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

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1972
Author(s):  
Qingqing Liu ◽  
Nianping Li ◽  
Yongga A ◽  
Jiaojiao Duan ◽  
Wenyun Yan
Keyword(s):  
Corrosion Rate ◽  
Heat Pump ◽  
Metal Corrosion ◽  
Support Vector ◽  
Mean Square ◽  
Ann Model ◽  
Good Reliability ◽  
Important Indicator ◽  
Corrosion Rates ◽  
Split Method

The corrosion rate is an important indicator describing the degree of metal corrosion, and quantitative analysis of the corrosion rate is of great significance. In the present work, the support vector machine (SVM) and the artificial neural network (ANN) integrating the k-fold split method and the root-mean-square prop (RMSProp) optimizer are used to evaluate the corrosion rates of alloys, i.e., copper H65, aluminum 3003, and 20# steel, applied to the heating tower heat pump (HTHP) in various anti-freezing solutions at different corrosion times, flow velocities, and temperatures. The mean-square error (MSE) versus the epoch of the ANN model shows that the result breaks the local minimum and is at or close to the global minimum. Comparisons of the SVM-/ANN-evaluated corrosion rates and the measured ones show good agreements, demonstrating the good reliability of the obtained SVM and ANN models. Moreover, the ANN model is recommended since it performs better than the SVM model according to the obtained R2 value. The present work can be further applied to predicting the corrosion rate without any prior experiment for improving the service life of the HTHP.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

Unique Autoclave Stress Induced Failure Mechanism

2005 ◽  
Author(s):  
John Butchko ◽  
Bruce T. Gillette
Keyword(s):  
Corrosion Rate ◽  
Grain Boundary ◽  
Failure Analysis ◽  
Failure Mechanism ◽  
Metal Corrosion ◽  
Corrosion Mechanism ◽  
Reliability Testing ◽  
Process Change ◽  
Transistor Reliability ◽  
Pass Through

Abstract Autoclave Stress failures were encountered at the 96 hour read during transistor reliability testing. A unique metal corrosion mechanism was found during the failure analysis, which was creating a contamination path to the drain source junction, resulting in high Idss and Igss leakage. The Al(Si) top metal was oxidizing along the grain boundaries at a faster rate than at the surface. There was subsurface blistering of the Al(Si), along with the grain boundary corrosion. This blistering was creating a contamination path from the package to the Si surface. Several variations in the metal stack were evaluated to better understand the cause of the failures and to provide a process solution. The prevention of intergranular metal corrosion and subsurface blistering during autoclave testing required a materials change from Al(Si) to Al(Si)(Cu). This change resulted in a reduced corrosion rate and consequently prevented Si contamination due to blistering. The process change resulted in a successful pass through the autoclave testing.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

scholarly journals Workflow to build a continuous static elastic moduli profile from the drilling data using artificial intelligence techniques

2021 ◽  
Author(s):  
Osama Siddig ◽  
Salaheldin Elkatatny
Keyword(s):  
Young’S Modulus ◽  
Elastic Moduli ◽  
Young's Modulus ◽  
Correlation Coefficients ◽  
Wellbore Stability ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ann Model ◽  
Weight On Bit ◽  
Data Points

AbstractRock mechanical properties play a crucial role in fracturing design, wellbore stability and in situ stresses estimation. Conventionally, there are two ways to estimate Young’s modulus, either by conducting compressional tests on core plug samples or by calculating it from well log parameters. The first method is costly, time-consuming and does not provide a continuous profile. In contrast, the second method provides a continuous profile, however, it requires the availability of acoustic velocities and usually gives estimations that differ from the experimental ones. In this paper, a different approach is proposed based on the drilling operational data such as weight on bit and penetration rate. To investigate this approach, two machine learning techniques were used, artificial neural network (ANN) and support vector machine (SVM). A total of 2288 data points were employed to develop the model, while another 1667 hidden data points were used later to validate the built models. These data cover different types of formations carbonate, sandstone and shale. The two methods used yielded a good match between the measured and predicted Young’s modulus with correlation coefficients above 0.90, and average absolute percentage errors were less than 15%. For instance, the correlation coefficients for ANN ranged between 0.92 and 0.97 for the training and testing data, respectively. A new empirical correlation was developed based on the optimized ANN model that can be used with different datasets. According to these results, the estimation of elastic moduli from drilling parameters is promising and this approach could be investigated for other rock mechanical parameters.

