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Application of AI Technologies in Pipeline Health Monitoring and Energy Prediction

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (7 February 2023) | Viewed by 15918

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Special Issue Editors

Dr. Xin Ma

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Guest Editor

School of Science, Southwest University of Science and Technology, Mianyang 621010, China
Interests: grey system; machine learning; energy prediction
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaoben Liu

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Guest Editor

Department of Oil & Gas Storage and Transportation Engineering, China University of Petroleum-Beijing, Beijing 102249, China
Interests: pipe mechanics; integrity assessment; ILI-based pipeline health inspection; real-time pipeline geohazard monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Pei Du

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Guest Editor

School of Management, Xi’an Jiaotong University, Xi’an 710049, China
Interests: artificial intelligence; optimization algorithms; machine learning; time series forecasting
Special Issues, Collections and Topics in MDPI journals

Dr. Jingwei Cheng

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Guest Editor

National Safety Engineering Technology Research Center for Pressure Vessels and Pipeline, Hefei General Machinery Research Institute Co., Ltd., Hefei 230031, China
Interests: structural health monitoring; ultrasonic phased array imaging

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has achieved remarkable success in a wide variety of fields in the last few years. With the tremendous number of innovations achieved using AI, it has become apparent that AI indeed has the potential to make a difference in our fields. For pipeline health monitoring, AI has been widely employed in the residual life prediction of defected pipes, e.g., dented or corroded pipes. AI has also been applied in multi-source health monitoring of pipe structures, especially those located in geohazard regions. For energy engineering, AI makes it easier to deal with high-dimensional features, complex scenarios, large-scale datasets, etc. With the growing demand for cleaner and renewable energy, we believe that AI techniques will help to provide more reliable support for new energy production, management, and marketing. Above all, investigations on new methods or new applications within these topics will be carried out in Energies.

Dr. Xin Ma
Dr. Xiaoben Liu
Dr. Pei Du
Dr. Jingwei Cheng
Guest Editors

Manuscript Submission Information

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Keywords

Artificial neural networks/integrity assessment/structural health inspection or monitoring
Machine learning/forecast method/renewable energy
Energy demand and production prediction by AI
AI support for energy management and policy decision
Machine learning for energy prediction
Evolutionary algorithms for energy prediction and optimization
Nondestructive testing/structural health monitoring
Ultrasonics/nonlinear ultrasonics
Phased array testing/imaging technique

Published Papers (9 papers)

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Research

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22 pages, 6324 KiB

Open AccessArticle

High-Resolution Load Forecasting on Multiple Time Scales Using Long Short-Term Memory and Support Vector Machine

by Sizhe Zhang, Jinqi Liu and Jihong Wang

Energies 2023, 16(4), 1806; https://0-doi-org.brum.beds.ac.uk/10.3390/en16041806 - 11 Feb 2023

Cited by 1 | Viewed by 1290

Abstract

Electricity load prediction is an essential tool for power system planning, operation and management. The critical information it provides can be used by energy providers to maximise power system operation efficiency and minimise system operation costs. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are two suitable methods that have been successfully used for analysing time series problems. In this paper, the two algorithms are explored further for load prediction; two load prediction algorithms are developed and verified by using the half-hourly load data from the University of Warwick campus energy centre with four different prediction time horizons. The novelty lies in comparing and analysing the prediction accuracy of two intelligent algorithms with multiple time scales and in exploring better scenarios for their prediction applications. High-resolution load forecasting over a long range of time is also conducted in this paper. The MAPE values for the LSTM are 2.501%, 3.577%, 25.073% and 69.947% for four prediction time horizons delineated. For the SVM, the MAPE values are 2.531%, 5.039%, 7.819% and 10.841%, respectively. It is found that both methods are suitable for shorter time horizon predictions. The results show that LSTM is more capable of ultra-short and short-term forecasting, while SVM has a higher prediction accuracy in medium-term and long-term forecasts. Further investigation is performed via blind tests and the test results are consistent. Full article

(This article belongs to the Special Issue Application of AI Technologies in Pipeline Health Monitoring and Energy Prediction)

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20 pages, 8527 KiB

Open AccessArticle

A Real-Time Method to Estimate the Operational Condition of Distribution Transformers

by Leandro José Duarte, Alan Petrônio Pinheiro and Daniel Oliveira Ferreira

Energies 2022, 15(22), 8716; https://0-doi-org.brum.beds.ac.uk/10.3390/en15228716 - 20 Nov 2022

