Ambiente de simulação híbrido integrando hysys e rede industrial Foundation Fieldbus aplicado ao controle de uma coluna de destilação

The main purpose of this work is to develop an environment that allows HYSYS R chemical process simulator communication with sensors and actuators from a Foundation Fieldbus industrial network. The environment is considered a hybrid resource since it has a real portion (industrial network) and a simulated one (process) with all measurement and control signals also real. It is possible to reproduce different industrial process dynamics without being required any physical network modification, enabling simulation of some situations that exist in a real industrial environment. This feature testifies the environment flexibility. In this work, a distillation column is simulated through HYSYS R with all its variables measured and controlled by Foundation Fieldbus devices

Application of multivariable control using artificial neural networks in a debutanizer distillation column

LOPES, Jose Soares Batista et al. Application of multivariable control using artificial neural networks in a debutanizer distillation column.In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING - COBEM, 19, 5-9 nov. 2007, Brasilia. Anais... Brasilia, 2007

Um sistema para a inferência de variáveis usando redes neurais artificiais

This work aims to obtain a low-cost virtual sensor to estimate the quality of LPG. For the acquisition of data from a distillation tower, software HYSYS ® was used to simulate chemical processes. These data will be used for training and validation of an Artificial Neural Network (ANN). This network will aim to estimate from available simulated variables such as temperature, pressure and discharge flow of a distillation tower, the mole fraction of pentane present in LPG. Thus, allowing a better control of product quality

Sistema Híbrido de Inferência Baseado em Análise de Componentes Principais e Redes Neurais Artificiais Aplicado a Plantas de Processamento de Gás Natural

Nowadays, where the market competition requires products with better quality and a constant search for cost savings and a better use of raw materials, the research for more efficient control strategies becomes vital. In Natural Gas Processin Units (NGPUs), as in the most chemical processes, the quality control is accomplished through their products composition. However, the chemical composition analysis has a long measurement time, even when performed by instruments such as gas chromatographs. This fact hinders the development of control strategies to provide a better process yield. The natural gas processing is one of the most important activities in the petroleum industry. The main economic product of a NGPU is the liquefied petroleum gas (LPG). The LPG is ideally composed by propane and butane, however, in practice, its composition has some contaminants, such as ethane and pentane. In this work is proposed an inferential system using neural networks to estimate the ethane and pentane mole fractions in LPG and the propane mole fraction in residual gas. The goal is to provide the values of these estimated variables in every minute using a single multilayer neural network, making it possibly to apply inferential control techniques in order to monitor the LPG quality and to reduce the propane loss in the process. To develop this work a NGPU was simulated in HYSYS R software, composed by two distillation collumns: deethanizer and debutanizer. The inference is performed through the process variables of the PID controllers present in the instrumentation of these columns. To reduce the complexity of the inferential neural network is used the statistical technique of principal component analysis to decrease the number of network inputs, thus forming a hybrid inferential system. It is also proposed in this work a simple strategy to correct the inferential system in real-time, based on measurements of the chromatographs which may exist in process under study

Simulação da destilação molecular de filme descendente para o petróleo

The heavy part of the oil can be used for numerous purposes, e.g. to obtain lubricating oils. In this context, many researchers have been studying alternatives such separation of crude oil components, among which may be mentioned molecular distillation. Molecular distillation is a forced evaporation technique different from other conventional processes in the literature. This process can be classified as a special distillation case under high vacuum with pressures that reach extremely low ranges of the order of 0.1 Pascal. The evaporation and condensation surfaces must have a distance from each other of the magnitude order of mean free path of the evaporated molecules, that is, molecules evaporated easily reach the condenser, because they find a route without obstacles, what is desirable. Thus, the main contribution of this work is the simulation of the falling-film molecular distillation for crude oil mixtures. The crude oil was characterized using UniSim® Design and R430 Aspen HYSYS® V8.5. The results of this characterization were performed in spreadsheets of Microsoft® Excel®, calculations of the physicochemical properties of the waste of an oil sample, i.e., thermodynamic and transport. Based on this estimated properties and boundary conditions suggested by the literature, equations of temperature and concentration profiles were resolved through the implicit finite difference method using the programming language Visual Basic® (VBA) for Excel®. The result of the temperature profile showed consistent with the reproduced by literature, having in their initial values a slight distortion as a result of the nature of the studied oil is lighter than the literature, since the results of the concentration profiles were effective allowing realize that the concentration of the more volatile decreases and of the less volatile increases due to the length of the evaporator. According to the transport phenomena present in the process, the velocity profile tends to increase to a peak and then decreases, and the film thickness decreases, both as a function of the evaporator length. It is concluded that the simulation code in Visual Basic® language (VBA) is a final product of the work that allows application to molecular distillation of petroleum and other similar mixtures.

Application of multivariable control using artificial neural networks in a debutanizer distillation column

LOPES, Jose Soares Batista et al. Application of multivariable control using artificial neural networks in a debutanizer distillation column.In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING - COBEM, 19, 5-9 nov. 2007, Brasilia. Anais... Brasilia, 2007

Um sistema para a inferência de variáveis usando redes neurais artificiais

This work aims to obtain a low-cost virtual sensor to estimate the quality of LPG. For the acquisition of data from a distillation tower, software HYSYS ® was used to simulate chemical processes. These data will be used for training and validation of an Artificial Neural Network (ANN). This network will aim to estimate from available simulated variables such as temperature, pressure and discharge flow of a distillation tower, the mole fraction of pentane present in LPG. Thus, allowing a better control of product quality