Investigation of Kinetic and Thermodynamic Parameters of Sapodilla Leaves Pyrolysis Using Coats Redfern Model

doi:10.21203/rs.3.rs-4956634/v1

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Investigation of Kinetic and Thermodynamic Parameters of Sapodilla Leaves Pyrolysis Using Coats Redfern Model

https://doi.org/10.21203/rs.3.rs-4956634/v1

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The determination of the energy potential of indigenous biomass waste is primarily the focus of every country in its quest to meet the energy demand via clean and sustainable sources. The objective of the current study is to investigate the thermo-kinetic behavior of the sapodilla leaves with thermogravimetric analysis to analyze its potential utilization for energy production through the thermochemical conversion process. Sapodilla leaves were dried, pulverized, and heated non-isothermally from room temperature to 900°C at different heating rates in an inert environment. Thermogravimetric results indicated that the Sapodilla leaves pyrolysis is divided into three stages. The Coats-Redfern integral model was applied in the 2nd and 3rd stages to estimate the numerical value of thermo-kinetic parameters, using five major solid-state reaction mechanisms. The low-temperature stable components (LTSC) degraded in the temperature range of 250–550°C while high-temperature stable components (HTSC) decomposed in the temperature range of 550–850°C. About 45-50wt% degradation in the first temperature range of 250–550°C and about 15-17wt% degradation was observed in 2nd degradation temperature range of 550–900°C. According to the results, the diffusion model D₃ showed the highest regression coefficient in the first degradation stage while in the second degradation stage, the nucleation models N_1.5 and N₂ showed the highest regression coefficient. The activation energy of sapodilla leaves degradation was found in the range between 30–32 kJmol^− 1 for best-fitted models in low-temperature regions and 8–12 kJmol^− 1 in high-temperature regions. The positive value of change in enthalpy and Gibbs free energy showed that the reaction is endothermic while the negative value of entropy showed the more regular arrangement of the products. Thermo-kinetic behavior of sapodilla leaves hints toward their huge potential for bio-oil and syngas production through pyrolysis and gasification processes.

Renewable Resources

Thermal degradation

Kinetics

Biomass

Coats Redfern model

Sapodilla leaves

Thermogravimetric analysis

The rapid increase in population is devouring already strained energy resources of this planet [1]. The energy demand of the world is estimated to increase by about 28% in 2040 and Asia is expected to contribute a significant part to this increased world energy demand owing to its fastest population growth [2–6]. Biomass, a prominent class of renewable energy source, has the added advantage of sweeping anthropogenic CO₂ by consuming it during photosynthesis reaction and hence serves as a sink to capture CO₂ [7, 8]. Converting the biomass into energy-dense products and valuable chemicals using thermochemical conversion approaches such as pyrolysis, gasification, and liquefaction may serve as an alternative not only to substitute fossil fuels partially but also as an efficient way of biomass waste utilization [6–12]

Sapodilla (Chico), a fruit abundantly available in Pakistan, has its origin in Mexico and is native to Central America. In Pakistan, Sapodilla is cultivated in the coastal areas of Baluchistan Hub, Kathore and Windor. In Sindh, it is cultivated in Karachi, Malir, Thatta, Hyderabad, Nawab Shah, and Mirpur Khas districts. With the huge production of this plant, its waste (like leaves) is going to increase inevitably as well. Effective utilization of this waste may encourage further production of this useful plant. With no data available on the thermal conversion of Sapodilla leaves, understanding the thermal degradation and kinetic behavior of this biomass is the focus of this study.

Thermal decomposition behavior of biomass helps us to understand the process chemistry and kinetic parameters of biomass utilization and hence reaction parameters for a thermochemical conversion process [4, 13–19]. A major problem in developing kinetic parameters for biomass thermal degradation is the structural heterogeneity which makes it difficult to predict the exact model for determining kinetic parameters. Two types of approaches namely model fitting and model free have been applied by many researchers to investigate the kinetic parameters of solid feedstocks [20–23]. This study employed a model fitting approach, using the Coats Redfern method, for the investigation of activation energy, collision frequency and thermodynamic parameters of Sapodilla leaves degradation by the thermogravimetric analyzer.