Cited by 2 | Viewed by 1298

Abstract

In this article, an unsupervised learning method is presented with the objective of modeling, in real-time, the main operating modes (OM) of distribution transformers. This model is then used to assess the operational condition through use of two tools: the operation map and the health index. This approach allows, mainly, for a reduction in the need for the interpretation of results by specialists. The method used the concepts of k-nearest neighbors (k-NN) and Gaussian mixture model (GMM) clustering to identify and update the main OMs and characterize these through operating mode clusters (OMC). The evaluation of the method was performed using data from a case study of almost one year in duration, along with five in-service distribution transformers. The model was able to synthesize 11 magnitudes measured directly in the transformer into two latent variables using the principal component analysis technique, while preserving on average more than 86% of the information present. The operation map was able to categorize the transformer operation into previously parameterized levels (appropriate, precarious, critical) with errors below 0.26 of standard deviation. In addition, the health index opened the possibility of identifying and quantifying the main abnormal variations in the operating pattern of the transformers. Full article

(This article belongs to the Special Issue Application of AI Technologies in Pipeline Health Monitoring and Energy Prediction)

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16 pages, 6497 KiB

Open AccessArticle

Automated Classification of Pipeline Defects from Ultrasonic Phased Array Total Focusing Method Imaging

by Haibin Wang, Zhichao Fan, Xuedong Chen, Jingwei Cheng, Wei Chen, Zhe Wang and Yangguang Bu

Energies 2022, 15(21), 8272; https://0-doi-org.brum.beds.ac.uk/10.3390/en15218272 - 05 Nov 2022

Cited by 3 | Viewed by 1462

Abstract

The defects in the welds of energy pipelines have significantly influenced their safe operation. The inefficient and inaccurate detection of the defects may give rise to catastrophic accidents. Ultrasonic phased array inspection is an important means of ensuring pipeline safety. The total focusing method (TFM), using ultrasonic phased arrays, has become widely used in recent years in non-destructive evaluation (NDE). However, manual defect recognition of TFM images is seen to lack accuracy and robustness, arising from deficiency of practical experience. In this paper, the automated classification of different defects from TFM images is studied with a view to facilitate inspection efficacy. By experimentally implementing the TFM approach on a bespoke specimen, the images corresponding to crack-like defects and pore-like defects were employed to investigate the effectiveness of four different machine learning models (known as Support Vector Machine, CART Decision tree, K Nearest Neighbors, Naive Bayes) containing data augmentation, feature extraction and defect classification. The results suggested that the accuracy of defect classification using the HOG-Poly-SVM algorithm was 93%, which outperformed the results from other algorithms. The advantage of the HOG-Poly-SVM algorithm used in defect classification of ultrasonic phased array TFM data is discussed by conducting ten-fold cross validation and other evaluation metrics. In this paper, in order to improve the efficiency of detecting pipeline defects in the future, and for testing test blocks simulating buried pipelines containing defects, we proposed, for the first time, that ultrasonic phased-array TFM imaging results in small object detection images, and found that the SVM algorithm was applicable to ultrasonic phased array TFM imaging, providing a research method and ideas for the use of artificial intelligence in industrial non-destructive testing. Full article

(This article belongs to the Special Issue Application of AI Technologies in Pipeline Health Monitoring and Energy Prediction)

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26 pages, 1982 KiB

Open AccessArticle

Multi-Step Ahead Natural Gas Consumption Forecasting Based on a Hybrid Model: Case Studies in The Netherlands and the United Kingdom

by Longfeng Zhang, Xin Ma, Hui Zhang, Gaoxun Zhang and Peng Zhang

Energies 2022, 15(19), 7437; https://0-doi-org.brum.beds.ac.uk/10.3390/en15197437 - 10 Oct 2022

Cited by 1 | Viewed by 1372

Abstract

With worldwide activities of carbon neutrality, clean energy is playing an important role these days. Natural gas (NG) is one of the most efficient clean energies with less harmful emissions and abundant reservoirs. This work aims at developing a swarm intelligence-based tool for NG forecasting to make more convincing projections of future energy consumption, combining Extreme Gradient Boosting (XGBoost) and the Salp Swarm Algorithm (SSA). The XGBoost is used as the core model in a nonlinear auto-regression procedure to make multi-step ahead forecasting. A cross-validation scheme is adopted to build a nonlinear programming problem for optimizing the most sensitive hyperparameters of the XGBoost, and then the nonlinear optimization is solved by the SSA. Case studies of forecasting the Natural gas consumption (NGC) in the United Kingdom (UK) and Netherlands are presented to illustrate the performance of the proposed hybrid model in comparison with five other intelligence optimization algorithms and two other decision tree-based models (15 hybrid schemes in total) in 6 subcases with different forecasting steps and time lags. The results show that the SSA outperforms the other 5 algorithms in searching the optimal parameters of XGBoost and the hybrid model outperforms all the other 15 hybrid models in all the subcases with average MAPE 4.9828% in NGC forecasting of UK and 9.0547% in NGC forecasting of Netherlands, respectively. Detailed analysis of the performance and properties of the proposed model is also summarized in this work, which indicates it has high potential in NGC forecasting and can be expected to be used in a wider range of applications in the future. Full article

