Variable importance assessments of an innovative industrial-scale magnetic separator for processing of iron ore tailings

Tohry, A.; Jafari, M; Farahani, Minoo Vasheghani; Manthouri, Mohammad; Chelgani, S. Chehreh

doi:10.1080/25726641.2020.1827674

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…SensAI methods are also showing promising results in other areas of the concentration stage [70][71][72]. Dewatering is used to reduce the water content in the outputs from the concentration stage.…”

Section: And Ai In Concentration and Dewateringmentioning

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 Concentration and Dewateringmentioning

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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Modeling metallurgical responses of coal Tri-Flo separators by a novel BNN: a “Conscious-Lab” development

Alidokht

Yazdani

Hadavandi

et al. 2021

Int J Coal Sci Technol

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Tri-flo cyclone, as a dense-medium separation device, is one of the most typical environmentally friendly industrial techniques in the coal washery plants. Surprisingly, no detailed investigation has been conducted to explore the effectiveness of tri-flo cyclone operating parameters on their representative metallurgical responses (yield and recovery). To fill this gap, this work for the first time in the coal processing sector is going to introduce a type of advanced intelligent method (boosted-neural network “BNN”) which is able to linearly and nonlinearly assess multivariable correlations among all variables, rank them based on their effectiveness and model their produced responses. These assessments and modeling were considered a new concept called “Conscious Laboratory (CL)”. CL can markedly decrease the number of laboratory experiments, reduce cost, save time, remove scaling up risks, expand maintaining processes, and significantly improve our knowledge about the modeled system. In this study, a robust monitoring database from the Tabas coal plant was prepared to cover various conditions for building a CL for coal tri-flo separators. Well-known machine learning methods, random forest, and support vector regression were developed to validate BNN outcomes. The comparisons indicated the accuracy and strength of BNN over the examined traditional modeling methods. In a sentence, generating a novel BNN within the CL concept can apply in various energy and coal processing areas, fill gaps in our knowledge about possible interactions, and open a new window for plants' fully automotive process.

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Selectivity index and separation efficiency prediction in industrial magnetic separation process using a hybrid neural genetic algorithm

et al. 2021

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It is essential to know the process efficiency in the industrial magnetic separation process under different operating conditions because it is required to control the process parameters to optimize the process efficiency. To our knowledge, there is no information about using artificial intelligence for modeling the magnetic separation process. Hence, finding a robust and more accurate estimation method for predicting the separation efficiency and selectivity index is still necessary. In this regard, a feed-forward neural network was developed to predict the separation efficiency and selectivity index. This model was trained to present a predictive model based on the percentage of iron, iron oxide and sulfur in mill feed and cobber feed, 80% passing size in mill feed and cobber feed and plant capacity. Therefore, this work aims to develop an intelligent technique based on an artificial neural network and a hybrid neural-genetic algorithm for modeling the concentration process. Results indicated that the values of mean square error and coefficient of determination for the testing phase were obtained 0.635 and 0.86 for selectivity index and of 4.646 and 0.84 for separation efficiency, respectively. In order to improve the performance of neural network, genetic algorithm was used to optimize the weights and biases of neural network. The results of modeling with GA-ANN technique indicated that the mean square error and coefficient of determination for the testing phase were achieved by 0.276 and 0.95 for selectivity index and of 1.782 and 0.92 for separation efficiency, respectively. The other statistical criteria for the GA-ANN model were better than those of the ANN model.

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Variable importance assessments of an innovative industrial-scale magnetic separator for processing of iron ore tailings

Cited by 6 publications

References 26 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

Modeling metallurgical responses of coal Tri-Flo separators by a novel BNN: a “Conscious-Lab” development

Selectivity index and separation efficiency prediction in industrial magnetic separation process using a hybrid neural genetic algorithm

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