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Journal ArticleDOI: 10.1080/19392699.2018.1458716

Prediction of the Ash Content of Flotation Concentrate Based on Froth Image Processing and BP Neural Network Modeling

04 Mar 2021-International Journal of Coal Preparation and Utilization (Taylor & Francis)-Vol. 41, Iss: 3, pp 191-202
Abstract: The concentrate ash content is the most effective evaluation index in the flotation process. However, the ash content obtained by the current technology has a serious hysteresis. In this study, a r...

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6 results found


Journal ArticleDOI: 10.1016/J.POWTEC.2020.07.040
Zelin Zhang1, Yang Liu1, Qi Hu1, Zhiwei Zhang1  +3 moreInstitutions (1)
01 Sep 2020-Powder Technology
Abstract: Online multi-information detection of mineral properties and composition plays a vital role in the realization of digital mining and digital concentrating mill, and the way of machine vision technology is put forward as a cost-effective and safe approach at present. This paper presents an exploratory study employing a bench-scale approach to detect the multi-information of coal quality online by machine vision simultaneously, including particle size distribution, density distribution, the ash content of each density fraction, and the total ash content. Firstly, we adopt a Finite-Erosion-and-Exact-Dilation (FEED) algorithm and a particle-on-edge region segmentation algorithm to segment overlapped particles and ensure the full analysis of target regions. Moreover, twenty-nine features are extracted and optimized to enable the particle mass estimation model, particle size characterization, classification model of density fraction, and prediction model of ash content to be implemented. Finally, an experimental study shows the merits of the proposed approach, and the average prediction errors of size distribution, density distribution, and ash content of each density fraction are 1.85%, 2.57%, 3.36%, respectively. The total ash content error is 2.54%. Results derived using the proposed approach reveal that it has the potential to be applied to the coal processing industry.

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Topics: Machine vision (51%)

13 Citations


Open accessJournal ArticleDOI: 10.5334/DSJ-2019-036
Abstract: With the rapid development of society, the real estate economy, as an important part of Chinese economy, is showing a growing trend. But it is also the most likely to generate bubble economy, causing financial risks; it will trigger a series of social contradictions and cause social unrest in severe cases. Therefore, it is urgent to improve and optimize the real estate evaluation model. In this study, the real estate was evaluated based on the neural network model optimized by genetic algorithm. Through sorting out and summarizing the real estate data in a period of time, the corresponding model was established and the test data were obtained. The average relative error value of the genetic algorithm optimized neural network model was 3.552, which was smaller than that of the Back-Propagation (BP) neural network prediction model. The experimental conclusion that the new network model was better than the traditional model was obtained. This work opens up a new route of real estate evaluation.

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Topics: Real estate (61%), Network model (53%)

4 Citations


Journal ArticleDOI: 10.1080/15567036.2019.1677807
Abstract: In the last few decades, many studies have been performed with the main hope of utilizing imaging methods so as to detect static (bubble size and shape, color, texture) and dynamic (velocity and st...

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Topics: Texture (geology) (55%), Froth flotation (54%)

4 Citations


Journal ArticleDOI: 10.1080/15567036.2019.1663313
Abstract: Digital image processing system is a promising technology for obtaining process-related information and well accepted in mineral processing industries as a fast, non-invasive and low-cost tool for ...

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2 Citations


Open accessJournal ArticleDOI: 10.3390/PR9081475
23 Aug 2021-
Abstract: In this paper, focusing on the inconvenience of variable value PID based on manual parameter adjustment for the hydraulic drive unit (HDU) of a legged robot, a method employing double-layer back propagation (BP) neural networks for learning the law of PID control parameters is proposed. The first layer is used to learn the relationship between different control parameters and the control performance of the system under various working conditions. The second layer is used to study the relationship between the parameters of the working conditions and the optimizing control parameters under various working conditions. The effectiveness of the proposed control method was verified by simulation and experiment. The results showed that the proposed method can provide a theoretical and experimental basis for the selection of control parameters, and can be extended to similar controllers, therefore possessing engineering application value.

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19 results found


Journal ArticleDOI: 10.1016/J.JPROCONT.2010.11.001
Luis Bergh1, Juan Yianatos1Institutions (1)
Abstract: Flotation processes are very complex, and after more than one hundred years of history, there are few reports on applications of novel techniques in monitoring and control of flotation units, circuits and global plants. On the other hand, the successful application of multivariate predictive control on other processes is well known. In this paper, an analysis on how the characteristics of flotation processes, the quality of measurements of key variables, and the general lack of realistic dynamic models, are delaying the appropriate use of predictive control. In this context, the applications of multivariate statistics, such as PCA, to model the relationship between operating data for on-line diagnosis and fault detection and to build causal models are discussed. Also the use of PLS models to predict target variables for control purposes, is presented. Results, obtained at pilot and industrial scales, are discussed, introducing new ideas on how to obtain more valuable information from the usual available operating data of the plant, and particularly from froth images.

