The Pyrolysis of Waste Biomass Investigated by Simultaneous TGA-DTA-MS Measurements and Kinetic Modeling with Deconvolution Functions

As waste biomass from fruit processing industry, apricot kernel shells have a potential for conversion to renewable energy through a thermo-chemical process such as pyrolysis. However, due to major differences of biomass characteristics as the well-known issue, it is extremely important to perform detailed analysis of biomass samples from the same type (or same species) but from different geographical regions. Regarding full characterization of considered biomass material and to facilitate further process development, in this paper, the advanced mathematical model for kinetic analysis was used. All performed kinetic modeling represents the process kinetics developed and validated on thermal decomposition studies using simultaneous thermogravimetric analysis (TGA) – differential thermal analysis (DTA) – mass spectrometry (MS) scanning, at four heating rates of 5, 10, 15 and 20 °C min−1, over temperature range 30–900 °C and under an argon (Ar) atmosphere. Model-free analysis for base prediction of decomposition process and deconvolution approach by Fraser-Suzuki functions were utilized for determination of effective activation energies (E), pre-exponential factors (A) and fractional contributions (φ), as well as for separation of overlapping reactions. Comparative study of kinetic results with emission analysis of evolved gas species was also implemented in order to determine the more comprehensive pyrolysis kinetics model. Obtained results strongly indicated that the Fraser-Suzuki deconvolution provides excellent quality of fits with experimental ones, and could be employed to predict devolatilization rates with a high probability. From energy compensation effect properties, it was revealed the existence of unconventional thermal lag due to heat demand by chemical reaction. © Springer Nature Switzerland AG 2020.In: Mitrovic N., Milosevic M., Mladenovic G. (eds) Computational and Experimental Approaches in Materials Science and Engineering. CNNTech 2018. Lecture Notes in Networks and Systems, vol 90. Springer, Cha