Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model
Abstract
:1. Introduction
2. Experimental
2.1. Experimental Device and Processes
- (1)
- Multiple sets of samples were cut from Φ50.8 mm × 4.4 mm coiled tubing with dimensions of 20 mm × 20 mm × 2 mm.
- (2)
- Unique identification numbers were assigned to the samples and they were weighed using a balance with a capacity of 100 g and an accuracy of 0.1 mg.
- (3)
- The samples were secured in the fixture and distance and angle were adjusted accordingly.
- (4)
- A 1 wt% KCl aqueous solution was poured into the sand-mixing barrel.
- (5)
- The mortar pump was started and the rotation speed was adjusted.
- (6)
- The 40–70 mesh silica sand was gradually added to achieve a sand concentration of 15–75 kg/m3.
- (7)
- The frequency converter was slowly adjusted to reach the predetermined impact velocity.
- (8)
- After the experiment was completed, the samples were removed, cleaned, and reweighed.
2.2. Experimental Results
3. GRA of Influencing Factors of Coiled Tubing Erosion Rate
- (1)
- Taking the erosion rate as the reference sequence, the impact angle, impact velocity, sand diameter, and sand concentration are used as the comparison sequence.
- (2)
- Perform dimensionless processing.
- (3)
- Calculate the relational coefficient between the comparison sequence xi and the reference sequence x0, as shown in Equation (1), where i is the index of features, i = 1, 2, ..., m, indicating different features; k is the index of samples, k = 1, 2, ..., n, representing distinct samples; [0, 1] is the resolution coefficient, usually 0.5.
- (4)
- Calculate the relational degree of the ith feature, as shown in Equation (2), where n denotes the total number of samples.
4. Methodology
4.1. Basic Principle of BPNN
4.2. Basic Principle of SSA
4.3. SSA-BPNN Model
- (1)
- Determine the structure of the BPNN according to the functional requirements.
- (2)
- The SSA is used to optimize the weight and threshold of the BPNN, and the initial optimal parameters are obtained.
- (3)
- The optimized optimal weights and thresholds are given to the BPNN to keep the network structure unchanged. Then, the optimized network is verified by experimental data.
5. Case Analysis
5.1. Data Selection
5.2. Results Analysis
6. Conclusions
- (1)
- Based on the experimental results, it is evident that the maximum erosion rate of the coiled tubing occurs at a 45° impact angle.
- (2)
- According to the GRA results, the impact velocity, impact angle, and sand concentration of the sand-carrying fluid have a strong influence on the erosion rate of coiled tubing, and the impact velocity has the greatest influence on the erosion rate of coiled tubing.
- (3)
- The SSA-BPNN model is reliable in predicting the erosion rate of coiled tubing, which can provide guidance for coiled tubing hydraulic sand blasting operation.
- (4)
- This method can provide a reference for the erosion rate of coiled tubing. In the future, influence factors such as the defect size and working conditions of coiled tubing can be considered.
- (5)
- The feature selection of the model is limited, because only five influencing parameters are selected as input parameters to predict the erosion rate of coiled tubing. This indicates that the influence of other parameters on the erosion rate may not be considered, which makes the model unable to fully explain the complexity of the erosion rate. In future model optimization, it is recommended to expand the scope of the dataset and consider a wider range of impact parameters.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Impact Velocity (m/s) | Impact Angle (°) | Weight Before (g) | Weight After (g) | Weight Loss (g) | Erosion Rate (mm/h) |
