scholarly journals Using Artificial Intelligence Techniques to Improve the Prediction of Copper Recovery by Leaching

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Victor Flores ◽  
Brian Keith ◽  
Claudio Leiva
Keyword(s):  
Artificial Intelligence ◽  
Underground Mining ◽  
Technological Development ◽  
Mining Industry ◽  
Copper Recovery ◽  
Copper Mining ◽  
Artificial Intelligence Techniques ◽  
Environmental Controls ◽  
Using Data ◽  
Operational Data

Copper mining activity is going through big changes due to increasing technological development in the area and the influence of industry 4.0. These changes, produced by technological context and more controls (e.g., environmental controls), are also becoming visible in Chilean mining. New regulations from the Chilean government and changes in the copper mining industry (such as a trend to underground mining) are fostering the search for better results in typical processes such as leaching. This paper describes an experience using artificial intelligence techniques, particularly random forest, to develop predictive models for copper recovery by leaching, using data from an enterprise present in northern Chile for more than 20 years. Two models, one of them with actual operational data and another one with data generated in a controlled environment (piling) are presented. Well-classified values of 98.90% for operational data and 98.72% for pile/piling data were obtained. The methodology devised for the study can be transferred to piling columns or piles with other characteristics, though the operation must focus on copper leaching. It can even be transferred to other leaching processes using another type of mineral, with proper adjustments.

scholarly journals Automation and Robotization of Underground Mining in Poland

2020 ◽  
Vol 10 (20) ◽  
pp. 7221
Author(s):  
Łukasz Bołoz ◽  
Witold Biały
Keyword(s):  
Artificial Intelligence ◽  
International Research ◽  
Underground Mining ◽  
Mining Industry ◽  
Specific Character ◽  
Research Centers ◽  
Rescue Work ◽  
Machine System ◽  
Foreign Companies

The article concerns the condition of automation and robotization of underground mining in Poland. Attention has been focused on the specific character of the mining industry. This limits the possibility of using robotization, and sometimes even the mechanization of certain processes. In recent years, robotic and automated machines and machine system solutions have been developed and applied in Poland. They are autonomous to a various degree, depending on the branch. The type of automation and artificial intelligence depends on the specific use. Some examples presently being used include the MIKRUS automated longwall system and autonomous device(s) for breaking rocks or mining rescue work. In Poland, fully automated plow systems produced by foreign companies are also used. Companies in Poland and international research centers are also actively engaged in the development of underwater and space mining. where robotization is of key importance. Research is also being undertaken by Robotics in Mining, euRobotics and PERASPERA as well as Space Mining Conference.

scholarly journals A Comparative Study on Supervised Machine Learning Algorithms for Copper Recovery Quality Prediction in a Leaching Process

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2119
Author(s):  
Victor Flores ◽  
Claudio Leiva
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Random Forest ◽  
Mining Industry ◽  
Machine Learning Algorithms ◽  
Copper Recovery ◽  
Support Vector ◽  
Copper Mining

The copper mining industry is increasingly using artificial intelligence methods to improve copper production processes. Recent studies reveal the use of algorithms, such as Artificial Neural Network, Support Vector Machine, and Random Forest, among others, to develop models for predicting product quality. Other studies compare the predictive models developed with these machine learning algorithms in the mining industry as a whole. However, not many copper mining studies published compare the results of machine learning techniques for copper recovery prediction. This study makes a detailed comparison between three models for predicting copper recovery by leaching, using four datasets resulting from mining operations in Northern Chile. The algorithms used for developing the models were Random Forest, Support Vector Machine, and Artificial Neural Network. To validate these models, four indicators or values of merit were used: accuracy (acc), precision (p), recall (r), and Matthew’s correlation coefficient (mcc). This paper describes the dataset preparation and the refinement of the threshold values used for the predictive variable most influential on the class (the copper recovery). Results show both a precision over 98.50% and also the model with the best behavior between the predicted and the real values. Finally, the obtained models have the following mean values: acc = 0.943, p = 88.47, r = 0.995, and mcc = 0.232. These values are highly competitive when compared with those obtained in similar studies using other approaches in the context.

scholarly journals A Comparative Study on Supervised Machine Learning Algorithms for Copper Recovery Quality Prediction in a Leaching Process

2021 ◽  
Author(s):  
Victor Flores ◽  
Claudio Leiva
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Random Forest ◽  
Mining Industry ◽  
Machine Learning Algorithms ◽  
Copper Recovery ◽  
Support Vector ◽  
Copper Mining

The copper mining industry is increasingly using artificial intelligence methods to improve cop-per production processes. Recent studies reveal the use of algorithms such as Artificial Neural Network, Support Vector Machine, and Random Forest, among others, to develop models for predicting product quality. Other studies compare the predictive models developed with these machine learning algorithms in the mining industry, as a whole. However, not many copper mining studies published compare the results of machine learning techniques for copper recovery prediction. This study makes a detailed comparison between three models for predicting copper recovery by leaching, using four datasets resulting from mining operations in northern Chile. The algorithms used for developing the models were Random Forest, Support Vector Machine, and Artificial Neural Network. To validate these models, four indicators or values of merit were used: accuracy (acc), precision (p), recall (r), and Matthew’s correlation coefficient (mcc). This paper describes dataset preparation and the refinement of the threshold values used for the predictive variable most influential on the class (the copper recovery). Results show both a precision over 98.50% and also the model with the best behavior between the predicted and the real. Finally, the models obtained show the following mean values: acc=94.32, p=88.47, r=99.59, and mcc=2.31. These values are highly competitive as compared with those obtained in similar studies using other approaches in the context.

