The Evaluation of the Corrosion Rates of Alloys Applied to the Heating Tower Heat Pump (HTHP) by Machine Learning

Liu, Qingqing; Li, Nianping; Yongga, A; Duan, Jiaojiao; Yan, Wenyun

doi:10.3390/en14071972

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…Liu Q. et al [15] established an ANN model for corrosion rate prediction in Copper and Aluminum alloys. The ANN had 5 input parameters and 2 hidden layers of 10 neurons each with Mean square error as loss function and a Sigmoid function as an activation function.…”

Section: Related Workmentioning

confidence: 99%

Atmospheric corrosion rate prediction of low-alloy steel using machine learning models

Thanush¹,

Chitra²,

Kasinath³

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Corrosion mitigation is one of the indispensable needs in many industries and is currently being pursued by various methods like surface modification, corrosion inhibitor addition, and cathodic protection systems. Corrosion rate prediction can help in designing alloys with an optimized composition of materials such that it has a lower corrosion rate in the environment where they are exposed. Corrosion rate prediction can also help the manufacturers to plan the replacement of the sample used in advance. Machine learning, which is the science of making machines learn without being explicitly programmed and without using pre-determined equations, can help overcome challenges in predicting corrosion of various materials under a variety of environmental conditions. In this paper, three machine learning algorithms namely Support Vector Regression, Multiple Linear Regression, and Random Forest Regression are used to develop a Hybrid model to predict the corrosion rate of materials. Feature reduction is performed after feature importance calculation using Random Forest Regression model. The accuracy of the developed models were calculated using r2 scores as an evaluation metric for different train-test split ratios. The input data for various conditions such as open, sheltered, coastal. Etc. are fed to the model and the performance was evaluated. The results show that the proposed Hybrid model outperforms the other baseline approaches with an accuracy of 91.46%, for predicting corrosion rate of materials.

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Section: Related Workmentioning

confidence: 99%

Atmospheric corrosion rate prediction of low-alloy steel using machine learning models

Thanush¹,

Chitra²,

Kasinath³

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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show abstract

“…As mentioned, various PPV prediction techniques are available but only the empirical formula of Equation (6) has been used to predict PPVs for blasting designs in South Korea [18]. Therefore, in this study, the empirical formula developed by USBM was selected to assess ground vibration and identify the optimal prediction method.…”

Section: Empirical Formulamentioning

confidence: 99%

“…The artificial neural network (ANN) has been applied in various fields such as renewable energy systems [6], atmospheric science [7], and civil engineering [8,9] to predict targets. In addition, research is also ongoing on predicting PPVs using ANN.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations

Choi

Lee

2021

Applied Sciences

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Reliable estimates of peak particle velocity (PPV) from blasting-induced vibrations at a construction site play a crucial role in minimizing damage to nearby structures and maximizing blasting efficiency. However, reliably estimating PPV can be challenging due to complex connections between PPV and influential factors such as ground conditions. While many efforts have been made to estimate PPV reliably, discrepancies remain between measured and predicted PPVs. Here, we analyzed various methods for assessing PPV with several key relevant factors and 1,191 monitored field blasting records at 50 different open-pit sites across South Korea to minimize the discrepancies. Eight prediction models are used based on artificial neural network, conventional empirical formulas, and multivariable regression analyses. Seven influential factors were selected to develop the prediction models, including three newly included and four already formulated in empirical formulas. The three newly included factors were identified to have a significant influence on PPV, as well as the four existing factors, through a sensitivity analysis. The measured and predicted PPVs were compared to evaluate the performances of prediction models. The assessment of PPVs by an artificial neural network yielded the lowest errors, and site factors, K and m were proposed for preliminary open-pit blasting designs.

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“…A study was conducted to develop a model for predicting energy consumption in buildings using machine learning [5][6][7] and to predict the heating and cooling load [8,9]. In relation to the heat pump system, a study to predict performance using a mathematical model [10], a study to develop a model to predict the performance of a geothermal heat pump system based on artificial neural networks [11,12] and random forest [13] models has proceeded A study was conducted to predict the performance of air-cooled heat pump systems [14][15][16] and to develop a performance prediction model for heating tower heat pumps [17]. Most of the studies related to the performance prediction of the aforementioned heat pump system develop a predictive model based on one machine learning.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Coefficient of Performance Prediction Model for Heat Pump Systems

Shin

Cho

2021

Applied Sciences

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In a heat pump system, performance is an important indicator that should be monitored for system optimization, fault diagnosis, and operational efficiency improvement. Real-time performance measurement and monitoring during heat pump operation is difficult because expensive performance measurement devices or additional installation of various monitoring sensors required for performance calculation are required. When using a data-based machine-learning model, it is possible to predict and monitor performance by finding the relationship between input and output values through an existing sensor. In this study, the performance prediction model of the air-cooled heat pump system was developed and verified using artificial neural network, support vector machine, random forest, and K-nearest neighbor model. The operation data of the heat pump system installed in the university laboratory was measured and a prediction model for each machine-learning stage was developed. The mean bias error analysis is −3.6 for artificial neural network, −5 for artificial neural network, −7.7 for random forest, and −8.3 for K-nearest neighbor. The artificial neural network model with the highest accuracy and the shortest calculation time among the developed prediction models was applied to the Building Automation System to enable real-time performance monitoring and to confirm the field applicability of the developed model.

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The Evaluation of the Corrosion Rates of Alloys Applied to the Heating Tower Heat Pump (HTHP) by Machine Learning

Cited by 7 publications

References 27 publications

Atmospheric corrosion rate prediction of low-alloy steel using machine learning models

Atmospheric corrosion rate prediction of low-alloy steel using machine learning models

Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations

Machine-Learning-Based Coefficient of Performance Prediction Model for Heat Pump Systems

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