(This article belongs to the Special Issue Application of AI Technologies in Pipeline Health Monitoring and Energy Prediction)

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18 pages, 9025 KiB

Open AccessArticle

Numerical Simulation of Erosion Characteristics and Residual Life Prediction of Defective Pipelines Based on Extreme Learning Machine

by Qi Wang, Chao Sun, Yuelin Li and Yuechan Liu

Energies 2022, 15(10), 3750; https://0-doi-org.brum.beds.ac.uk/10.3390/en15103750 - 19 May 2022

Viewed by 1187

Abstract

Aiming to solve the problem that the residual life of defective elbows is difficult to predict and the prediction accuracy of a traditional extreme learning machine (ELM) is unsatisfactory, a genetic algorithm optimization neural network extreme learning machine method (GA-ELM) that can effectively predict erosion rate and residual life is proposed. In this method, the input weight and hidden layer node threshold of the hidden layer node is mapped to GA, and the input weight and threshold of the ELM network error is selected by GA, which improves the generalization performance of the ELM. Firstly, the effects of solid particle velocity, particle size, and mass flow rate on the erosion of elbow are studied, and the erosion rates under the conditions of point erosion defect, groove defect, and double groove erosion defect are calculated. On this basis, the optimized GA-ELM network model is used to predict the residual life of the pipelines and then compared with the traditional ELM network model. The results show that the maximum erosion rate of defect free elbow is linearly correlated with solid particle velocity, particle size, and mass flow rate; The maximum erosion rate of defective bend is higher than that of nondefective bends, and the maximum erosion rate of defective bend is linearly related to mass flow rate, but nonlinear to solid particle flow rate and particle size; the GA-ELM model can effectively predict the erosion residual life of a defective elbow. The prediction accuracy and generalization ability of the GA-ELM model are better than those of the traditional ELM model. Full article

(This article belongs to the Special Issue Application of AI Technologies in Pipeline Health Monitoring and Energy Prediction)

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18 pages, 2896 KiB

Open AccessArticle

A Data-Driven Methodology for the Reliability Analysis of the Natural Gas Compressor Unit Considering Multiple Failure Modes

by Weichao Yu, Xianbin Zheng, Weihe Huang, Qingwen Cai, Jie Guo, Jili Xu, Yang Liu, Jing Gong and Hong Yang

Energies 2022, 15(10), 3557; https://0-doi-org.brum.beds.ac.uk/10.3390/en15103557 - 12 May 2022

Cited by 1 | Viewed by 1412

Abstract

In this study, a data-driven methodology for the reliability analysis of natural gas compressor units is developed, and both the historical failure data and performance data are employed. In this methodology, firstly, the reliability functions of the catastrophic failure and degradation failure are built. For catastrophic failure, the historical failure data are collected, and the rank regression model is utilized to obtain the reliability function of the catastrophic failure. For degradation failure, a support-vector machine is employed to predict the unit’s performance parameters, and the reliability function of the degradation failure is determined by comparing the performance parameters with the failure threshold. Finally, the reliability of the compressor unit is assessed and predicted by integrating the reliability functions of the catastrophic failure and the degradation failure, and both their correlation and competitiveness are considered. Furthermore, the developed methodology is applied to an actual compressor unit to confirm its feasibility, and the reliability of the compressor unit is predicted. The assessment results indicate the significant impact of the operating conditions on the precise forecasting of the performance parameters. Moreover, the effects of the value of the failure threshold and the correlation of the two failure modes on the reliability are investigated. Full article

(This article belongs to the Special Issue Application of AI Technologies in Pipeline Health Monitoring and Energy Prediction)

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21 pages, 5536 KiB

Open AccessArticle

Stress and Pressure Pulsation Analysis of Low Temperature Compressor Piping System in LNG Vaporizing Station

by Baoqing Wang, Zhi Zhang, Kun Huang, Yaotong Zhang, Zhenwu Zhang, Hui Gao and Lingdi Fu

Energies 2022, 15(5), 1874; https://0-doi-org.brum.beds.ac.uk/10.3390/en15051874 - 03 Mar 2022

