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Improvement of different methods of predicting the vapor pressure of different materials

Number of pages: 96 File Format: word File Code: 31750
Year: 2013 University Degree: Master's degree Category: Chemical - Petrochemical Engineering
Tags/Keywords: Aromatic hydrocarbons - Artificial neural networks - liquid - Liquid temperature - Modeling - Smart ways - Thermodynamic properties - vapor pressure
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  • Summary of Improvement of different methods of predicting the vapor pressure of different materials

    Dissertation

    Master's degree

    Department: Chemical Engineering

    Abstract

    Vapor pressure is an important thermodynamic property in the design of process equipment and the operation of a chemical engineering unit. Therefore, experimental vapor pressure data that cover the entire range of vapor pressure are very valuable, but due to the lack of accurate measurements for the vapor pressure of some materials near the triple and critical point, equations that are able to predict the vapor pressure in such conditions are very important. in chemical engineering calculations, the calculation method is based on neural networks. In this research, a model was presented for predicting the vapor pressure of different substances using artificial neural networks. In this work, four groups of substances were used, including aromatic hydrocarbons, alkanes and alkenes, alcohols, and alkylcyclohexanes. The most suitable type of artificial neural network for predicting the vapor pressure of these substances is a three-layer feedforward network with backpropagation algorithm, in which the function The sigmoid tangent transfer is used in the hidden layer and the linear transfer function is used in the output layer. The input parameters of the network are: temperature, critical temperature, critical pressure and eccentricity coefficient. The data required for training and testing the network were collected from valid laboratory values. The error rate of the neural network method was compared with the error values ??obtained from different vapor pressure estimation methods. The simulation results show that the neural network method has been able to provide an accurate prediction of the vapor pressure of materials and is more accurate than other methods. rtl;"> Vapor pressure is an important thermodynamic property in many different chemical engineering processes such as chemical equilibrium, distillation, evaporation and the like. Determining this characteristic can lead to the calculation of other important characteristics such as enthalpy of evaporation. In the oil and gas industry, the determination of steam pressure is of particular importance. In this area, steam pressure is dealt with in two main cases; One of these cases is the steam pressure in the tanks. One of the important methods for dividing the types of fluid storage tanks in the oil and gas industry is their division based on the vapor pressure of the desired fluid. For each range of material vapor pressure (low pressure, medium pressure, high pressure) special types of tanks are used. For example, for fluids with low vapor pressure, fixed roof tanks are used. For fluids with medium vapor pressure, floating roof tanks are used.

    The vapor pressure of liquid products is another thing that is measured in the industry. One of the indirect ways of measuring the rate of evaporation of volatile petroleum solvents is their vapor pressure. Refinery products must also have special specifications and meet standards in order to be able to be present and sell well in global markets. Accordingly, the amount of steam pressure is one of the most important features, which, in addition to quality and price, is also very important from the point of view of safety during transportation and storage, and is always tested and controlled. Therefore, the importance of accurately determining the vapor pressure of fluids in the field of oil industry is not hidden. When the moment of equilibrium is approached, the number of vaporized molecules equals the number of condensed molecules. In fact, the rate of evaporation is equal to the rate of condensation, and the specified pressure in this case is the vapor pressure of that liquid at that temperature.] 1 [

    1-3-Factors affecting vapor pressure

    In general, the vapor pressure of a liquid depends on the following two factors:

    Nature and nature of liquid

    Liquid temperature

    1-3-1- Nature of liquid

    Liquids that have weak intermolecular forces are more volatile and have a higher vapor pressure. For example, the vapor pressure of ethyl alcohol is higher than the vapor pressure of water.

    1-3-2-liquid temperature

    The vapor pressure increases with increasing temperature. This problem is due to the fact that with the increase in temperature, the rate of evaporation also increases. 1-4- Statement of the problem The vapor pressure of various substances is one of the properties required to perform chemical engineering calculations, such as equilibrium calculations and chemical engineering unit operations, on which many researches are being conducted. The most common methods for determining vapor pressure include laboratory measurements, equations of state, empirical relationships, and relationships based on the law of corresponding states. The necessity of calculating vapor pressure using mathematical relationships increases when laboratory data is not available. Since the conditions of many chemical processes are such that it is not practically possible to determine the vapor pressure of materials experimentally and its measurement at certain pressures and temperatures is difficult and the obtained values ??are not very reliable, therefore providing models and methods for predicting vapor pressure will have a major contribution in determining this thermodynamic property. Therefore, numerous methods have been presented to predict this characteristic, and newer methods are introduced or old methods are corrected every year. ] 5-2 [

     

    1-5-Justification of the necessity of conducting research

    Since many experimental relationships or relationships of corresponding states have limitations in determining the vapor pressure and cannot be used to determine the vapor pressure in the entire required temperature range and do not have acceptable accuracy, therefore the use of new methods that are far from these limitations It is recommended. One of the modeling methods that has attracted the attention of many researchers in various sciences in recent years is artificial neural network modeling. Artificial neural networks are used as a subset of artificial intelligence methods, with a structure and function similar to the human brain in a wide range to solve many problems including evaluation, optimization, prediction, diagnosis and control. [6] One of the advantages of using artificial neural networks compared to old models is that it does not require determining a specific function to express the relationship between input and output data.  The relationship between the input and output data is obtained through the training process. ] 7 [

     

    1-6-Research objectives

    Study different methods to calculate vapor pressure

    Investigate the level of accuracy and how to apply existing methods to calculate vapor pressure of different materials

    Improve vapor pressure prediction methods with Applying the new method of the neural network and comparing it with the old methods

    Evaluating the results of modeling by means of the neural network with experimental data and checking its accuracy

    1-7-Steps of conducting the research

    In the first part of this thesis, different methods for calculating the vapor pressure were comprehensively and completely investigated and the parameters of each relationship, the accuracy of the estimation of the vapor pressure And the optimal temperature range of each of the methods was mentioned.

