A new hybrid algorithm model for prediction of internal corrosion rate of multiphase pipeline

Peng, Shanbi; Zhang, Zhe; Liu, Enbin; Liu, Wei; Qiao, Weibiao

doi:10.1016/j.jngse.2020.103716

Cited by 86 publications

(14 citation statements)

References 36 publications

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“…Given the shortcomings and limitations of each of the stand alone models Feng et al 2020;Qian et al 2020;Qu et al 2020;Shi et al 2020), scientists have proposed and developed integrated methods to overcome their disadvantages and increase their efficiency (Cao, Dong, et al 2020;Peng et al 2020;Liu et al 2021). In this research, A novel ensemble of GE-XGBoost was proposed for better prediction analysis of gully erosion than the stand-alone model.…”

Section: Discussionmentioning

confidence: 99%

Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

Arabameri

Pal

Costache

et al. 2021

Geomatics, Natural Hazards and Risk

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Spatial modelling of gully erosion at regional level is very relevant for local authorities to establish successful counter-measures and to change land-use planning. This work is exploring and researching the potential of a genetic algorithm-extreme gradient boosting (GE-XGBoost) hybrid computer education solution for spatial mapping of the susceptibility of gully erosion. The new machine learning approach is to combine the extreme gradient boosting machine (XGBoost) and the genetic algorithm (GA). The GA metaheuristic is being used to improve the efficiency of the XGBoost classification approach. A GIS database has been developed that contains recorded instances of gully erosion incidents and 18 conditioning variables. These parameters are used as predictive variables used to assess the condition of non-erosion or erosion in a given region within the Kohpayeh-Sagzi River Watershed research area in Iran. Exploratory results indicate that the proposed GE-XGBoost model is superior to the other benchmark solution with the desired predictive precision (89.56%). Therefore, the newly built model may be a promising method for large-scale mapping of gully erosion susceptibility.

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Section: Discussionmentioning

confidence: 99%

Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

Arabameri

Pal

Costache

et al. 2021

Geomatics, Natural Hazards and Risk

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“…Moreover, they concluded that the corrosion growth rate increased with the increase of metal loss and decreased with the careful maintenance of pipeline cathodic protection. A hybrid intelligent model named PCA-CPSO-SVR, which combines PCA, SVR and chaos particle swarm optimization (CPSO), is proposed for corrosion rate improvement of pipelines [ 184 ]. PCA plays a role in reducing data dimension and screening out the main variables of corrosion influencing factors, such as temperature, liquid holdup, etc.…”

Section: Data Managementmentioning

confidence: 99%

Pipeline In-Line Inspection Method, Instrumentation and Data Management

Qiu

Tian

Zeng

et al. 2021

Sensors

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Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

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“…With recent advances in computational intelligence, many scholars have replaced traditional methods with new generated machine learning [6][7][8][9][10][11], deep learning [12][13][14][15][16][17], decision making [18,19], and artificial intelligence-based tools [20][21][22]. These novel approximation techniques are well employed in various engineering fields such as in evaluating environmental concerns [19,[23][24][25][26][27][28][29][30][31], implications for natural environmental management [32][33][34][35][36][37][38][39], water resources management [28,[40][41][42][43][44], natural gas consumption [45][46][47][48], energy efficiency [49][50]…”

Section: Background Of Artificial Intelligencementioning

confidence: 99%

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Moayedi

Mosavi

2021

Energies

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A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).

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A new hybrid algorithm model for prediction of internal corrosion rate of multiphase pipeline

Cited by 86 publications

References 36 publications

Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

Pipeline In-Line Inspection Method, Instrumentation and Data Management

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

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