2.1 Sample Collection and Characterization

Sapodilla leaves were collected from a house backyard and, dried, ground to fine particles of about 250 µm size by using a biomass grinder and stored in air-tight bags for subsequent use. Proximate analysis was performed by using ASTM standard procedures (moisture, volatile matter, and ash were determined by ASTM-E-872, ASTM-E-1755, ASTM-D-3172, and ASTM-D-5291 respectively). The weight percentage of fixed carbon was calculated by subtracting the sum of moisture, volatile matter, and ash from 100% Ultimate analysis was performed by using Elemental Analyzer (PerkinElmer inc, Hopkinton, USA).

2.2 Thermogravimetric Analysis

The thermal degradation behavior of sapodilla leaves pyrolysis was performed at different heating rates of 10°C min^− 1, 20°C min^− 1, 30°C min^− 1, and 40°C min^− 1 with 10 min hold at 110°C and 900°C under nitrogen atmosphere by the thermogravimetric analyzer (Leco TGA 701). Nitrogen gas flow rate was kept constant at 3.5 L min^− 1 and temperature was increased from room temperature to 900°C.

2.3 Kinetic Study

The kinetic analysis of sapodilla leaves pyrolysis was performed using Arrhenius law:

$\:\frac{d\propto\:}{dt}$ = k(T) f($\:\propto\:$) (1)

Where:

$$\:\propto\:\:=\:\frac{{m}_{0}\:-\:{m}_{i}}{{m}_{0}\:-\:{m}_{f}}$$

m₀ is the initial mass of the sample taken for thermogravimetric analysis, m_i is the mass of the sample at a specific time during pyrolysis and m_f is the final residual mass of the sample.

And k(T) = A exp (-$\:\frac{Ea}{RT}$) (3)

A is a pre-exponential factor (collision frequency, min^− 1), E_a is activation energy (kJ mol^− 1), R is a universal gas constant (0.008314 kJ mol^− 1 k), and T is absolute reaction temperature (k).

For a constant heating rate

$$\:\beta\:=\:\frac{dT}{dt}$$

So, equations (1) and (2) in combined form can be written as

$\:\frac{d\alpha\:}{dT}$ = $\:\frac{A}{\beta\:}$ exp (-$\:\:\frac{Ea}{RT}$) f($\:\alpha\:$) (4)

$$\:g\left(a\right)=\:{\int\:}_{0}^{a}\frac{da}{f\left(a\right)}$$

$$\:g\left(a\right)=\:\frac{A}{\beta\:}\underset{T0}{\overset{T}{\int\:}}(\:-\frac{Ea}{RT}\left)\:\text{d}\text{T}\:\right(5)$$

Where g($\:\alpha\:$) is an integral form of the reaction model. The solution of the above equation is impossible by analytical method hence various approximation models are in use.

2.4 Coats Redfern model

The Coats-Redfern model is extensively used for the determination of kinetic parameters such as the pre-exponential factor and activation energy of the sample. The basic equation of Coats Redfern model for the calculation of kinetic parameters is given below.

ln [$\:\frac{g\left(\alpha\:\right)}{{\text{T}}^{2}}$] = ln $\:\frac{AR}{\beta\:Ea}$ (1- $\:\frac{2RT}{Ea}$) - $\:\frac{Ea}{RT}$ (6)

Where g ($\:\alpha\:)$ is a kinetic function of different reaction mechanisms given in Table 1.

Plot between ln [$\:\frac{g\left(\alpha\:\right)}{{\text{T}}^{2}}$] verses $\:\frac{1}{T}$ gives a straight line whose slope can be used to calculate activation energy and intercept for the calculation of pre-exponential factor. The approximate value of g ($\:\alpha\:)$ may be varied according to different reaction mechanisms but most of the solid-state reactions fall into five categories which are listed in Table 1.

2.5 Calculation of thermodynamic parameters

Thermogravimetric analysis helps us in determining the thermodynamic parameters including change in enthalpy, Gibbs free energy, and entropy of the reactions which can be calculated based on kinetic parameters using the following equation.

