Particle size impact on pyrolysis of multi-biomass: a solid-state reaction modeling study

ABSTRACT

Pyrolysis has gained significant attention due to its generation of value-added products from waste feeds in an environmentally friendly manner. The primary purpose of this study is to understand the effect of different particle sizes of biomass wastes – date stones (DS), cow manure (CM), and spent coffee grounds (SCG) – to understand better and design a biomass pyrolysis system. Thermogravimetric analysis of four different sizes of DS, SCG, and CM (range 1 mm to 125 μm) and a mixed sample (for each feed) was conducted at a heating rate of 10K/min from room temperature to 1173.15 K at inert conditions and employed model-based Coats–Redfern equations to understand the kinetic and thermodynamic parameters of the pyrolysis process. All the particle sizes except 355–125 μm for DS and SCG have the best-fit reaction mechanism of Ginstling-Brounshtein (D4). Both activation energy and pre-exponential factor decreased from 18.78 to 5.57 kJ/mol and 1.16 E+10 to 1.48 E+08 with reducing particle sizes. The onset degradation temperature, activation energy, change in enthalpy, and entropy decrease with particle sizes. The product formation is favored for all feeds and particle sizes, as the difference between the enthalpy and activation energies (Ea) is below 10 kJ/mol. As a result of their substantially lower activation energies and better reaction thermodynamics, mixed and smaller particle-sized biomass are favored.

KEYWORDS:

• Pyrolysis
• TGA
• kinetics
• coats-redfern
• thermodynamics
• particle size

Introduction

Global concerns about waste management are becoming more and more urgent. Landfilling is the most popular technique for trash disposal; although affordable and simple, it poses significant risks to both human health and the environment as a result of its emissions to the air and bodies of water (Foong et al. 2020). Also, the demand for alternative waste disposal methods is essential due to related environmental requirements and a paucity of land area for landfilling (Parthasarathy et al. 2022). Environmental friendliness and the creation of value-added products are just two of the reasons why pyrolysis, a thermochemical processing technology, has attracted interest (Zuhara et al. 2022). There is no oxidizing agent used in the process. The products resulting from pyrolysis, including oil, char, and gas, depend on various operational factors such as the type of feedstock used, heating rate, temperature, solid residence periods, and particle size (Mariyam et al. 2022). The wastes of interest in this research are three biomasses – Date Stones (DS), Spent Coffee Grounds (SCG), and Cow Manure (CM). About 10% of the date fruit comprise date stones; with about 120 million date palms worldwide, DS is a significant waste (Tahir, Al-Obaidy, and Mohammed 2020). SCG is a massive waste stream from the coffee brewing industry with valuable resources and requires an effective waste management technique rather than landfilling (McNutt and He 2019). Additionally, about 55 million tons of animal manure are collected for disposal annually due to increased husbandry production, with great potential for producing value-added products via chemical and biological methods due to its valuable components (Khoshnevisan et al. 2021). Therefore, the three feeds selected for this study are examples of significant wastes with the potential for value-added production. Additionally, due to the significant composition differences of the studied biomass feeds, the authors have previously studied the feeds to understand the potential for bio-oil production using a micro-scale pyrolyzer (Mariyam et al. 2022) and developed a biochar prediction model using response surface methodology (Mariyam et al. 2023); both the studies further emphasizes the value of studying the pyrolysis kinetics of the feeds.

Furthermore, biomass particle sizes can affect feedstocks’ yields, characteristics, and processing time requirements (Asadullah, Zhang, and Li 2010; Das et al. 2021; El Hanandeh, Albalasmeh, and Gharaibeh 2021). Particle size also affects the pyrolysis process’s kinetics by impacting accessibility to biomass components, chemical reactivity, and thermal resistances (Mlonka-Mędrala et al. 2019). Studying biomass pyrolysis reactions will help understand thermochemical conversion technology parameters and provide data for the pyrolysis plant design. Additionally, particle sizes have been known to impact the various process kinetics (including Activation Energy – Ea), product yield distributions, and accessibility to cellulose due to biomass grinding (Kumar et al. 2020; Qureshi et al. 2018). A recent extensive review on pyrolysis and gasification of biomass wastes by the authors’ reiterated the need for studies focusing on the physical attributes of the feeds – including particle sizes – for products generation (Mariyam et al. 2022).

Thermogravimetric Analysis (TGA) may be used to understand the effects of particle size on the kinetic parameters and thermodynamic parameters of biomass degradation systems, providing valuable guidance in designing biomass pyrolysis systems. The TGA data reflect changes in materials’ weight loss over time and temperature (Elkhalifa et al. 2022). Although TGA does not detect phase transitions or processes for which mass does not change, it is often used in modern chemical engineering to derive kinetics and mechanisms due to changes in mass by non-isothermal, quasi-isothermal, and isothermal methods (Saadatkhah et al. 2020). Kinetic parameters are often calculated from the master plots derived from TGA curves using kinetic-free models such as Kissinger-Akahira-Sunose and Flynn-Wall-Ozawa assuming linear transformations and omitting reaction mechanisms (Bach and Chen 2017). Alternatively, model-based methods such as Coats Redfern give more insight into the reaction mechanism during thermal degradation and have previously been employed to study biomass (Ali et al. 2019; Naqvi et al. 2019; Parthasarathy et al. 2021).

