The Online Prediction of the Low Carbon Ferrochrome Terminal Composition in Smelting Process

2013 ◽  
Vol 645 ◽  
pp. 519-522
Author(s):  
Niao Na Zhang ◽  
Ying Ying Wang ◽  
Yong Jun Bai
Keyword(s):  
Power Supply System ◽  
Production Quality ◽  
Smelting Process ◽  
Support Vector ◽  
Low Carbon ◽  
Multi Scale ◽  
Carbon Ferrochrome ◽  
Svm Algorithm ◽  
Wavelet Kernel Function ◽  
Online Prediction

The online prediction of the low carbon ferrochrome terminal composition in electro-silicothemic smelting process plays a key role in guiding the determining the tapping time, the smelting process of the power supply system, the production quality and the energy consumption and so on. By introducing the multi-scale wavelet kernel function in the support vector machine (SVM) algorithm, and according to the Bayesian classifier to certain different smelting conditions, we chose different decomposition scales. In this way, the accuracy of the terminal composition prediction during the smelting process is improved greatly. Experiments show the effectiveness of the proposed method.

The End-Point Ingredient Prediction of Low Carbon Ferrochrome Smelt Based on the Working Condition

2011 ◽  
Vol 128-129 ◽  
pp. 1246-1249
Author(s):  
Niao Na Zhang ◽  
Ke Wei Liu ◽  
Bao Dong Zhang
Keyword(s):  
Working Conditions ◽  
Information Fusion ◽  
Working Condition ◽  
Bayesian Classifier ◽  
Support Vector ◽  
Low Carbon ◽  
Multi Scale ◽  
End Point ◽  
Prediction Result ◽  
Carbon Ferrochrome

Construct the model of the end-point ingredient prediction of low carbon ferrochrome smelt based on the working condition of electrothermal silicon method using the method of multi-scale support vector machiness information fusion, where the best decomposition scale information is according to different smelt working conditions using Levenberg-Marquart algorithm to optimize the design, smelt working condition is judged by Bayesian classifier. Researches have proved that this method can improve the precision of prediction and make the prediction result more accurate, reasonable and practical.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

scholarly journals Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

2021 ◽  
Vol 10 (5) ◽  
pp. 992
Author(s):  
Martina Barchitta ◽  
Andrea Maugeri ◽  
Giuliana Favara ◽  
Paolo Marco Riela ◽  
Giovanni Gallo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Support Vector ◽  
Icu Admission ◽  
Risk Of Death ◽  
Saps Ii ◽  
Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

Factors Influencing Dephosphorization of Low Carbon Steel in Converter

2021 ◽  
Vol 1047 ◽  
pp. 111-119
Author(s):  
Zhao Liu ◽  
Shu Sen Cheng ◽  
Liang Wang
Keyword(s):  
Carbon Steel ◽  
Low Temperature ◽  
Slag Composition ◽  
Phosphorus Content ◽  
Low Carbon Steel ◽  
Smelting Process ◽  
Low Carbon ◽  
Factors Influencing ◽  
High Basicity ◽  
Metal Yield

A 300-metric ton converter in a steel plant in China was studied. The influence of factors such as slag composition and temperature in the smelting process on the dephosphorization effect was statistically analyzed. The dephosphorization ability of slag increased firstly and then decreased with the increase of temperature, basicity and FeO content. Low-temperature, high-basicity and high-oxidizing slag are thermodynamically beneficial to promote the dephosphorization reaction, but the basicity is higher than 4.0, and the temperature is higher than 1640 °C are not conducive to the slag to obtain better fluidity. At the same time, too high FeO content will increase the activity coefficient of P2O5, thereby increasing its activity, which is not conducive to the progress of the dephosphorization reaction. As the end point content of carbon decreases, the oxygen content increases and the phosphorus content decreases. A very low carbon content is not conducive to metal yield and temperature control.

A Study on the smelting process of low carbon steel scrap and sponge iron in medium frequency induction furnace for production of front bumper beams

2021 ◽  
Vol 98 ◽  
pp. 8-13
Author(s):  
Dung Ngo Quoc ◽  
◽  
Viet Nguyen Hoang
Keyword(s):  
Carbon Steel ◽  
Induction Furnace ◽  
Current Trend ◽  
Low Carbon Steel ◽  
Steel Grade ◽  
Sponge Iron ◽  
Smelting Process ◽  
Steel Scrap ◽  
Low Carbon ◽  
Medium Frequency

MS1200 steel grade is now widely utilized in the automotive sector because it is a good solution for the current trend of vehicle chassis frame construction. This research presents a technology procedure for producing MS1200 steel grade from low carbon steel scrap and sponge iron – a product of MIREX Vietnam. The smelting using up to 30 % sponge iron briquettes combined with low carbon scrap, FeSi, FeMn, FeCr, FeTi,… was realized in a medium frequency induction furnace. The heat treatment for forged steel was performed to obtain required properties. The steel product has the following properties: tensile strength σb = 1280 MPa, yield strength σ0.2 = 990 MPa and impact toughness ak = 769 J/mm2, that meets the need of industrial use.

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