∆H = E_a – RT (7)

∆G = E_a + R T_m ln ($\:\frac{{K}_{B}T}{hA}$) (8)

∆S = $\:\frac{\varDelta\:\text{H}\:-\:\varDelta\:\text{G}}{{T}_{m}}$ (9)

Where K_B is Boltzmann constant with value of 1.381×$\:{\text{10}}^{\text{-23}}$ m² kg s^− 2 K^− 1, T_m is maximum decomposition temperature, h is a planked constant having a value of 6.626×10^− 34 m² kg s^− 1 and R is a universal gas constant which is equal to 0.008314 kJ mol^− 1 k^− 1.

Table 1

Reaction mechanism, model names with their f(α) and g(α).
Reaction mechanism	Model name	f(α)	g(α)
Chemical reaction order	Chemical reaction order 1 (F₁) Chemical reaction order 1.5 (F_1.5)	1- α (1- α)]^3/2	-ln (1- α) 2[(1- α)-^3/2-1]
Diffusion	Parabolic law (D₁) Valensi equation (D₂) Ginstling-Brousshtein equation (D₃)	1/2 α - [ln (1- α)]⁻¹ 3/2[(1- α)^1/3-1]⁻¹	α² α + (1- α) ln (1- α) (1–2/3 α) - (1- α)^2/3
Nucleation and growth	Avrami-Erofeev equation nucleation and growth (N_1.5) Avrami-Erofeev equation nucleation and growth (N₂)	3(1- α) [-ln (1- α)]^2/3 2(1- α) [-ln (1- α)]^1/2	[-ln (1- α)]^2/3 [-ln (1- α)]^1/2
Phase interfacial reaction	Shrinkage gematrical column (S₁) Shrinkage geometrical spherical (S₂)	2(1- α)^1/2 3(1- α)^2/3	2(1- α)^1/2 3(1- α)^2/3
Power law	Power law (P)	1	α

3.1 Characterization of sample

Air air-dried sapodilla leaf samples were subjected to preliminary characterization such as proximate analysis and ultimate analysis. Sapodilla leaves contain a small amount of moisture content (9.0 wt%) and ash (3.7 wt%) and contain 87.3 wt% combustible material (69.3 wt% volatile matter and 18.0 wt% fixed carbon). The proximate composition of sapodilla leaves is much better than most agriculture-based biomasses. The higher value of combustible fraction and lower values of moisture and ash dictated the HHV of sapodilla leaves about 23.4MJ kg^− 1 which is higher than most of the biomasses and even the lignite coal [24]. The reason for such a high HHV is associated with its high carbon (58.2 wt%) and high hydrogen contents (7.1 wt%) coupled with a relatively lower content of oxygen (28.8 wt%) as shown in Table 2. Such a high value of HHV, and lower value of sulfur (0.1 wt%) and nitrogen (5.7 wt%) hints towards the potential of Sapodilla leaves for direct combustion application with in situ or post-combustion nitrogen removal strategies in place [25–27]. The HHV of the Sapodilla leaves was calculated by ultimate analysis according to the Dulong formula which is given below:

HCV = 1/100[8080C + 34500(H-O/8) + 22400S] (10)

Where the C, S, O, H are the percentages of carbon, sulfur, oxygen, and hydrogen.

Table 2

Preliminary characterization of Sapodilla leaves.
Proximate Analysis	Moisture	Volatile Matter	Fixed Carbon	Ash	HHV
Proximate Analysis	9.0 (wt%)	69.3 (wt%)	18.0 (wt%)	3.7 (wt%)	23.4 MJkg^− 1
Ultimate Analysis (wt%)	Carbon	Hydrogen	Oxygen	Nitrogen	Sulfur
Ultimate Analysis (wt%)	58.2	7.1	28.8	5.7	0.1