scholarly journals Assessment of metallurgical enterprises’ activities in Kazakhstan in the context of international trends

2021 ◽  
Vol 187 (1-2) ◽  
pp. 121-130
Author(s):  
Gulnara Aubakirova ◽  
◽  
Georgiy Rudko ◽  
Farida Isataeva ◽  
◽  
...  
Keyword(s):  
Global Economy ◽  
Business Processes ◽  
Underground Mining ◽  
Technological Development ◽  
International System ◽  
Mining Industry ◽  
Economic Assessment ◽  
Economic Indicators ◽  
International Trends ◽  
Method Of Production

The geological industry of Kazakhstan is transiting to CRIRSCO, the international system of reporting standards for mineral reserves. In view of the set tasks, the problem of adjusting the geological and economic assessment of deposits is being updated in order to adapt it to the international requirements and to increase accessibility and transparency for a potential external investor. This research has been carried out on the basis of the Kazakhmys Corporation LLС, the largest international company engaged in exploring, mining and processing of various minerals. The authors of this paper have made an attempt to expand the geological and economic assessment of the enterprise by digitizing the key business processes. On the basis of the formed factual database of the geological and economic indicators and characteristics of the stratiform pyrite-copper-lead-zinc deposit Kusmuryn, which is part of the Kazakhmys Corporation LLС, the economic indicators of extracting associated components have been calculated. Digital transformation is a key area of technological development of the mining industry in Kazakhstan for the coming years. In this regard, automation of calculating the geological and economic assessment of the investigated field will allow the company not only to reduce investment and operating costs, but also to deepen the internal analytical work to monitor the effectiveness of the applied digital solutions. Transformation of the economy of Kazakhstan presupposes state support for promising regions. The article shows that transition of the Kusmuryn deposit to underground mining in the medium term will accelerate the solution of pressing regional problems and remove social tension in the monotowns adjacent to the deposit. In order to strengthen its position in the global economy, Kazakhstan strives to achieve socio-economic goals in the field of sustainable development. It has been established that changing the method of production and automation of business processes of the Kazakhmys Corporation LLС will have a positive effect on the energy efficiency due to more rational use of available technologies. The research will improve the validity of predictive management decisions to strengthen the financial and economic situation and the international positions of the mining and smelting enterprise.

A Survey on Artificial Intelligence Techniques to Prevent Cyber Crime

2018 ◽  
Vol 8 (5) ◽  
pp. 80
Author(s):  
Juveriya Afreen
Keyword(s):  
Artificial Intelligence ◽  
Cyber Security ◽  
Data Security ◽  
Human Reasoning ◽  
Cyber Crime ◽  
Artificial Intelligence Techniques

Abstract-- With increase in complexity of data, security, it is difficult for the individuals to prevent the offence. Thus, by using any automation or software it’s not possible by only using huge fixed algorithms to overcome this. Thus, we need to look for something which is robust and feasible enough. Hence AI plays an epitome role to defense such violations. In this paper we basically look how human reasoning along with AI can be applied to uplift cyber security.

The Nature of Mining-Induced Horizontal Displacement of Surface on the Example of Several Coal Mines

2014 ◽  
Vol 59 (4) ◽  
pp. 971-986 ◽  
Author(s):  
Krzysztof Tajduś
Keyword(s):  
Underground Mining ◽  
Mining Industry ◽  
Horizontal Displacement ◽  
Surface Slope ◽  
Proportionality Coefficient ◽  
Model Studies ◽  
Displacement Vectors ◽  
Mining Conditions ◽  
Design Projects ◽  
Academy Of Sciences

Abstract The paper presents the analysis of the phenomenon of horizontal displacement of surface induced by underground mining exploitation. In the initial part, the basic theories describing horizontal displacement are discussed, followed by three illustrative examples of underground exploitation in varied mining conditions. It is argued that center of gravity (COG) method presented in the paper, hypothesis of Awierszyn and model studies carried out in Strata Mechanics Research Institute of the Polish Academy of Sciences indicate the proportionality between vectors of horizontal displacement and the vector of surface slope. The differences practically relate to the value of proportionality coefficient B, whose estimated values in currently realized design projects for mining industry range between 0.23r to 0.42r for deep exploitations, whereas in the present article the values of 0.33r and 0.47r were obtained for two instances of shallow exploitation. Furthermore, observations on changes of horizontal displacement vectors with face advancement indicated the possibility of existence of COG zones above the mined-out field, which proved the conclusions of hitherto carried out research studies (Tajduś 2013).

SISTEM PAKAR MENDIAGNOSA VIRUS MAYORA DENGAN METODE VARIABLE CENTERED INTELLIGENT RULE SYSTEM (VCIRS)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Siti Nurhena ◽  
Nelly Astuti Hasibuan ◽  
Kurnia Ulfa
Keyword(s):  
Artificial Intelligence ◽  
Immune System ◽  
Expert System ◽  
Expert Systems ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Visual Basic ◽  
Artificial Intelligence Techniques ◽  
Individual Needs ◽  
Rule System

The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms. Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms. With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms.Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms.With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)

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