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235 Citations


Journal ArticleDOI: 10.1016/J.MINPRO.2010.04.005
Abstract: Research and development into the application of machine vision in froth flotation systems has continued since its introduction in the late 1980s. Machine vision is able to accurately and rapidly extract froth characteristics, both physical (e.g. bubble size) and dynamic (froth velocity) in nature, from digital images and present these results to operators and/or use the results as inputs to process control systems. Currently, machine vision has been implemented on several industrial sites worldwide and the technology continues to benefit from advances in computer technology. Effort continues to be directed into linking concentrate grade with measurable attributes of the froth phase, although this is proving difficult. As a result other extracted variables, such as froth velocity, have to be used to infer process performance. However, despite more than 20 years of development, a long-term, fully automated control system using machine vision is yet to materialise. In this review, the various methods of data extraction from images are investigated and the associated challenges facing each method discussed. This is followed by a look at how machine vision has been implemented into process control structures and a review of some of the commercial froth imaging systems currently available. Lastly, the review assesses future trends and draws several conclusions on the current status of machine vision technology.

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Topics: Machine vision (57%), Computer technology (53%)

170 Citations


Journal ArticleDOI: 10.1016/J.MINPRO.2011.05.002
B. Shean1, Jan J. Cilliers1Institutions (1)
Abstract: The last few decades have seen major advances in instrumentation and technology, and simplifications and modifications of new flotation plant designs. This has allowed for significant developments in process control. In particular, the development of base level process control (control of pulp levels, air flowrates, reagent dosing, etc.) has seen significant progress. Long-term, automated advanced and optimising flotation control strategies have, however, been more difficult to implement. It is hoped that this will change as a result of the development of new technologies such as machine vision and the measurement of new control variables, such as air recovery. This review looks at each of the four essential levels of process control (instrumentation, base level flotation control, advanced flotation control and optimising flotation control) and examines current and future trends within each sub-level.

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Topics: Froth flotation (54%), Process control (50%)

157 Citations


Journal ArticleDOI: 10.1016/S0098-3004(00)00153-9
Sean Thompson1, Frank Fueten2, David Bockus1Institutions (2)
Abstract: An artificial neural network is used for the classification of minerals. Optical data using thin sections is acquired using the rotating polarizing microscope stage, which extracts a basic set of seven primary images during each sampling. A selected set of parameters based on hue, saturation, intensity and texture measurements are extracted from the segmented minerals within each data set. Parameters such as pleochroism, plane light hue, and gradient homogeneity were a few that proved to yield class-discriminating properties. Texture parameters are shown to have the ability to classify colourless minerals. The neural network is trained on manually classified mineral samples. The most successful artificial network to date is a three-layer feed forward network using generalized delta error correction. The network uses 27 distinct input parameters to classify 10 different minerals. Testing the network on previously unseen mineral samples yielded successful results as high as 93%.

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76 Citations


Open accessJournal ArticleDOI: 10.1016/J.MINENG.2014.08.003
Abstract: It is now generally accepted that froth appearance is a good indicative of the flotation performance. In this paper, the relationship between the process conditions and the froth features as well as the process performance in the batch flotation of a copper sulfide ore is discussed and modeled. Flotation experiments were conducted at a wide range of operating conditions (i.e. gas flow rate, slurry solids%, frother/collector dosage and pH) and the froth features (i.e. bubble size, froth velocity, froth color and froth stability) along with the metallurgical performances (i.e. copper/mass/water recoveries and concentrate grade) were determined for each run. The relationships between the froth characteristics and performance parameters were successfully modeled using the neural networks. The performance of the developed models was evaluated by the correlation coefficient (R) and the root mean square error (RMSE). The results indicated that the copper recovery (RMSE = 2.9; R = 0.9), concentrate grade (RMSE = 1.07; R = 0.92), mass recovery (RMSE = 1.94; R = 0.94) and water recovery (RMSE = 3.07; R = 0.95) can be accurately predicted from the extracted surface froth features, which is of central importance for control purposes.

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Topics: Froth flotation (65%)

62 Citations