3.2 Thermogravimeteric Analysis

Figure 1 represents the three distinct mass loss regions with the increase in temperature at four different heating rates 10, 20, 30, and 40°C min^− 1. In all heating rates, the first mass loss region represented the loss of moisture and low molecular volatiles at 110°C and it turned out to be 9.85 wt%, 9.62 wt%, 9.15 wt% and 8.7 wt% for 10, 20, 30 and 40°C/min respectively wt.% which is in good agreement with proximate analysis. The second mass loss region consists of a degradation temperature range (between 250–550°C) in all heating rates, and it showed significant mass loss which corresponds to fraction loss of cellulose and hemicellulose components of sapodilla leaves due to cracking and to produce a large amount of gas and bio-oil causing serious weight loss of the sample. In this region, the weight loss percentage for sapodilla leaves were 53.87 wt% for 10°C/min, 53.28 wt% for 20°C/min, 52.17 wt% for 30°C/min, and 52.16% for 40°C/min. The amount of volatile fraction released in pyrolysis has a direct relation with the heating rate. At a lower heating rate, more amount of volatile matter and less amount of ash is produced, and vice versa. This happens due to the fast achievement of the exothermic phase. There are condensed aromatics ring structures, linked with aliphatic hydrocarbons in biomass structures. In the third degradation curve, these aliphatic hydrocarbons broke down and evaporated along with some portion of hydrogen from aromatics rings leaving behind porous carbon-rich material [4]. .

The third degradation curve was observed at the temperature between 550–900°C with a relatively lower slope as compared to the second degradation curve which represents the degradation of the lignin component of the sample. The weight loss % of this region was 21.56 wt%, 19.20 wt%, 19.18 wt%, and 16.48 wt% for 10, 20, 30, and 40°C/min While the weight of the residue left was lower w for the lower heating rate and more for higher heating rates, in current situation the weight of the residue was about 22.66 wt%, 19.50 wt% and 17.9 wt% for 40°C min^− 1, 30°C min^− 1, and 20°C min^− 1 respectively, but as the heating rate decreased to 10°C/min, the weight of the residue was decreased to minimum level of 14.72 wt%. Faster heating rates can result in uneven temperature distributions within the biomass particles. This non-uniform heating can lead to incomplete pyrolysis and the production of solid residue. Slower heating rates allow for more even heat distribution, promoting thorough pyrolysis.

Differential thermal analysis was plotted by temperature vs dw/dt which shows that the degradation curve is shifted towards high temperature with increasing heating rate. The peak temperature is a temperature where the maximum conversion of the material occurs. The values of peak temperature for sapodilla leaves at 10, 20, 30, and 40°C/min were 325, 450, 501, and 570°C. The reason behind this shift is the complex nature of the pyrolysis process, there may be multiple decomposition reactions occurring simultaneously or in sequence. The relative rates of these reactions can be affected by heating rate variations, leading to shifts in the DTG peaks. At higher heating rates due to a sharp increase in temperature, the minimum heat required to initiate the degradation is provided at higher temperatures because of the rapid increase in temperature as depicted in Fig. 2.

Kinetic analysis of sapodilla leaves pyrolysis

Based on thermogravimetric analysis, different types of reaction models were applied for the determination of kinetic parameters as mentioned in Table 1. The values of activation energy (E_a), pre-exponential factor (A), and linear regression coefficient (R²) were calculated using the Coats Redfern model for every model which has been presented in Fig. 3. The best reaction model for the thermal decomposition behavior of sapodilla leaves was estimated by the good agreement with the range of R² value between 0,90 to 0.99. In the low-temperature region (250–550°C), all models gave values in the range except power law P2 and one-dimensional diffusivity model D1. In this region, the chemical reaction model F_1.5 has the highest R² value among all reaction models with an activation energy value between 30–32 kJ mole^− 1 as illustrated by Fig. 05. It indicates that sapodilla leaves have the highest degradation rate in low-temperature regions which is attributed to straight chain aliphatic hydrocarbon which are easily degraded at a lower temperature [16, 28]. With the increase in heating rate the regression coefficient also increased in all reaction models because certain reaction mechanisms or kinetics equations may provide a better fit to the data at higher heating rates due to the specific kinetics of the reaction and reaction that are partially observable at lower heating rates may become more pronounced at higher heating rates [16]. The nucleation model showed a very lower R² value in the low-temperature region but in the high-temperature region, nucleation models N_1.5 and N₂ showed the highest R² value as presented in Fig. 04. This shifting of the best-fitted model in lower and higher temperature regions indicated the presence of different nature of compound with the different functional group and molecular weight which could be degraded at different temperature range but independent of heating rate. [29, 30]. The activation energy decreased with the increase in temperature from the low-temperature region to the high-temperature region due to the higher heat energy available for the reaction.

