Contents & References of Non-linear identification of de-butanizer separator tower system located in a gas refinery in South Pars using experimental data.
List:
Chapter 1: Getting to know the debutanizer separator tower, description of input and output, data collection
1
1-1- Introduction
1
1-2- Separation process and looking back
1-3- Review of past works
3
4
1-4- Description of a separator tower
6
1-5- Debutanizer tower in South Pars Gas Refinery
8
Chapter Two: Identification by linear and non-linear methods
2-1- Introduction
11
11
2-2- Identification by linear methods
11
2-2-1- Linear identification by parametric method
11
2-2-1-1- Identification by ARX method
12
2-2-1-2- Identification by OE method
14
2-2-1-3- Identification by BJ method
15
2-2-2- Linear identification based on subspace analysis
16
2-3- Identification by non-linear methods
20
2-3-1- Identification by non-linear ARX (NLARX) method
20
2-3-2- Identification by method Hammerstein-Wiener (NLHW)
21
2-3-3- Recognition by Neural Networks
23
(MLP) 2-3-3-1- Multilayer Perceptron
23
2-3-3-2- Learning by Lunberg-Marquardt
25
2-3-4- identification by fuzzy-neural method
26
2-3-4-1- classification or clustering
27
2-3-4-2- subtractive classification
28
2-3-4-3- membership function
29
2-4- aggregation Classification
Title
30
Page
Chapter 3: Implementation of linear and non-linear identification methods on dibutanizer system
31
3-1- Introduction
31
3-2- Collecting data to identify the dynamic system of dibutanizer
3-2-1- Variables Physical and precision instruments
32
32
3-2-2- Sampling and charting the variables of de-butanizer tower
34
3-3- Implementation of ARX identification method
37
3-4- Implementation of OE identification method
39
3-5- Implementation of identification method BJ
41
3-6- Implementation of N4SID identification method
44
3-7- Implementation of NLARX identification method
47
3-8- Implementation of NLHW identification method
48
3-9- Implementation of neural network identification method
50
3-10-Implementation of recognition by neuro-fuzzy method
3-11- Summary
53
56
Chapter four: Expanded neuro-fuzzy method
4-1-Introduction
57
57
4-2- Type fuzzy systems- 2
57
4-3- Categorization by reduction method, fuzzy type-2
58
4-4- Determining the neighborhood radius of mutual influence of membership functions
61
4-5- Implementation of identification by extended fuzzy-neural method
4-6- Summary
65
68
Chapter five: discussion, conclusions and suggestions
69
Resources
71
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