alimentación con el fin de controlar la temperatura del baño del aceite. El modelo humano es seleccionado teniendo en cuenta la experiencia del operador en el control de la temperatura del horno (y considerando el principio de buenas prácticas del Instituto Australiano de Minería y Metalurgia). Esta estructura se complementa con un predictor basado en redes neuronales, que estima las variables medidas de material fundido como cobre (%Cu) y el contenido de sílice (%SiO2). En el método actual, esas variables se calculan después de ensayos de química de escoria a intervalos por hora, por lo tanto se introducen tiempos de retardo en la operación. Para probar la estructura de control propuesto, la operación del horno ha sido modelada en base a balances de masa y energía. Este modelo se ha simulado en una plataforma de Matlab-Simulink (previamente validada mediante la comparación de variables de salida real y lo simulado: temperatura de baño y presión en la punta de la lanza) como referencia para hacer comparaciones técnicas entre la actual y la estructura de control propuesta. Para evaluar sistemáticamente los resultados de estas operaciones, se han definido algunas propuestas originales sobre indicadores que se relacionan con las variables de productividad y costos. Estos indicadores, complementados con indicadores tradicionales, permite evaluar cualitativamente los resultados de las comparativas de control. Estos indicadores de productividad complementan las medidas de desempeño tradicionales y mejoran la información sobre la eficiencia de control. El resultado principal muestra que la estructura de control propuesta presenta un mejor rendimiento en el control de temperatura de baño fundido que el actual sistema de control. (La estimación de la composición del material fundido es de gran ayuda para alcanzar esta mejora). El error relativo cuadrático medio de la temperatura se reduce de 0,72% al 0,21% (72%) y la desviación estandar de temperatura de 27,8 C a 11,1 C (aprox. 60%). Los indicadores de productividad establecen asimismo un menor consumo de materias primas (13%) y de consumo de energía (29%)

    Preprints / 2nd IFAC Workshop on Computer Software Structures Integrating AI/KBS Systems in Process Control, August 10-12, 1994, Lund, Sweden

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    Advanced modeling for small glass furnaces

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    One of the most pressing issues facing the glass industry is improving energy efficiency. The largest energy user in any glass company is the melting furnace or furnaces. While large float glass and container glass companies have developed sophisticated control systems, little work has been done until recently for small glass furnaces. This thesis extends the work of Holladay (2005), in which an observer was developed to estimate the temperature of glass in a small day-tank furnace. The current work eliminates the assumption of homogeneous glass melt and refractory temperatures, and develops a furnace model suitable for implementation with a real-time controller.;A state space model of an end-fired furnace was developed in which the furnace was divided longitudinally into two zones. Zone 1 contains the burner flame cylinder , while Zone 2 is beyond the end of the flame cylinder. Separate states are identified for the temperatures of the refractory in the crown, the walls above the glass melt, the walls adjacent to the two primary melt zones, and the floor of the furnace. The furnace ends are also divided into similar zones constituting discrete states. The glass melt itself contains a thin, surface layer and two thicker layers of stratification. In all, 24 state variables are included in the model. The inputs are the net thermal power provided by the flame and the ambient temperature.;Simulations were performed in Simulink and Matlab and were used to predict the temperatures of all 24 state variables. The results were verified using data collected from a similar tank furnace at Fenton Art Glass Company. The results showed a significant stratification in the vertical axis of the furnace but very nearly uniform temperatures in the length and width directions. The model was used to study various melting strategies. Preliminary results suggest that using the estimated glass temperature and feedback from thermocouples in the wall and floor of the furnace could lead to significant energy savings in the melt cycle. Suggestions are made for using the model within a real-time control system implementable on a small glass furnace

    Identification and Control of Glass Furnace

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    Identification and Control of Glass Furnace

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    NASA SBIR abstracts of 1991 phase 1 projects

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    The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

    PI and Fuzzy Control for P-removal in Wastewater Treatment Plant

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    Due to the complex and non linear character, wastewater treatment process is difficult to be controlled. The demand for removing the pollutant, especially for nitrogen (N) and phosphorus (P), as well as reducing the cost of wastewater treatment plant is an important research theme recently. Thus, in this paper, the benchmark proposed default control strategy and 10 additional control strategies are applied on the combined biological P and N removal Benchmark Simulation Model No.1 (BSM1-P). In addition, according to the results of applying PI controllers, as usual, we also chose the group with the better performance, as well as the default control strategy, to replace the PI controllers with fuzzy controllers. In this way, it can be seen that in all cases the quality of effluent of the controlled process could be improved in some degree; and the fuzzy controllers get a better phosphorus removal
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