. [15]. Elevation of temperature increased the vibrational kinetic energy of the molecules present in the biomass sample which leads to the breakage of the large biomass fragments into smaller molecules. The bond breakage rate depends on the number of molecular collisions per unit of time and the fraction of fruitful collision that leads the reactant molecules toward the product. Initially, when the biomass sample is heated, the molecules become activated by absorbing heat energy as represented in Eq. 11. In an activated complex there is competition between fruitful collision (Eq. 12) and the deactivation of activated molecules (Eq. 13) when they colloid with inactive molecules. So, the activated molecules could either be converted into products if they have a collision with other activated molecules or be converted to a less active stage if they have a collision with the inactivated molecule. Based on the kinetic data it can be inferred that at high temperatures there is an increase in the number of collisions which lowered the activation energy as given in Table 3, and for the best fitted model it is presented in Fig. 4.

At a higher heating rate, all molecules of biomass sample could not get equal heat energy due to the rapid increase in temperature as opposed to the lower heating rate. Hence there are higher no. of inactive molecules as compared to lower heating rate. Due to the presence of a large fraction of inactive molecules, the fraction of fruitful collision becomes lowered which leads to an increase in the activation energy at a higher heating rate such as 40°C min^− 1. The values of activation energy are lower at lower heating rates. The potential reason might be that biomass pyrolysis is rate-dependent. The rate at which these reactions occur is not only influenced by temperature but also by the rate at which temperature changes. However, in the collision frequency, a small variation is observed at different heating rates among all selected models as mentioned in Fig. 6. At low heating rates, the temperature of the biomass increases slowly, leading to slower reaction rates and providing enough time to convert into the required product. This can make the apparent activation energy appear lower. Various similar trends of heating rate and collision frequencies dependent on activation energy variation were also reported in the literature [31].

A + A → A* +A (11)

A* + A* → Product (12)

A* + A → A (Deactivation) (13)

Table 3

Kinetic parameter of Sapodilla leaves.
Heating Rate	Model Name	Low Temperature (250–500°C)			High Temperature (500–900°C)
Heating Rate	Model Name	E_a	A(min^− 1)	R²	E_a	A(min^− 1)	R²
10°C	F₁	3.38	38.52	0.869	6.29	42.17	0.998
	F_1.5	6.81	28.91	0.984	1.08	31.00	0.343
	D₁	8.64	36.30	0.918	6.82	45.23	0.996
	D₂	9.76	36.76	0.939	5.44	45.48	0.996
	D₃	10.17	41.19	0.946	4.79	49.28	0.995
	N_1.5	0.40	41.11	0.131	7.99	43.44	0.998
	N₂	1.09	42.50	0.609	8.84	44.08	0.998
	S₁	2.41	42.34	0.741	7.90	46.18	0.998
	S₂	2.73	43.20	0.793	7.41	46.83	0.998
	P	1.54	41.47	0.508	9.10	45.61	0.997
20°C	F₁	3.29	38.07	0.837	7.36	42.54	0.996
	F_1.5	7.72	27.62	0.983	1.25	31.71	0.550
	D₁	8.17	36.88	0.890	7.97	45.67	0.994
	D₂	9.48	37.10	0.923	6.89	46.02	0.994
	D₃	9.99	44.66	0.933	6.36	49.86	0.993
	N_1.5	0.17	40.81	0.021	8.67	43.68	0.997
	N₂	1.39	42.18	0.649	9.32	44.25	0.997
	S₁	2.10	42.24	0.628	8.67	46.46	0.996
	S₂	2.48	43.00	0.715	8.28	47.14	0.996
	P	1.05	41.59	0.267	9.62	45.81	0.996
30°C	F₁	3.69	37.87	0.921	7.51	42.34	0.990
	F_1.5	8.30	27.18	0.931	0.96	30.82	0.439
	D₁	8.75	36.67	0.953	8.36	45.65	0.990
	D₂	10.09	36.86	0.964	7.30	45.93	0.986
	D₃	10.62	41.07	0.967	6.77	49.73	0.984
	N_1.5	0.47	40.67	0.281	8.78	43.55	0.993
	N₂	1.15	42.07	0.771	9.41	44.15	0.994
	S₁	2.46	42.10	0.843	8.88	46.39	0.993
	S₂	2.85	42.84	0.880	8.47	47.03	0.994
	P	1.39	41.47	0.595	9.83	45.81	0.994
40°C	F₁	3.57	38.11	0.948	7.69	42.71	0.995
	F_1.5	7.98	27.66	0.937	2.22	31.84	0.966
	D₁	8.71	36.95	0.969	8.33	45.91	0.994
	D₂	10.00	37.21	0.978	7.41	46.29	0.992
	D₃	10.50	41.44	0.980	6.95	50.16	0.992
	N_1.5	0.38	40.81	0.300	8.79	43.82	0.996
	N₂	1.21	42.16	0.848	9.34	44.37	0.996
	S₁	2.39	42.26	0.889	8.84	46.64	0.995
	S₂	2.76	43.02	0.920	8.50	47.33	0.996
	P	1.36	41.58	0.658	9.67	45.97	0.996

