Design and development of a machine vision system using artificial neural network-based algorithm for automated coal cha
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Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization Amit Kumar Gorai1 • Simit Raval2 • Ashok Kumar Patel3 • Snehamoy Chatterjee4 Tarini Gautam1
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Received: 7 March 2020 / Revised: 11 June 2020 / Accepted: 22 September 2020 The Author(s) 2020
Abstract Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and converted into five other color spaces (HSI, CMYK, Lab, xyz, Gray) for feature extraction. The intensity component image of HSI color space was further transformed into four frequency components (discrete cosine transform, discrete wavelet transform, discrete Fourier transform, and Gabor filter) for the texture features extraction. A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development. The datasets of the optimized features were used as an input for the model, and their respective coal characteristics (analyzed in the laboratory) were used as outputs of the model. The R-squared values were found to be 0.89, 0.92, 0.92, and 0.84, respectively, for fixed carbon, ash content, volatile matter, and moisture content. The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression, support vector regression, and radial basis neural network models. The study demonstrates the potential of the machine vision system in automated coal characterization. Keywords Coal characterization Machine vision system Artificial neural network Gaussian process regression
1 Introduction
& Amit Kumar Gorai [email protected] 1
Department of Mining Engineering, National Institute of Technology, Rourkela 769008, India
2
School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW 2052, Australia
3
Department of Computer Science & Engineering, C. V. Raman Global University, Bhubaneswar, Odisha 752054, India
4
Department of Geological and Mining Engineering and Science, Michigan Technological University, Houghton, MI 49931, USA
Coal is the most widely used fossil fuel energy resource in the world since industrialization. In most of the countries, it continues to play an essential role in the production and supply of energy. Coal is heterogeneous in nature and formed from decomposed plant materials. It includes different constituents called macerals, only grouped by its specific course of action of physical properties, compound structure, and morphology. According to World Energy Council (WEC 2016), over 7800 million tons of coal are consumed by a variety of sectors like power generation, steel production, cement ind
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