scholarly journals Computational Modeling and Prediction on Viscosity of Slags by Big Data Mining

Minerals ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 257 ◽  
Author(s):  
Ao Huang ◽  
Yanzhu Huo ◽  
Juan Yang ◽  
Huazhi Gu ◽  
Guangqiang Li
Keyword(s):  
Data Mining ◽  
Recall Rate ◽  
Equation Model ◽  
Misclassification Rate ◽  
Slag Viscosity ◽  
Data Mining Approach ◽  
Key Factor ◽  
Wool Processing ◽  
Single Data ◽  
Positive Correlations

The viscosity of slag is a key factor affecting metallurgical efficiency and recycling, such as metal-slag reaction and separation, as well as slag wool processing. In order to comprehensively clarify the variation of the slag viscosity, various data mining methods have been employed to predict the viscosity of the slag. In this study, a more advanced dual-stage predictive modeling approach is proposed in order to accurately analyze and predict the viscosity of slag. Compared with the traditional single data mining approach, the proposed method performs better with a higher recall rate and low misclassification rate. The simulation results show that temperature, SiO2, Al2O3, P2O5, and CaO have greater influences on the slag’s viscosity. The critical temperature for onset of the important influence of slag composition is 980 °C. Furthermore, it is found that SiO2 and P2O5 have positive correlations with slag’s viscosity, while temperature, Al2O3, and CaO have negative correlations. A two-equation model of six-degree polynomial combined with Arrhenius formula is also established for the purpose of providing theoretical guidance for industrial application and reutilization of slag.

Data mining approach to construction productivity prediction for cutter suction dredgers

2019 ◽  
Vol 105 ◽  
pp. 102833 ◽  
Author(s):  
Shuo Bai ◽  
Mingchao Li ◽  
Rui Kong ◽  
Shuai Han ◽  
Heng Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Construction Productivity ◽  
Data Mining Approach ◽  
Productivity Prediction

Data mining approach for improving the optimal control of HVAC systems: An event-driven strategy

2021 ◽  
Vol 39 ◽  
pp. 102246
Author(s):  
Junqi Wang ◽  
Jin Hou ◽  
Jianping Chen ◽  
Qiming Fu ◽  
Gongsheng Huang
Keyword(s):  
Data Mining ◽  
Optimal Control ◽  
Hvac Systems ◽  
Data Mining Approach ◽  
Event Driven

scholarly journals Data Mining Approach for Learning management system

2021 ◽  
Vol 1088 (1) ◽  
pp. 012013
Author(s):  
Harry Dhika ◽  
Fitriana Destiawati ◽  
Surajiyo ◽  
Musa Jaya
Keyword(s):  
Data Mining ◽  
Management System ◽  
Learning Management System ◽  
Data Mining Approach ◽  
Learning Management

scholarly journals Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Satoko Hiura ◽  
Shige Koseki ◽  
Kento Koyama
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Listeria Monocytogenes ◽  
Water Activity ◽  
Bacterial Population ◽  
Gradient Boosting ◽  
Initial Cell ◽  
Data Mining Approach ◽  
Cell Counts ◽  
Extreme Gradient Boosting

AbstractIn predictive microbiology, statistical models are employed to predict bacterial population behavior in food using environmental factors such as temperature, pH, and water activity. As the amount and complexity of data increase, handling all data with high-dimensional variables becomes a difficult task. We propose a data mining approach to predict bacterial behavior using a database of microbial responses to food environments. Listeria monocytogenes, which is one of pathogens, population growth and inactivation data under 1,007 environmental conditions, including five food categories (beef, culture medium, pork, seafood, and vegetables) and temperatures ranging from 0 to 25 °C, were obtained from the ComBase database (www.combase.cc). We used eXtreme gradient boosting tree, a machine learning algorithm, to predict bacterial population behavior from eight explanatory variables: ‘time’, ‘temperature’, ‘pH’, ‘water activity’, ‘initial cell counts’, ‘whether the viable count is initial cell number’, and two types of categories regarding food. The root mean square error of the observed and predicted values was approximately 1.0 log CFU regardless of food category, and this suggests the possibility of predicting viable bacterial counts in various foods. The data mining approach examined here will enable the prediction of bacterial population behavior in food by identifying hidden patterns within a large amount of data.

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