Thermodynamic Parameter Calculation

Thermodynamic parameters such as change in enthalpy (ΔH, kJ mol^− 1), change in Gibbs free energy (ΔG, kJ mol^− 1), and change in entropy (ΔS, kJ mol^− 1) were calculated using Eq. 7, 8, and 9 respectively. These thermodynamic parameters were calculated at four different heating rates 10, 20, 30 and 40°C min^− 1 of TGA analysis for each reaction mechanism model listed in Table 1. A brief data of thermodynamic parameters has been presented in Table 4.

Change in Enthalpy

ΔH is a state function that indicates the emission or absorption of the heat from the experimental system at a constant pressure. All the models showed positive value of ΔH except D₂ and D₃ at 10°C min^− 1 and F_1.5 at 20, 30, 40°C min^− 1. The numerical value of conversion factor (α) is less than unity in all reaction models except F_1.5, D₁, D₂ and D₃ that is why these models usually behave differently compared with other models. The higher value of α indicates the presence of easily degradable volatile fraction which is actually true for leaves and other lower plant based biomasses [16, 32]. The positive value of change in enthalpy indicated that the energy is required from an external source to convert the sapodilla leaves into an activated complex to cross the potential barrier for its conversion into final product. It also indicates that reaction is endothermic in nature and heat is required for higher energy state transition for effective conversion into product [15]. Increase of ΔH value with increasing heating rate could be the increased the degradation time for a biomass sample. Higher value of ΔH may have significant effect on activity of primary reaction due to direct relationship between the ΔH and E_a magnitude as depicted by our calculated data in Table 3 and best fitted model has been presented in Fig. 07. This type of trend was also observed during the pyrolysis of high ash sewage sludge [15]. The endothermic nature of the reaction also confirms that the product has more energy as compared to the reactant. In solid fuel there are more -C-C- long chains of aliphatic hydrocarbons and complex aromatic structure of the biomass as compared to gaseous or liquid fuel which consist of small chain of aliphatic hydrocarbons with more hydrogen content as compared to solid fuel. During the conversion of solid biomass into liquid or gaseous product the supplied energy is stored in the form of new ion or free radical formation which released less energy during bonds formation as compared to that energy which is needed to break different bonds in solid biomass. The second plausible reason may be the high oxygen content of the biomass, because the breakdown of oxygenated compound required more energy as compared to that energy which is released during hydrocarbon bond formation [30, 33]. Hence the energy is required from an external source which makes the nature of reaction endothermic.

