Contents & References of Prediction of corrosion rate and wear rate constant in gas cylinder tubes using neural network
List:
Chapter One: Introduction. 9
1-1 Introduction of the entire research. 9
1-2 previous activities and research history. 11
1-3 research objectives. 17
Chapter Two: Neural Networks. 18
2-1 Single neuron modeling 19
2-2 Activity function. 20
2-3 neural network architecture. 21
2-3-1 feed networks 22
2-3-2 return networks. 22 2-4 learning algorithms. 23
2-5 MLP neural network. 24
2-5-1 error back propagation algorithm 25
2-5-2 error signal 26
2-5-3 learning rate selection. 26
2-5-4 training stage. 27
2-5-5 Generalization ability. 27
2-5-6 Stop training. 28
2-6 RBF network. 29
2-6-1 Radial neural network structure. 30
2-6-2-1 Determining the location of centers. 35
2-6-2-2 Determination of standard deviation. 37
2-6-2-3 Teaching the weight matrix of the output layer. 38
The third chapter: fuzzy logic. 40
3-1 introductions to fuzzy systems. 40
3-2 basic components of fuzzy inference system (FIS) 45
3-2-1 fuzzy rule base. 45
3-2-1-1 Characteristics of the set of rules. 45
3-2-2 Fuzzy inference engine. 47
3-2-2-1 Inference based on the combination of rules. 47
3-3 Non-fuzzifier 49
3-3-1 Non-fuzzifier of center of gravity. 49
3-3-2 Average center de-fuzzifier. 49
3-3-3 Maximum non-fuzzifier. 50
Chapter Four: Adaptive Neural-Fuzzy Inference Systems (ANFIS)). 52
Chapter Five: Corrosion. 54
5-1 An introduction to corrosion. 54
5-1-1 Corrosion costs. 56
5-1-2 Investigation of types of corrosion. 57
5-2 Design of organic anti-corrosion systems. 68
5-3 Corrosion in oil and gas facilities 70
5-3-1 Corrosion by corrosive carbon dioxide gas. 71
5-3-2 Corrosion by corrosive liquids of oil tanks. 73
5-3-3 Corrosion by hydrogen sulphide corrosive gas. 73
5-4 Corrosion in three-phase systems of wells and gas pipes and its control methods. 77
5-4-1 Corrosion control methods. 77
5-4-1-1 Corrosion inhibitors. 78
5-3-1-2 pH stabilization method. 82
Sixth chapter: Wear phenomenon in hydrocarbon production systems. 88
6-1 Wear process in oil and gas production wells 89
6-2 Wear mechanisms. 90
6-2-3 Vulnerability of equipment against the phenomenon of wear: 90
6-3-2-1 Material of equipment. 92
6-3-2-2 conductive metals and other conventional materials. 92
6-3-2-3 Special wear-resistant materials. 93
6-4 Abrasion caused by sand or fine particles. 94
6-4-1 Sand production and transportation. 94
6-4-2 Size, shape and hardness of solid particles. 96
6-5 wear/corrosion. 97
6-6 Wear caused by hitting liquid drops. 98
6-7 cavitation. 100
6-8 Wear due to solid particles in elbows 101
6-9 Wear of solid particles in closed T-shaped joints. 103
6-10 methods of monitoring, preventing and managing wear phenomena. 104
6-10-1 wear management techniques. 105
6-10-1-1 Reducing production flow. 105
6-10-1-2 pipeline design. 105
6-10-1-3 Separation and removal of sand from the flow. 106
6-10-1-4 instructions and prediction of wear. 107
6-10-1-5 Wall thickness assessment. 109
6-11 wear prediction tools and a review of the research done. 110
6-11-1 Review of the most important standards in pipeline design and wear management. 110
6-11-2 Wear prediction tools and models. 111
6-11-2-1 API standard RP 14E. 112
6-11-2-2 Other wear prediction models. 117
6-11-3 Comparison of knee wear prediction models 124
Seventh chapter: research method. 131
7-1 Prediction of corrosion rate. 134
7-1-1 Corrosion rate prediction using neural network. 134
7-1-2 Prediction of corrosion rate using ANFIS. 141
7-2 constant prediction of wear rate. 151
Chapter Eight: Conclusion. 158
Chapter 9: Suggestions. 159
Resources 160
Source:
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