Monitoring the fill level of a ball mill using vibration sensing and artificial neural network

Nayak, Dilip Kumar; Das, Debi Prasad; Behera, Santosh Kumar; Das, Sarada Prasad

doi:10.1007/s00521-019-04555-5

Cited by 15 publications

(3 citation statements)

References 28 publications

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“…Sensors with ML algorithms can be used to make sure that the size of rocks entering a mill and on the conveyor belt is not too big to cause failure [56]. ML-based monitoring of sizes [57][58][59][60] and quality (in terms of concentration of economic mineral) [61] of particles are some of the existing works in this domain.…”

Section: And Ai In Comminution and Sizingmentioning

confidence: 99%

AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing

Mishra

2021

Minerals

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In the last few years, jargon, such as machine learning (ML) and artificial intelligence (AI), have been ubiquitous in both popular science media as well as the academic literature. Many industries have tried the current suite of ML and AI algorithms with various degrees of success. Mineral processing, as an industry, is looking at AI for two reasons. First of all, as with other industries, it is pertinent to know if AI algorithms can be used to enhance productivity. The second reason is specific to the mining industry. Of late, the grade of ores is reducing, and the demand for ethical mining (with as little effect on ecology as possible) is increasing. Thus, mineral processing industries also want to explore the possible use of AI in solving these challenges. In this review paper, first, the challenges in mineral processing that can potentially be solved by AI are presented. Then, some of the most pertinent developments in the domain of ML and AI (applied in the domain of mineral processing) are discussed. Lastly, a top-level modus operandi is presented for a mineral processing industry that might want to explore the possibilities of using AI in its processes. Following are some of the new paradigms added by this review. This review presents a holistic view of the domain of mineral processing with an AI lens. It is also one of the first reviews in this domain to thoroughly discuss the use of AI in ethical, green, and sustainable mineral processing. The AI process proposed in this paper is a comprehensive one. To ensure the relevance to industry, the flow was made agile with the spiral system engineering flow. This is expected to drive rapid and agile investigation of the potential of applying ML and AI in different mineral processing industries.

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Section: And Ai In Comminution and Sizingmentioning

confidence: 99%

AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing

Mishra

2021

Minerals

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“…External sensors have been developed to gain some insights into the internal dynamics of mills whilst avoiding this harsh environment. For instance, the acoustic signals of sensors placed on the mill shell were related to features including feed size fraction [4], feed hardness [5], fill level and charge composition [6,7]. Other externally measured quantities such as feed tonnage, bearing pressure, and spindle speed were successfully used to predict the mill energy consumption, including via machine learning methods [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Using tracer particle kinematics to predict particle size in rotating drums

Laudari,

Marks,

Rognon

2024

Preprint

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Comminution is an energy intensive process. In ball mills, it is achieved by rotating a drum in which large metal balls crush ore particles. In-situ monitoring of particle size would be of considerable interest to optimize their operation. However, there is no established solution to measure particle size in such a harsh mechanical environment. We show here that the acceleration of the grinding media, which can be monitored using embedded accelerometers, can be used to sense the particle size during operation. In DEM simulations, we find that a machine learning classifier is able to detect the size of small "ore" particles solely based on the knowledge of the acceleration of larger grinding media particles. Results show that this kinematic sensing is effective over a wide range of particle size ratios, mixture ratio and mill charge. Beyond their practical interest in mineral processing, these results point out that the kinematics of large particles is affected by the size of the smaller particles, an observation which can help advance rheological models for bi-disperse granular flows.

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“…This method can effectively estimate and predict the key parameters which are difficult to measure online in an industrial process [3]. Nayak et al [4] used an accelerometer to collect the vibration signal of the mill base at the laboratory scale and used different transform methods to extract features from the vibration signals, which were adopted as training data for the neural network to predict the mill filling rate. Yan et al [5] proposed an uncertain inference soft measuring model based on a cloud model, which was verified in a small ball mill and successfully applied in the industrial field, effectively improving the accuracy and reliability of the measurement of filling rate [6].…”

Section: Introductionmentioning

confidence: 99%

Soft measurement of ball mill load under variable working conditions based on deep transfer learning

Huang

Guo

Sang

et al. 2022

Meas. Sci. Technol.

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In actual industrial production, the labeled sample data of the ball mill is difficult to be obtained under variable working condition. Aiming at realizing the soft measurement of ball mill load under variable working conditions, a joint discriminative high-order moment alignment network (JDMAN) is proposed based on the deep transfer learning in this paper. In this method, the discriminative features were learned through jointly training the labeled samples belonging to source domain and unlabeled samples belonging to target domain. Simultaneously, the features learned by deep convolution network were aligned and clustered through central moment discrepancy (CMD) and center loss to accomplish transfer. The comparison with other transfer methods indicate that the proposed JDMAN can efficaciously promote the accuracy of soft measurement under variable working conditions.

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Monitoring the fill level of a ball mill using vibration sensing and artificial neural network

Cited by 15 publications

References 28 publications

AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing

AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing

Using tracer particle kinematics to predict particle size in rotating drums

Soft measurement of ball mill load under variable working conditions based on deep transfer learning

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