Estimation of hydrogen flow rate in atmospheric Ar:H2 plasma by using artificial neural network

Das, Supriyo; Das, Debi Prasad; Sarangi, Chinmaya Kumar; Bhoi, Bhagyadhar

doi:10.1007/s00521-018-3674-z

Cited by 11 publications

(3 citation statements)

References 32 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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“…Multivariate analysis techniques have been widely applied for process diagnostics in low-pressure plasma processing [24,39,40] and more recently in magnetically confined plasma fusion reactors [41,42], but much remains to be done to more fully utilize ML for real-time and quantitative diagnosis of NEPs. A promising application of ML is inference of plasma characteristics from spectral information.…”

Section: Machine Learning For Diagnostics Of Nepmentioning

confidence: 99%

Machine learning for modeling, diagnostics, and control of non-equilibrium plasmas

Mesbah¹,

Graves²

2019

J. Phys. D: Appl. Phys.

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Machine learning (ML) is a set of computational tools that can analyze and utilize large amounts of data for many different purposes. Recent breakthroughs in ML and artificial intelligence largely enabled by advances in computing power and parallel computing present cross-disciplinary research opportunities to exploit some of these techniques in the field of non-equilibrium plasma (NEP) studies. This paper presents our perspectives on how ML can potentially transform modeling and simulation, real-time monitoring, and control of NEP.

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“…To address these issues, supervised machine learning methods are required for spectroscopy analyses. The use of algorithms such as artificial neural network (ANN) [25,26], support vector machine [27][28][29], and k-nearest neighbors [30], has been suggested for this purpose. Utilizing ANN for endpoint detection has been reported in several studies [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Development and testing of an efficient data acquisition platform for machine learning of optical emission spectroscopy of plasmas in aqueous solution

Wang

Hsu

2019

Plasma Sources Sci. Technol.

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This study presents the development of an efficient data acquisition platform and discusses machine learning of optical emission spectroscopy (OES) of plasma in aqueous solution to demonstrate the multivariate analysis of spectra. A specially designed platform for efficient acquisition of spectra emanated from plasmas in solutions is developed, and several machine learning algorithms are tested for plasma analysis. This platform enables acquiring up to 10k spectra solutions with various pH values under constant solution conductivity, or vice versa, within 15 min. This rapid acquisition scheme provides a sufficient dataset for testing machine learning algorithms. We test the OES of plasmas ignited in solutions with designated conductivities with pH of 2.2-5.2. A total 40k spectra are collected and tested with principal component analysis (PCA) and an artificial neural network (ANN) to predict the conductivity of the solution. In PCA, the results show that most data points are overlapped in the score plot constructed using principal components 1 and 2, implying that PCA cannot discriminate the conductivity based on the spectra. In an ANN, several network structures are constructed and tested. The results show that the deep ANN significantly improves the accuracy of conductivity prediction in terms of mean squared error by three orders of magnitude compared with the method of using single emission line. Regularization techniques including dropout and early stopping show promise for mitigating overfitting in a deep ANN. Such improvements suggest that a deep ANN considers the nonlinear behaviors of plasma and can handle datasets with high complexity. In addition, the application of a deep ANN with a large number of parameters makes using this experimental platform for efficient spectra acquisition highly desirable.

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Estimation of hydrogen flow rate in atmospheric Ar:H2 plasma by using artificial neural network

Cited by 11 publications

References 32 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

Machine learning for modeling, diagnostics, and control of non-equilibrium plasmas

Development and testing of an efficient data acquisition platform for machine learning of optical emission spectroscopy of plasmas in aqueous solution

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