Gaussian Smoothing Strategy of Thermogravimetric Data of Biomass Materials in an Air Atmosphere

Abstract

In differential analysis, noises may induce high fluctuations in the differential thermogravimetric curve (DTG) curve and thereby induce considerable errors in the evaluation of kinetic parameters. This paper studies the strategy of using the Gaussian smoothing algorithm in the pretreatment of biomass decomposition data. Special attention is paid to the choice of the smoothing parameter a in both mathematical and kinetic senses. A mathematical optimum range (MOR), 3−5 ≤ a ≤ 10, is definitely suggested to achieve a high enough mathematical smoothing degree while keeping the data distortion at a low level. By simulation analysis, it is found that, in the range of MOR, lower values of a can achieve the best fit to the expected DTG peaks. The smoothing parameters within the central section of 3−10 are highly suggested to achieve the best kinetic parameter fitting. Higher values of a are required for the transition and end regions. A two-stage Gaussian smoothing strategy for biomass decomposition data is forma...