Machine vision based monitoring and analysis of a coal column flotation circuit

Massinaei, M.; Jahedsaravani, A.; Taheri, Elmira; Khalilpour, Jafar

doi:10.1016/j.powtec.2018.11.056

Cited by 68 publications

(7 citation statements)

References 43 publications

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“…Through the intelligent control system of the working face, the use of visual remote monitoring is used to realize the intelligent operation of coal mining, support, and coal transportation at the working face. Intelligent fully-mechanized caving for extra-thick coal seams is mainly achieved through the use of ultra-large mining height top coal caving hydraulic supports, intelligent coal caving systems, intelligent control of key technologies, etc., to achieve intelligent mining [15,16]. Comprehensive mechanized unmanned coal mining is of great significance to the development of intelligent unmanned mining.…”

Section: The Development History Of Unmanned Mining Technologymentioning

confidence: 99%

A Review of Intelligent Unmanned Mining Current Situation and Development Trend

et al. 2022

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Intelligent unmanned mining is a key process in coal mine production, which has direct impact on the production safety, coal output, economic benefits and social benefits of coal mine enterprises. With the rapid development and popularization of 5G+ intelligent mines and coal mine intelligent equipment in China, the intelligentization of intelligent unmanned mining has become an important research topic. Especially with the promulgation of some Chinese policies and regulations, intelligent unmanned mining technology has become one of the key technologies of coal mine production. To understand the connotation, status quo and development trends of intelligent unmanned mining, this paper takes the relationship between key technologies and engineering application of intelligent unmanned mining in China as the perspective. It is proposed that the intelligent unmanned mining technology is in the whole process of working face mining. A research structure of unmanned follow-up operation and safe patrol is changing to the mode of intelligent adaptive mining, followed by the basic concepts and characteristics of intelligent unmanned mining. Relevant researches that maybe beneficial to the proposed research content are reviewed in four layers, which include basic theory, key technology, mining mode, and overall design system theory and technology. Finally, the current intelligent unmanned mining mode and future trends in this field in China are summarized.

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Section: The Development History Of Unmanned Mining Technologymentioning

confidence: 99%

A Review of Intelligent Unmanned Mining Current Situation and Development Trend

et al. 2022

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“…The methods employed advancements in deep learning techniques and advanced analytical tools with greater robustness to identify and classify defective products without loss of accuracy using system identification modules based on a convolutional neural network (CNN). Massinaei et al [24] investigated the potential of machine vision in monitoring and controlling flotation circuits. The authors developed a machine vision system for a coal column flotation circuit and demonstrated its use at an industrial site with flotation experiments conducted under various operating conditions.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms

et al. 2020

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Machine vision systems are applied in industry to control the quality of production while optimizing efficiency. A machine vision and AI-based inspection of color intensity in transparent Polyethylene Terephthalate (PET) preforms is especially sensitive to backgrounds and lighting, therefore, much attention is given to its illumination conditions. The paper examines the adverse factors affecting the quality of image recognition and presents an adaptive method for reducing the influence of changing illumination conditions in the color inspection process of transparent PET preforms. The method is based on predicting measured color intensity correction parameters according to illumination conditions. To test this adaptive method, a hardware and software system for image capture and processing was developed. This system is capable of inspecting large quantities of preforms in real time using a neural network with a modified gradient descent and momentum algorithm. The experiment showed that correction of the measured color intensity value reduced the standard deviation caused by variable and uneven illumination by 61.51%, demonstrating that machine vision color intensity evaluation is a robust and adaptive solution under illuminated conditions for detecting abnormalities in machine-based PET inspection procedures. INDEX TERMS Image processing, machine vision, neural nets, data mining.

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“…To validate and compare model performance, different validation criteria, such as RMSE and MAPE, were employed. They were used to evaluate the errors of the trained models, MAPE was used to calculate the average percentage difference between the measured and predicted values without consideration of their weight and direction, and RMSE is defined as the square median root the squared difference between measured and predicted values [25][26][27]. The calculations of RMSE and MAPE are based on the following Equations:…”

Section: Prediction Performance Indicatorsmentioning

confidence: 99%

Prediction Model of Suspension Density in the Dense Medium Separation System Based on LSTM

et al. 2020

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In the dense medium separation system of coal preparation plant, the fluctuation of raw coal ash and lag of suspension density adjustment often causes the instability of product quality. To solve this problem, this study established a suspension density prediction model for the dense medium separation system based on Long Short-Term Memory (LSTM). First, the historical data in the dense medium separation system of a coal preparation plant were collected and preprocessed. Moving average and cubic exponential smoothing methods were used to replace abnormal data and to fill in the missing data, respectively. Second, a LSTM network was used to construct the density prediction model, and the optimal number of time steps, hidden layers, and nodes was determined. Finally, the model was employed on a testing set for prediction, and a Back-Propagation (BP) network without a time series was used for comparison. Root Mean Squared Error (RMSE) were the minimum when the number of the hidden layers, nodes, and time steps was 6, 12, and 5, respectively. In this case, the RMSE and Mean Absolute Percent Error (MAPE) of the LSTM method were 0.009 and 0.007, respectively, while those of the BP method were 0.019 and 0.015, respectively. Therefore, the model established using LSTM can be used to accurately predict the suspension density of the dense medium separation system.

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Machine vision based monitoring and analysis of a coal column flotation circuit

Cited by 68 publications

References 43 publications

A Review of Intelligent Unmanned Mining Current Situation and Development Trend

A Review of Intelligent Unmanned Mining Current Situation and Development Trend

An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms

Prediction Model of Suspension Density in the Dense Medium Separation System Based on LSTM

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