Analytical Semi-Empirical Model for the Prediction of Products Yields at the Fast Pyrolysis of Waste Palm Oil

The interest for vegetable oils, as a source of fuels and chemicals, boosted in the last two decades amid concerns related to oil depletion. The fast pyrolysis is a process of industrial consequences. In the present study, original experimental data in fast pyrolysis are processed in order to find the parameters of ASEM- an analytical semi-empirical model developed by the Clean Combustion Technology Laboratory (CCTL), University of Florida. The parameters, constants and coefficients of the model equation (w, To, D, p and q), were calculated for each main product with satisfactory accuracy and results were compared with other authors�. The results were discussed based on similarities and differences between slow and fast pyrolysis. The conclusion was that ASEM is a valid model which can be applied with confidence in very different process conditions.

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Steam gasification of char from wood chips fast pyrolysis: Development of a semi-empirical model for a fluidized bed reactor application

Biomass and Bioenergy ◽

10.1016/j.biombioe.2015.03.011 ◽

2015 ◽

Vol 77 ◽

pp. 64-74 ◽

Cited By ~ 8

Author(s):

S. Septien ◽

F.J. Escudero Sanz ◽

S. Salvador ◽

S. Valin

Keyword(s):

Fluidized Bed ◽

Empirical Model ◽

Fast Pyrolysis ◽

Fluidized Bed Reactor ◽

Steam Gasification ◽

Wood Chips ◽

Reactor Application ◽

Semi Empirical

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A Semi-Empirical Model for Predicting Frost Properties

Processes ◽

10.3390/pr9030412 ◽

2021 ◽

Vol 9 (3) ◽

pp. 412

Author(s):

Shao-Ming Li ◽

Kai-Shing Yang ◽

Chi-Chuan Wang

Keyword(s):

Thermal Conductivity ◽

Linear Programming ◽

Numerical Model ◽

Empirical Model ◽

Quantitative Method ◽

Predictive Ability ◽

Dimensionless Temperature ◽

Crystal Shape ◽

Proposed Model ◽

Semi Empirical

In this study, a quantitative method for classifying the frost geometry is first proposed to substantiate a numerical model in predicting frost properties like density, thickness, and thermal conductivity. This method can recognize the crystal shape via linear programming of the existing map for frost morphology. By using this method, the frost conditions can be taken into account in a model to obtain the corresponding frost properties like thermal conductivity, frost thickness, and density for specific frost crystal. It is found that the developed model can predict the frost properties more accurately than the existing correlations. Specifically, the proposed model can identify the corresponding frost shape by a dimensionless temperature and the surface temperature. Moreover, by adopting the frost identification into the numerical model, the frost thickness can also be predicted satisfactorily. The proposed calculation method not only shows better predictive ability with thermal conductivities, but also gives good predictions for density and is especially accurate when the frost density is lower than 125 kg/m3. Yet, the predictive ability for frost density is improved by 24% when compared to the most accurate correlation available.

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