    In the second part, the neural network method was introduced and reviewed as an accurate calculation method for predicting the vapor pressure of materials. In this stage of the thesis, the artificial neural network model was investigated as a research method and a comprehensive explanation of this method and its applications and features and how to apply this method to predict the vapor pressure of materials was presented. In the third part of the thesis, the new neural network method was applied to more accurately predict the vapor pressure of several groups of materials, and the optimal neural network was designed and the results of modeling by the artificial neural network method for different groups of materials were presented.

  • Contents & References of Improvement of different methods of predicting the vapor pressure of different materials

    List:

    Chapter One: Introduction to research principles. 1

    1-1-Introduction. 2

    1-2-Definition of vapor pressure 2

       1-3-Effective factors of vapor avalanche 3

    1-3-1-Liquid nature. 3

    1-3-2-liquid temperature. 3

    1-4- statement of the problem. 3

    1-5- Justification of the necessity of conducting research. 4

    1-6-Research objectives. 4

    1-7-Steps of conducting research. 4

    1-8- Research structure. 5

    Chapter Two: Literature and research background. 7

    2-1-Introduction. 8

    2-2-Mathematical relations of estimation and prediction of vapor pressure of different materials. 9

    2-2-1- Clausius-Clapyron equation. 9

    2-2-2- Antoine's equation. 10

    2-2-2-1-Limitations of Antoine's equation. 10

    2-2-3- developed Antoine's equation. 10

    2-2-4-Wagner's equation. 11

    2-2-4-1-Limitations of Wagner's equation. 12

    2-2-5-Relation of Riddle's corresponding states. 12

    2-2-6-Lee-Kessler equation. 14

    2-2-6-1-Limitations of the Lee-Kessler relationship. 15

    2-2-7-Ambrose-Patel vapor pressure equation. 15

    2-2-7-1-Notes on the Ambrose-Patel equation. 16

    2-2-8-Ambrose-Walton method of corresponding states. 16

    2-3-The importance of new methods of predicting and estimating material properties. 17

    2-4-The background of the neural network method in estimating thermodynamic properties. 18

    2-5-prediction of vapor pressure of materials using artificial neural network. 19

    The third chapter: research method. 21

    3-1-Introduction. 22

    3-2-The history of artificial neural networks. 22

    3-3-Characteristics of artificial neural networks. 24

    3-3-1- Ability to teach. 24

    3-3-2- Generalization ability. 24

    3-3-3- Distributed (parallel) processing 24

    3-3-4-Fault tolerance 25

    3-4-Structure of artificial neural networks. 25

    3-4-1-Neron model with one input. 25

    3-4-2- Neuron model with a vector as input. 26

    3-4-3-The structure of a layer of neural networks. 27

    3-4-4-multilayer networks. 27

    3-4-5-transfer functions. 28

    3-4-5-1-limited hard transfer function. 29

    3-4-5-2-linear transfer function. 29

    3-4-5-3-sigmoid logarithmic transfer function. 30

    3-4-5-4-base radius transfer function 30

    3-4-5-5-symmetric linear threshold transfer function. 31

    3-4-5-6-tangent-sigmoid transfer function. 31

    3-5-Neural network training methods. 32

    3-6-Learning rules of neural networks. 32

    3-6-1-rules of supervised learning 32

    3-6-2-rules of unsupervised learning. 33

    3-7- Perceptron neural networks. 33

    3-7-1-Perceptron network limitations. 34

    3-8-Feedback neural networks 35

    3-9-Error backpropagation algorithm 36

    3-10-Training of backpropagation networks 37

    3-11-Network overfitting. 37

    3-12-Improving the generality of the network. 38

    3-13-basic parameters for designing a neural network. 39

    3-13-1-Choosing the most appropriate input information to the network. 39

    3-13-2-how to enter data 39

    3-13-3-data division 39

    3-13-4-choosing the most appropriate number of hidden layer neurons. 40

    3-12-Evaluation criteria of model efficiency. 40

    3-12-Software used in this research. 41

    Chapter 4: calculations and research findings. 42

    4-1-Introduction. 43

    4-2- Designing artificial neural network for aromatic hydrocarbons. 43

    4-3- Artificial neural network design for alkanes and alkenes 52

    4-4- Artificial neural network design for alcohols.

    4-5- Artificial neural network design for alkylcyclohexanes 68

    Chapter five: Conclusion and suggestions 77

    5-1-Conclusion. 78

    2-5-Suggestions for future research. 79

    References. 80

    English abstract. 86

     

    Source:

     

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    [3] H. An, W. Yang, A new generalized correlation for accurate vapor pressure prediction, Chemical Physics Letters, vol. 543, 2012, pp. 188–192. [4] L.A. Forero, J.A. Velasquez, Wagner liquid-vapor pressure equation constants from a. Velasquez, Wagner liquid–vapor pressure equation constants from a simple methodology, The Journal of Chemical Thermodynamics, vol. 43, 2011, pp. 1235–1251.

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    [6] D. Richon, S. Laugier, Use of artificial neural networks for calculating derived thermodynamic quantities from volumetric property data, Fluid phase equilibria, vol. 210, 2003, pp. 247-255. [7] M.G. Schaap, W. Bouten, Modeling water retention curves of sandy soils using neural networks, Water Resources Research, vol. 32, 1996, pp. 3033-3040.

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