Multiple-distribution DAEM modelling of spruce pyrolysis: An investigation of the best trade-off regarding the number and shape of distributions

The distributed activation energy model (DAEM) is a widely used, accurate and robust method to model biomass pyrolysis. However, the appropriate numerical strategy in terms of distribution number and shape has not been systematically determined. This study analysed spruce powder pyrolysis under different scenarios of multiple-distribution DAEMs with symmetric/asymmetric distributions (Gaussian, logistic and exponential) and different distribution numbers. Dynamic tests at four heating rates (1, 2, 5 and 10 degrees C/min up to 800 degrees C) provided solid numerical learning database, and the optimization of residues between numerical calculation and database enabled identification of model parameters. Subsequently, validation was performed with static tests (250 to 500 degrees C with an interval of 50 degrees C and 2 h-isothermal stages), and their corresponding residue analysis provided a fundamental basis to assess the model's true prediction ability. The trade-off between the model's prediction ability and degrees of freedom was robustly investigated with regard to the number and shape of the distribution. As stated by the quality of validations, a series of Gaussian-DAEMs (distribution number ranged from one to five) allowed for the determination of the best trade-off when the distribution number was three. Finally, the two-Gaussian plus one exponential distribution exhibited the best overall prediction capacity among different multiple-distribution DAEMs, and was confirmed as the best strategy with regard to both distribution shape and number. A DTG simulation investigated each model's simulation effects with three assigned variation sections and justified the correspondence between pseudo-components and biomass constituents. Finally, the DAEM's capability to distinguish the effects of heating rate was demonstrated.

Automated summary

The study analyzed the performance of multiple-distribution DAEM models in predicting biomass pyrolysis, determining that the two-Gaussian plus one exponential distribution was the best strategy in terms of both distribution shape and number. Validation tests and DTG simulations confirmed the model's ability to accurately predict pyrolysis behaviors and the effects of heating rate.