Gibbs free Energy

The Gibbs free energy ΔG is the amount of heat which can be utilized from the reactant to propagate the reaction at a constant temperature and pressure, and it must be given by the system for the conversion of reactants into activated complex or products [34]. It is a true indicator, for the evaluation of heat flow changes for a chemical reaction and its magnitude is inversely proportional with favorability of the reaction [35]. Usually, the value of Gibbs free energy is decreased with the increase in temperature and similar trends are also observed in sapodilla leaves degradation presented in Fig. 8. Low temperature degradable compounds are very simple and may be the straight chain hydrocarbons as compared to high temperature degradable compound which may have very complex nature, highly branched chain and condensed aromatic structure which need higher energy for bond breakage.

It was also observed in literature that reaction mechanism model fit well in low temperature regime as well as high temperature regime for same biomass, but in case of sapodilla leaves it is different, which indicates the presence of different identity/functional groups in low temperature degraded and high temperature degraded fraction of Sapodilla leaves [14] (Compare values in both region) which indicates that in different temperature range (lower temperature region and high temperature region) which reaction mechanisms are dominant than other please mention clearly. You can describe the overall range as well.

Entropy

Entropy ΔS is the measure of the degree of disorder in a structure or molecular rearrangement. ΔS showed the negative values in all the reaction mechanism model in sapodilla leaves pyrolysis in both temperature regions which indicates the product is in less ordered form as compared to reactant. Moreover, it shows the product has higher Gibs free energy (available energy) to perform a chemical reaction which increased the overall energy density of the product. In the pyrolysis process the dissociated species can rejoin together according to their nature and electro-chemical extent. The ΔS value for sapodilla leaves pyrolysis decreased with the increase in temperature in all heating rates as depicted in Fig. 9 which followed the basic law of thermodynamics ΔS = Q/T. It varied in the range between − 157.9 J mol^− 1 to -0.221 J mol^− 1 in different heating rates as mentioned in Table 4, which is higher than the sewage sludge pyrolysis − 181 J mol^− 1 to -225 J mol^− 1 [15], and rice straw pyrolysis − 249 J mol^− 1 to -8.31 J mol^− 1 [33].

Table 4

Thermodynamic Parameter of Sapodilla leave.
Heating Rate	Model name	Low Temperature (250–550°C)			High Temperature (550–900°C)
Heating Rate	Model name	ΔH	ΔG	ΔS	ΔH	ΔG	ΔS
10°C	F₁	0.469	99.233	-0.2224	0.442	104.382	-0.2341
	F_1.5	3.899	103.722	-0.2248	-4.765	100.311	-0.2367
	D₁	5.733	104.717	-0.2229	0.971	104.652	-0.2335
	D₂	6.846	105.783	-0.2228	-0.401	103.260	-0.2335
	D₃	7.265	105.783	-0.2219	-1.054	102.311	-0.2328
	N_1.5	-2.513	96.011	-0.2219	2.141	105.972	-0.2339
	N₂	-1.816	96.586	-0.2216	2.991	106.768	-0.2337
	S₁	-0.496	97.920	-0.2217	2.058	105.662	-0.2333
	S₂	-0.185	98.157	-0.2215	1.568	105.121	-0.2332
	P	-1.371	97.121	-0.2218	3.256	106.906	-0.2334
20°C	F₁	-0.020	99.662	-0.2245	1.523	105.417	-0.2340
	F_1.5	4.408	105.274	-0.2272	-4.591	100.387	-0.2364
	D₁	4.858	104.657	-0.2248	2.129	105.761	-0.2334
	D₂	6.171	105.948	-0.2247	1.053	104.657	-0.2333
	D₃	6.684	105.776	-0.2232	0.528	103.836	-0.2327
	N_1.5	-3.138	96.287	-0.2239	2.830	106.627	-0.2338
	N₂	-1.920	97.383	-0.2237	3.484	107.232	-0.2337
	S₁	-1.211	98.087	-0.2236	2.836	106.404	-0.2333
	S₂	-0.830	98.403	-0.2235	2.440	105.956	-0.2331
	P	-2.259	97.097	-0.2238	3.787	107.408	-0.2334
30°C	F₁	0.396	100.062	-0.2245	1.670	105.595	-0.2341
	F_1.5	5.004	105.894	-0.2272	-4.884	100.214	-0.2367
	D₁	5.461	105.246	-0.2247	2.517	106.164	-0.2334
	D₂	6.801	106.566	-0.2247	1.457	105.081	-0.2334
	D₃	7.329	106.695	-0.2238	0.920	104.252	-0.2327
	N_1.5	-2.826	96.576	-0.2239	2.932	106.754	-0.2338
	N₂	-2.147	97.130	-0.2236	3.563	107.334	-0.2337
	S₁	-0.831	98.444	-0.2236	3.033	106.621	-0.2333
	S₂	-0.439	98.771	-0.2234	2.626	106.164	-0.2332
	P	-1.905	97.426	-0.2237	3.987	107.621	-0.2334
40°C	F₁	0.223	99.971	-0.2247	2.052	105.604	-0.2332
	F_1.5	4.637	105.568	-0.2273	-3.419	101.216	-0.2357
	D₁	5.367	105.229	-0.2249	2.696	105.981	-0.2326
	D₂	6.654	106.490	-0.2249	1.775	105.029	-0.2326
	D₃	7.158	106.597	-0.2240	1.317	104.275	-0.2319
	N_1.5	-2.963	96.533	-0.2241	3.156	106.613	-0.2330
	N₂	-2.129	97.247	-0.2238	3.707	107.118	-0.2329
	S₁	-0.954	98.412	-0.2238	3.208	106.434	-0.2325
	S₂	-0.578	98.722	-0.2236	2.862	106.034	-0.2324
	P	-1.984	97.442	-0.2239	4.029	107.309	-0.2326