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

scholarly journals Simulating the Stress-Strain Relationship of Geomaterials by Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hongbo Zhao ◽  
Zenghui Huang ◽  
Zhengsheng Zou
Keyword(s):  
Support Vector Machine ◽  
Constitutive Relationship ◽  
Support Vector ◽  
Nonlinear Relationship ◽  
Ann Model ◽  
Stress Strain ◽  
Svm Model ◽  
Strain Relationship ◽  
Artificial Neural Network Ann ◽  
Relationship Of

Stress-strain relationship of geomaterials is important to numerical analysis in geotechnical engineering. It is difficult to be represented by conventional constitutive model accurately. Artificial neural network (ANN) has been proposed as a more effective approach to represent this complex and nonlinear relationship, but ANN itself still has some limitations that restrict the applicability of the method. In this paper, an alternative method, support vector machine (SVM), is proposed to simulate this type of complex constitutive relationship. The SVM model can overcome the limitations of ANN model while still processing the advantages over the traditional model. The application examples show that it is an effective and accurate modeling approach for stress-strain relationship representation for geomaterials.

The Corrosion of Carbon Steel and Stainless Steel in Simulated Geothermal Media

1985 ◽  
Vol 38 (8) ◽  
pp. 1133 ◽  
Author(s):  
BG Pound ◽  
MH Abdurrahman ◽  
MP Glucina ◽  
GA Wright ◽  
RM Sharp
Keyword(s):  
Carbon Steel ◽  
Corrosion Rate ◽  
Stainless Steels ◽  
Polarization Resistance ◽  
Iron Sulfide ◽  
Low Carbon Steel ◽  
Passive Films ◽  
Low Carbon ◽  
Corrosion Rates ◽  
Good Agreement

The corrosion rates of low-carbon steel, and 304, 316 and 410/420 stainless steels in simulated geothermal media containing hydrogen sulfide have been measured by means of the polarization resistance technique. Good agreement was found between weight-loss and polarization resistance measurements of the corrosion rate for all the metals tested. Carbon steel formed a non-adherent film of mackinawite (Fe1 + xS). The lack of protection afforded to the steel by the film resulted in an approximately constant corrosion rate. The stainless steels also exhibited corrosion rates that were independent of time. However, the 410 and 420 alloys formed an adherent film consisting mainly of troilite ( FeS ) which provided only limited passivity. In contrast, the 304 and 316 alloys appeared to be essentially protected by a passive film which did not seem to involve an iron sulfide phase. However, all the stainless steels, particularly the 410 and 420 alloys, showed pitting, which indicated that some breakdown of the passive films occurred.

Corrosion Tests of Zircaloy Hull Waste to Confirm Applicability of Corrosion Model and to Evaluate Influence Factors on Corrosion Rate under Geological Disposal Conditions

2014 ◽  
Vol 1665 ◽  
pp. 195-202 ◽  
Author(s):  
Osamu Kato ◽  
Hiromi Tanabe ◽  
Tomofumi Sakuragi ◽  
Tsutomu Nishimura ◽  
Tsuyoshi Tateishi
Keyword(s):  
Corrosion Rate ◽  
Corrosion Behavior ◽  
Pure Water ◽  
Influence Factors ◽  
Spent Fuel ◽  
Geological Disposal ◽  
Corrosion Tests ◽  
Temperature Range ◽  
Corrosion Rates ◽  
Temperature Ranges

ABSTRACTCorrosion behavior is a key issue in the assessment of disposal performance for activated waste such as spent fuel assemblies (i.e., hulls and end-pieces) because corrosion is expected to initiate radionuclide (e.g., C-14) leaching from such waste. Because the anticipated corrosion rate is extremely low, understanding and modeling Zircaloy (Zry) corrosion behavior under geological disposal conditions is important in predicting very long-term corrosion. Corrosion models applicable in the higher temperature ranges of nuclear reactors have been proposed based on considerable testing in the 523−633 K temperature range.In this study, corrosion tests were carried out to confirm the applicability of such existing models to the low temperature range of geological disposal, and to examine the influence of material, environmental, and other factors on corrosion rates under geological disposal conditions. A characterization analysis of the generated oxide film was also performed.To confirm applicability, the corrosion rate of Zry-4 in pure water with a temperature change from 303 K to 433 K was obtained using a hydrogen measuring technique, giving a corrosion rate for 180 days of 8 × 10-3 μm/y at 303 K.To investigate the influence of various factors, corrosion tests were carried out. The corrosion rates for Zry-2 and Zry-4 were almost same, and increased with a temperature increase from 303 K to 353 K. The influence of pH (12.5) compared with pure water was about 1.4 at 180 days at 303 K.

