Application of Gaussian Smoothing Technique in Evaluation of Biomass Pyrolysis Kinetics in Macro-TGA

Pyrolysis is the most fundamental process in thermal chemical conversion of biomass, and pyrolysis kinetic analysis is valuable for the in-depth explore of process mechanism. On the basis of thermal gravity analysis of different kinds of biomass feedstock, thermal kinetics analysis was performed to analyze the pyrolysis behavior of biomass. With the apparent kinetic parameters derived, kinetic model was proposed for the main reaction section of biomass pyrolysis process. The pyrolysis characteristics of three kinds of biomass material were compared in view of corresponding biochemical constitution. Through model simulation of different pyrolysis process, the diversity in pyrolysis behavior of different kinds of biomass feedstock was analyzed, and pyrolysis mechanism discussed. The results derived are useful for the development and optimization of biomass thermal chemical conversion technology.

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Application of Weibull 2-Mixture Model To Describe Biomass Pyrolysis Kinetics

Energy & Fuels ◽

10.1021/ef700514t ◽

2008 ◽

Vol 22 (1) ◽

pp. 675-678 ◽

Cited By ~ 12

Author(s):

Junmeng Cai ◽

Ronghou Liu

Keyword(s):

Mixture Model ◽

Biomass Pyrolysis ◽

Pyrolysis Kinetics

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BIOMASS PYROLYSIS KINETICS: A REVIEW OF MOLECULAR-SCALE MODELING CONTRIBUTIONS

Brazilian Journal of Chemical Engineering ◽

10.1590/0104-6632.20170341s20160086 ◽

2017 ◽

Vol 34 (1) ◽

pp. 1-18 ◽

Cited By ~ 11

Author(s):

J. D. Murillo ◽

J. J. Biernacki ◽

S. Northrup ◽

A. S. Mohammad

Keyword(s):

Biomass Pyrolysis ◽

Pyrolysis Kinetics ◽

Scale Modeling ◽

Molecular Scale

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Denoising in Magnetic Resonance Images using Improved Gaussian Smoothing Technique

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b2859.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 3693-3696

Keyword(s):

Magnetic Resonance ◽

Gabor Filter ◽

Signal To Noise Ratio ◽

Median Filter ◽

Structural Similarity ◽

Magnetic Resonance Images ◽

Smoothing Technique ◽

Imaging Studies ◽

Gaussian Smoothing ◽

Better Than

Magnetic Resonance Images (MRI) are usually prone to noise like Rician and Gaussian noise. It is very difficult to perform image processing functions with the presence of noise. The objective of our work is to investigate the best method for denoising the MRI images. This study included 25 MRI subjects selected from the Open Access Series of Imaging Studies (OASIS) - 3 database. The 25 brain image subjects includes cases of both men and women aged 60 to 80. The input RGB image is first converted to gray scale image in which the contrast, sharpness, shadow and structure of the color of image are preserved. The proposed work uses an improved Gaussian smoothing technique for denoising Magnetic Resonance Images by constructing a modified mask for Gaussian smoothing. The performance of the proposed technique has been compared with various filters like median filter, Gaussian filter and Gabor filter. The performance evaluation was carried out by metrics like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity (SSIM) index. The experimental results show that the Improved Gaussian Smoothing Technique (IGST) performs better than other methods. All experiments were conducted using Scikit Learn version 0.20 and Scikit Image version 0.14.1 under Python version 3.6.7.

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