In this study, thermo-kinetic parameters of sapodilla leaves were investigated with the help of the Coats Redfern model. Proximate and ultimate analysis showed the high potential of sapodilla leave utilization. The TGA data indicated that sapodilla leaves pyrolysis is a multi-step process with complex reaction mechanisms. Thermo-kinetic analysis was performed in the low-temperature 250–550°C and high-temperature 550–900°C region. In every model, the collision frequency was observed to increase with the increase in temperature also reflected by a decrease in activation with an increase in temperature, which means the extent of the pyrolysis reaction increases with the increase in temperature. A higher correlation coefficient suggested the most suitable model for the description of sapodilla leaves pyrolysis process. Enthalpy of the reaction confirms that the product has higher energy content as compared to the reactant, Gibbs free energy also indicated that for the pyrolysis process, we should have to provide some energy to the system. Entropy also confirms that the product has a well-ordered structure as compared to the reactant. Kinetic and thermodynamic parameters information will be vital and useful knowledge for large-scale Sapodilla leaves pyrolysis processes for bioenergy applications like its gasification or bio-oil production. Quantification of different products released during pyrolysis of sapodilla leaves may be the subject of future study also with the liquefication or gasification experiments.

Authors Contributions

Muhammad Asif: Conceptualization, methodology, formal analysis, data curation, and writing - original draft. Rumaisa Tariq: Methodology, investigation, and data curation. Muhammad Bilal: Methodology, investigation, and data curation. Hafiz Abdul Mannan: investigation Sumaira Kanwal: investigation Abdul Rauf: investigation, Faisal Raza: investigation, Hassan Zeb: Supervision, project administration, resources, writing - review and editing. All authors read and approved the final manuscript.

Funding Acquisition

There is no funding acquisition associated with the preparation of this manuscript.

Competing interest

The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability Statement

Data will be made available on reasonable request to corresponding author.

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Investigation of Kinetic and Thermodynamic Parameters of Sapodilla Leaves Pyrolysis Using Coats Redfern Model

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Abstract

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1. Introduction

2. Material and Methodology

2.1 Sample Collection and Characterization

2.2 Thermogravimetric Analysis

2.3 Kinetic Study

2.4 Coats Redfern model

2.5 Calculation of thermodynamic parameters

3 Results and Discussions

3.1 Characterization of sample

3.2 Thermogravimeteric Analysis

Conclusion

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References

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