---|---|---|---|---|---|
6.63 | 30 | 9.8391 | 9.8128 | 0.0263 | 0.042 |
6.63 | 45 | 9.7657 | 9.7165 | 0.0492 | 0.078 |
6.63 | 60 | 9.8689 | 9.8132 | 0.0557 | 0.089 |
6.63 | 90 | 9.8403 | 9.7979 | 0.0424 | 0.068 |
13.27 | 30 | 9.8521 | 9.8177 | 0.0344 | 0.055 |
13.27 | 45 | 9.8734 | 9.8208 | 0.0526 | 0.084 |
13.27 | 60 | 9.88 | 9.8185 | 0.0615 | 0.098 |
13.27 | 90 | 9.8573 | 9.8088 | 0.0485 | 0.077 |
19.90 | 30 | 9.8451 | 9.8008 | 0.0443 | 0.071 |
19.90 | 45 | 9.86 | 9.7976 | 0.0624 | 0.099 |
19.90 | 60 | 9.8485 | 9.8056 | 0.0429 | 0.068 |
19.90 | 90 | 9.841 | 9.7978 | 0.0432 | 0.069 |
26.54 | 30 | 9.7894 | 9.7101 | 0.0793 | 0.126 |
26.54 | 45 | 9.8898 | 9.8187 | 0.0711 | 0.113 |
26.54 | 60 | 9.8791 | 9.8137 | 0.0654 | 0.104 |
26.54 | 90 | 9.8601 | 9.8063 | 0.0538 | 0.086 |
Rank of Relational Degree | Influencing Factor | Degree of Association |
---|---|---|
1 | Impact velocity (15° impact angle) | 0.5975 |
2 | Impact velocity (30° impact angle) | 0.6420 |
3 | Impact velocity (45° impact angle) | 0.6290 |
4 | Impact velocity (60° impact angle) | 0.8124 |
5 | Impact velocity (75° impact angle) | 0.6465 |
6 | Impact velocity (90° impact angle) | 0.6989 |
7 | Impact angle (2.4 m/s impact velocity) | 0.6727 |
8 | Impact angle (7.2 m/s impact velocity) | 0.6336 |
9 | Impact angle (12 m/s impact velocity) | 0.7218 |
10 | Impact angle (16.9 m/s impact velocity) | 0.6839 |
11 | Sand concentration | 0.6636 |
12 | Sand particle diameter | 0.1827 |
Parameter | Input Parameter Values |
---|---|
Number of nodes in the hidden layer | 3 |
Transfer function | trainscg |
Training method | SSA-BPNN |
Maximum number of iterations | 1500 |
Learning rate | 0.005 |
Training convergence requirement | 10−4 |
Sample Number | Input Parameters | Output Parameters | ||||
---|---|---|---|---|---|---|
Exhaust Volume (L/min) | Sand Ratio (%) | Impact velocity (m/s) | Sand diameter (mm) | Sand mass flow (kg/s) | Erosion rate (kg/(m2·s)) | |
1 | 100 | 10 | 1.21 | 0.1 | 0.3 | 5.27 × 10−8 |
2 | 200 | 10 | 2.41 | 0.1 | 0.598 | 1.47 × 10−7 |
3 | 250 | 10 | 3.04 | 0.1 | 0.755 | 4.17 × 10−7 |
4 | 300 | 10 | 3.62 | 0.1 | 0.894 | 4.44 × 10−7 |
5 | 350 | 10 | 4.17 | 0.1 | 1.035 | 5.66 × 10−7 |
6 | 400 | 10 | 4.82 | 0.1 | 1.197 | 9.21 × 10−7 |
7 | 450 | 10 | 5.4 | 0.1 | 1.341 | 1.21 × 10−6 |
8 | 250 | 5 | 3.04 | 0.1 | 0.587 | 1.14 × 10−7 |
9 | 300 | 5 | 3.62 | 0.1 | 0.399 | 1.33 × 10−7 |
10 | 250 | 10 | 3.04 | 0.1 | 0.755 | 4.17 × 10−7 |
··· | ··· | ··· | ··· | ··· | ··· | ··· |
55 | 300 | 10 | 3.57 | 0.7 | 4.5 | 7.43 × 10−6 |
56 | 300 | 10 | 3.57 | 0.9 | 4.5 | 8.05 × 10−6 |
57 | 300 | 10 | 3.57 | 0.3 | 4.5 | 5.63 × 10−6 |
58 | 300 | 15 | 3.57 | 0.3 | 6.75 | 8.44 × 10−6 |
59 | 300 | 20 | 3.57 | 0.3 | 9 | 1.21 × 10−5 |
r2 | X2 | RMSE | MBE | MPE | Skew. | Kurt. | Mean Deviation | Standard Deviation | |
---|---|---|---|---|---|---|---|---|---|
SSA-BPNN | 0.998 | 1.76 | 1.25 × 10−6 | 5.83 × 10−7 | 0.1 | 1.648 | 4.971 | 0.056 | 1.25 |
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Cao, Y.; Fang, F.; Wang, G.; Zhu, W.; Hu, Y. Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model. Appl. Sci. 2024, 14, 9519. https://doi.org/10.3390/app14209519
Cao Y, Fang F, Wang G, Zhu W, Hu Y. Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model. Applied Sciences. 2024; 14(20):9519. https://doi.org/10.3390/app14209519
Chicago/Turabian StyleCao, Yinping, Fengying Fang, Guowei Wang, Wenyu Zhu, and Yijie Hu. 2024. "Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model" Applied Sciences 14, no. 20: 9519. https://doi.org/10.3390/app14209519
APA StyleCao, Y., Fang, F., Wang, G., Zhu, W., & Hu, Y. (2024). Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model. Applied Sciences, 14(20), 9519. https://doi.org/10.3390/app14209519