Tesina di "Energia da biomasse e rifiuti" - Applicazione di un modello di energia di attivazione distribuita (DAEM) alla pirolisi di diverse biomasse

Application of a Distributed Course of Energy from Biomass and Waste

Where, E is the global volatile quantity of the material, Ea is the mean activation energy, and σ is the standard deviation of the activation energy. This approach avoids the low values of the activation energies which result when a single first-order reaction model is applied to fit a temperature dependence that arises from the occurrence of different reactions in different temperature intervals [17].

References:

1. [17] C. Di Blasi, Modeling Chemical and Physical Processes of Wood and Biomass Pyrolysis. Progress in Energy and Combustion Science, 2008. 34(1): p. 47-90. DOI: 10.1016/j.pecs.2006.12.001.
2. [42] D.B. Anthony and J.B. Howard, Coal Devolatilization and Hydrogasification. AICHE, 1976. 22(4): p. 625-656.
3. [43] Federica Lippi Enrico Biagini, Luigi Petarca, Leonardo Tognotti, Devolatilization Rate of Biomasses and Coal-Biomass Blends: An Experimental Investigation. Fuel, 2002. 81: p. 1041-1050.

Activation Energy Model (DAEM) to the pyrolysis of different biomasses

Students: Lorenzo Catanzani (code: 279775) & Samuele Trinari (code: 280861)

3 ­ TGA and DTGA diagrams

The following charts show the experimental data provided by thermogravimetric analysis (TGA) and the plot of dm/dT (DTGA) that must show the peak of the single component of the samples.

The thermogravimetric analysis of the two materials are made in different times with different instruments.

Fig.3.1. TGA (left) and DTGA (right) for the glycerol pellet

Fig.3.2. TGA (left) and DTGA (right) for the olive stone

From a quick view of the DTGA plots it’s possible to notice for each curve that are present different peaks. These peaks represent the single components of the source material, in the present case, that are cellulose, hemicellulose, lignin and only for the plot of glycerol pellet, the gycerol as well.

11Course of Energy from biomass and wasteApplication of a Distributed Activation Energy Model (DAEM) to the

(Eβ)α βWhere, and are constants dependent on the reacting material. For illustration of thedistributed activated energy model (DAEM), Figures 4.1 through 4.4 describe the method forestablishing the kinetic parameters during the pyrolysis of glycerol pellet . For different heatingrates ranging from 5 °C/min to 40 °C/min, Figure 4.1 describes the linear relationship between ln​​ 2(H/T ) and 1/T at various conversions. The idea is that, with increase in heating rates, thetemperature required to attain a particular conversion increases and hence, the kineticparameters can be determined at each conversion point. 12Course of Energy from biomass and wasteApplication of a Distributed Activation Energy Model (DAEM) to the pyrolysis of different biomassesStudents: Lorenzo Catanzani (code: 279775) & Samuele Trinari (code: 280861)

conversion points using DAEM. Once the activation energies at selected conversions are determined, the relationship between conversion (V/Vf) and activation energies needs to be established through a plot of V/Vf vs E as shown in Figure 4.2. The relationship between V/Vf and E is fitted using a logistic distribution curve using Equation 4.3:

A - AV = A + 1/2pE^2V / (1 + (E/E0))

where A1 and A2 are the initial and final conversion points, E0 is the mean activation energy and p is a constant. The values of these constants are obtained by fitting the experimental data with Equation 4.3.

Course of Energy from biomass and waste

Application of a Distributed Activation Energy Model (DAEM) to the pyrolysis of different biomasses

Students: Lorenzo Catanzani (code: 279775) & Samuele Trinari (code: 280861)

Figure 4.2: Plot for estimating the relationship between V/Vf and activation energy for pyrolysis of glycerol pellet using DAEM.

The two data sets show an important difference between the

activaction energy ranges of 0<E<200and 300<E<350. It has been hypothesized that the cause can be the presence of the glycerol asfourth component. So other analysis have been conducted with other materials like the olivekernel.

Once the relationship between V/Vf and E is established and the unknown constants obtained,Equation 4.3 can be differentiated with respect to E to obtain the values for the function f (E).Finally, a plot of the obtained f (E) values with respect to the activation energy shown in Figure4.3, can be fitted using a Gaussian distribution function and that the complex devolatilizationreaction kinetics of carbonaceous materials may not be represented by only a single first orderreaction but, the reaction is made up of several parallel reactions occurring simultaneously withincreasing temperatures.

Figure 4.3: Plot for estimating the relationship between f (E) and activation energy for pyrolysis of glycerol pellet using DAEM.

Course of Energy from biomass

Application of the DAEM model to olive stone

The procedure used is the same of the previous chapter. It follows the charts of the main results, obtained by the DAEM model.

Figure 5.1: Plot for estimating the activation energy and Arrhenius constant for pyrolysis of olive stone at various heating rates and conversion points using DAEM.

Figure 5.2: Plot for estimating the relationship between V/Vf and activation energy for pyrolysis of olive stone using DAEM.

Course of Energy from biomass and waste

Application of a Distributed Activation Energy Model (DAEM) to the pyrolysis of different biomasses

Figure 5.3: Plot for estimating the relationship between f (E) and activation energy for pyrolysis of olive stone using DAEM.

Figure 5.4: Comparison between experimental and calculated devolatilization weight loss of

Olive stone with increasing temperature using DAEM. The comparison between the results is reported in the next section.

Course of Energy from biomass and waste

Application of a Distributed Activation Energy Model (DAEM) to the pyrolysis of different biomasses

Students: Lorenzo Catanzani (code: 279775) & Samuele Trinari (code: 280861)

Conclusions

The results of this MATLAB model are obtained taking as input data a .xls file, shown in appendix, relating to the glycerol pellet and olive stone provided by a thermogravimetric analysis. These results reflect trends reported by some reviews [18]. Nevertheless better results would be achievable if DAEM is used for single components (glycerol, cellulose, hemicelllulose, lignin). However this procedure is a good starting basis for further development.

The results shown that the model is applicable to different materials although with different accuracy.

References

[18] Bhagavatula, Abhijit, "THERMO-CHEMICAL CONVERSION OF COAL-BIOMASS BLENDS: KINETICS MODELING OF PYROLYSIS, MOVING BED GASIFICATION AND STABLE CARBON ISOTOPE ANALYSIS" (2014). Theses and Dissertations-Chemical and Materials Engineering. Paper 43. http://uknowledge.uky.edu/cme_etds/437

Appendix

Course of Energy from biomass and waste

Application of a Distributed Activation Energy Model (DAEM) to the pyrolysis of different biomasses

Students: Lorenzo Catanzani (code: 279775) & Samuele Trinari (code: 280861)

A: dataset used for the glycerol pellet 19

Course of Energy from biomass and waste

Application of a Distributed Activation Energy Model (DAEM) to the pyrolysis of different biomasses

Students: Lorenzo Catanzani (code: 279775) & Samuele Trinari (code: 280861)

20

Course of Energy from biomass and waste

Application of a Distributed Activation Energy Model (DAEM) to the pyrolysis of different biomasses

Students: Lorenzo Catanzani (code: 279775) & Samuele Trinari (code: 280861)

Dataset used for the olive stone 21

Course of Energy from biomass and waste

Application of a Distributed Activation Energy Model (DAEM) to the pyrolysis of different biomasses

Students: Lorenzo Catanzani (code: 279775) & Samuele Trinari (code: 280861)

22

Course of Energy from biomass and waste

Application of a Distributed Activation Energy Model (DAEM) to the pyrolysis of diffe