scholarly journals Effects of tungsten, chromium and zirconium on the corrosion behavior of ternary amorphous W–Cr–Zr alloys in 1 M NaOh solution

1970 ◽  
Vol 9 (9) ◽  
pp. 39-43
Author(s):  
Basu Ram Aryal ◽  
Jagadeesh Bhattarai
Keyword(s):  
Corrosion Resistance ◽  
Corrosion Rate ◽  
Open Circuit Potential ◽  
Open Circuit ◽  
Corrosion Rates ◽  
Corrosion Resistance Property ◽  
Naoh Solution ◽  
Order Of Magnitude ◽  
Sputter Deposited ◽  
Zr Alloys

Simultaneous additions of tungsten, chromium and zirconium in the chromium- and zirconium-enriched sputter-deposited binary W-xCr and W-yZr are effective to improve the corrosion resistance property of the ternary amorphous W- xCr-yZr alloys after immersion for 240 h in 1 M NaOH solution open to air at 25°C. The corrosion rates of all the examined sputter-deposited (10-57)W-(18-42)Cr-(25-73)Zr alloys is higher than those of alloy-constituting elements (that is, tungsten, chromium and zirconium) in aggressive 1 M NaOH solution open to air at 25°C. The corrosion rates of all the examined sputter−deposited W–xCr–yZr alloys containing 10-57 at% tungsten, 18-42 at% chromium and 25-73 at% zirconium were in the range of 1.5-2.5 × 10−3 mm/y or lower which are more than two orders of magnitude lower than that of sputter-deposited tungsten and even about one order of magnitude lower than those of the sputter-deposited zirconium in 1 M NaOH solution. Keywords: Ternary W–Cr–Zr alloys; Amorphous; Corrosion rate; Open circuit potential; 1 M NaOH. DOI: http://dx.doi.org/10.3126/sw.v9i9.5516 SW 2011; 9(9): 39-43

scholarly journals The effect of volatile organic acids and CO2 on the corrosion rate of carbon steel from a Top-of-Line-Corrosion (TLC) perspective

2021 ◽  
Vol 1201 (1) ◽  
pp. 012079
Author(s):  
S B Gjertsen ◽  
A Palencsar ◽  
M Seiersten ◽  
T H Hemmingsen
Keyword(s):  
Carbon Steel ◽  
Corrosion Rate ◽  
Organic Acid ◽  
Carbonic Acid ◽  
Acid Corrosion ◽  
Iron Concentration ◽  
Critical Factor ◽  
Steel Corrosion ◽  
Carbon Steels ◽  
Corrosion Rates

Abstract Models for predicting top-of-line corrosion (TLC) rates on carbon steels are important tools for cost-effectively designing and operating natural gas transportation pipelines. The work presented in this paper is aimed to investigate how the corrosion rates on carbon steel is affected by acids typically present in the transported pipeline fluids. This investigation may contribute to the development of improved models. In a series of experiments, the corrosion rate differences for pure CO2 (carbonic acid) corrosion and pure organic acid corrosion (acetic acid and formic acid) on X65 carbon steel were investigated at starting pH values; 4.5, 5.3, or 6.3. The experiments were conducted in deaerated low-salinity aqueous solutions at atmospheric pressure and temperature of 65 °C. The corrosion rates were evaluated from linear polarization resistance data as well as mass loss and released iron concentration. A correlation between lower pH values and increased corrosion rates was found for the organic acid experiments. However, the pH was not the most critical factor for the rates of carbon steel corrosion in these experiments. The experimental results showed that the type of acid species involved and the concentration of the undissociated acid in the solution influenced the corrosion rates considerably.

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