Viewed by 1963

Abstract

LNG (Liquefied Natural Gas) vaporizing stations are usually built in the cities and towns, and the BOG (Boiled Off Gas) pressurizing system is a very important element. In the pressurizing system, the severe vibration of the low-temperature reciprocating compressor may lead to a failure of the pipeline system and the equipment. Therefore, this paper analyzes the stress and pressure pulsation of the BOG compressor piping system in the LNG vaporizing station. The beam model was used to establish the pipe model. The static, harmonic and modal analysis were carried out based on the plane-wave theory and the pressure-fluctuation theory, and the influence factors of support spacing, the settlement of the fulcrum foundation, pipe pressure and elbow angle were analyzed. The main conclusions are as follows: (1) the unbalanced excited force caused by pressure pulsation greatly affects the stress of the exhaust pipe and compressor outlet pipe, and has less influence on the stress of the suction pipe and compressor inlet pipe; (2) although unbalanced excited force is generated in the elbow, it also has an impact on the straight pipe stress; (3) adding an expansion joint to the pipe of the BOG compressor can effectively reduce the stress of the pipe its the displacement, and can increase the flexibility of the pipe. Full article

(This article belongs to the Special Issue Application of AI Technologies in Pipeline Health Monitoring and Energy Prediction)

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19 pages, 2845 KiB

Open AccessArticle

A Novel Feature Identification Method of Pipeline In-Line Inspected Bending Strain Based on Optimized Deep Belief Network Model

by Shucong Liu, Hongjun Wang, Rui Li and Beilei Ji

Energies 2022, 15(4), 1586; https://0-doi-org.brum.beds.ac.uk/10.3390/en15041586 - 21 Feb 2022

Cited by 2 | Viewed by 2007

Abstract

Both long-distance oil and gas pipelines often pass through areas with unstable geological conditions or natural disasters. As a result, they are prone to bending, displacement, and deformation due to the action of an external environmental loading, which poses a threat to the safe operation of pipelines. The in-line inspection method that is based on the implementation of high-precision inertial measurement units (IMU) has become the main means of pipeline bending stress-strain detection technique. However, to address the problems of the inconsistent identification, low identification efficiency, and high misjudgment rate during the application of the traditional manual identification methods, a feature identification approach for the in-line inspected pipeline bending strain based on the employment of an optimized deep belief network (DBN) model is proposed in this work. In addition, our model can automatically learn features from the pipeline bending strain signals and complete classification and identification. On top of that, after the network model was trained and tested by using the actual pipeline bending strain inspection data, the extracted results showed that the model after the implementation of the training process could accurately identify and classify various pipeline features, with an identification accuracy and efficiency of 97.8% and 0.02 min/km, respectively. The high efficiency, elevated accuracy, and strong robustness of our method can effectively improve the in-line inspection procedure of pipelines during the enforcement of a bending strain load. Full article

(This article belongs to the Special Issue Application of AI Technologies in Pipeline Health Monitoring and Energy Prediction)

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Review

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30 pages, 5631 KiB

Open AccessReview

Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review

by Haotian Li, Kun Huang, Qin Zeng and Chong Sun

Energies 2022, 15(3), 726; https://0-doi-org.brum.beds.ac.uk/10.3390/en15030726 - 19 Jan 2022

Cited by 4 | Viewed by 2624

Abstract

Prediction of residual strength and residual life of corrosion pipelines is the key to ensuring pipeline safety. Accurate assessment and prediction make it possible to prevent unnecessary accidents and casualties, and avoid the waste of resources caused by the large-scale replacement of pipelines. However, due to many factors affecting pipeline corrosion, it is difficult to achieve accurate predictions. This paper reviews the research on residual strength and residual life of pipelines in the past decade. Through careful reading, this paper compared several traditional evaluation methods horizontally, extracted 71 intelligent models, discussed the publishing time, the evaluation accuracy of traditional models, and the prediction accuracy of intelligent models, input variables, and output value. This paper’s main contributions and findings are as follows: (1) Comparing several traditional evaluation methods, PCORRC and DNV-RP-F101 perform well in evaluating low-strength pipelines, and DNV-RP-F101 has a better performance in evaluating medium–high strength pipelines. (2) In intelligent models, the most frequently used error indicators are mean square error, goodness of fit, mean absolute percentage error, root mean square error, and mean absolute error. Among them, mean absolute percentage error was in the range of 0.0123–0.1499. Goodness of fit was in the range of 0.619–0.999. (3) The size of the data set of different models and the data division ratio was counted. The proportion of the test data set was between 0.015 and 0.4. (4) The input variables and output value of predictions were summarized. Full article

(This article belongs to the Special Issue Application of AI Technologies in Pipeline Health Monitoring and Energy Prediction)

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