Therefore, this research aims to understand the thermokinetics and thermodynamics of three distinct biomasses (DS, SCG, and CM) using the Coats Redfern method at four distinct particle sizes. Additionally, the final feed was a mixed feed; for the purpose of a systematic study, the particle sizes were equally mixed to analyze the effect of each particle sizes on the kinetics and thermodynamics. A study found significant Ea variations when comparing two particle sizes, 0.09 and 0.425 mm, of five biomasses (including agricultural residues and energy crops) (Mlonka-Mędrala et al. 2019). Recently, the effect of biomass particle sizes has been studied for the pyrolysis of rice straw and pine sawdust (Xiao et al. 2020), sour cherry stalk (Gözke and Açıkalın 2021) and radiata pine wood (Somerville and Deev 2020). While the first study shared opposing results for the two feeds, the second study focused on two particle sizes only. The third study found there is a significant effect when gas flow increased, but studied only one feed. However, none of these studies analyzed the effect of particle sizes on thermodynamics.

The novelty of this study lies in addressing the need for a single, and systematic study focusing on biomasses with unique and wider composition ranges, and the effect of a wider range of particle sizes and their effects on thermal degradation, solid state reaction models, kinetics, and thermodynamics. The study contributes to addressing the pressing global concern of waste management and reducing environmental and health issues associated with landfilling. Therefore, the objective of this article is to provide a more comprehensive understanding of the effect of particle size on the pyrolysis system by analyzing (i) the thermal degradation behavior of the three biomasses due to the effect of particle sizes (ii) comparing the solid state reaction models and kinetics of three distinct biomasses (iii) thermodynamics of the studied biomasses, thereby providing conclusions necessary to design pyrolysis systems for biomass waste management better.

Materials and methods

Materials procurement

DS was obtained from Doha Dates National Food Co., SCG from the campus coffee shop, and CM from a dairy-producing firm in Qatar. The DS and SCG underwent a cleaning process using distilled water and were left to dry at room temperature. In contrast, the CM was initially sun-dried for 24 h while still moist, then washed with distilled water and dried at room temperature.

Characterization of feeds

The feedstocks (particle size <355 μm) were characterized through proximate, ultimate, and calorimetric analysis. Proximate analysis was conducted using the standard procedure of ASTM D7582–12, with a thermogravimetric analyzer (SDT650, TA Instruments). The moisture, volatiles, fixed carbon, and ash were obtained following the standard method. Samples weighing around 9 ± 0.1 mg were heated in an inert nitrogen environment from room temperature to 378.15 K for 30 min to evaluate the moisture content. The temperature was then increased to 1223.15 K at a rate of 30 K/min, and the samples were held at this temperature for 7 min under isothermal conditions. The samples were combusted with oxygen for 10 min to determine the ash content. The fixed carbon was obtained by subtracting the remaining mass (moisture, volatile matter, and ash) from the original mass.

Using a CHNS elemental analyzer, the ASTM D 3176–8 standard protocol was followed to estimate the weight % of various elements such as carbon, hydrogen, nitrogen, oxygen, sulfur, chlorine, and ash in the feedstock (EA3000 CHNS elemental analyzer, EuroVector). The average results of three duplicate samples ranging in size from 0.5 to 1.5 mg were recorded. By deducting the total weight of the collected elements and ash from the sample’s overall mass, the oxygen content was estimated. The samples’ calorific values were determined by utilizing an Automated Isoperibol Fixed Bomb Parr 6300 bomb calorimeter, where the sample was combusted completely in an oxygen environment to give the values in MJ/kg. A dried sample weighing between 0.2 and 0.6 grams was weighed and put in the bomb with a magnetic thread. The bomb was then filled with oxygen at 4000 psi pressure before being placed in a water bucket filled with 2 l of water. The temperature rise in the bucket was measured by the built-in thermometers.

Thermogravimetric analysis study for pyrolysis

The DS, CM, and SCG were pulverized and divided into four distinct particle sizes for the particle size analysis using a sieve bank by Haver & Boecker, Germany: 1 mm–710 m (S1), 710–500 m (S2), 500–355 m (S3), and 355–125 m (S4). Also, an equal blend of samples from each of the four particle sizes (S5) was examined.

An SDT 650 thermogravimetry analyzer (refer to figure S1) was used to conduct the TGA within the temperature range of 294.15 K to 1173.15 K. The heating rate was set at 10 K/min, while the inert nitrogen gas flow rate was 100 ml/min. To ensure accuracy within 5%, the samples were measured at 4.5 mg, and the runs were performed in duplicate.

The TGA and Derivative Thermogravimetric (DTG) curves show the thermal behavior of the samples and offer the information needed to run Coats Redfern single-heating rate models (CR). The rate of non-isothermal solid decomposition can be predicted by Equations (1)(1) $\frac{\mathit{d\alpha }}{\mathit{dt}}=\mathit{k}\left(\mathit{T}\right)\left(1-\mathit{\alpha }\right)$(1) and (2(2) $\frac{\mathit{d\alpha }}{\mathit{dt}}=\mathit{A}{e}^{-\frac{\mathit{Ea}}{\mathit{RT}}}\frac{\mathit{dt}}{\mathit{dT}}\left(1-\mathit{\alpha }\right)$(2) ).

(1) $\frac{\mathit{d\alpha }}{\mathit{dt}}=\mathit{k}\left(\mathit{T}\right)\left(1-\mathit{\alpha }\right)$(1)

(2) $\frac{\mathit{d\alpha }}{\mathit{dt}}=\mathit{A}{e}^{-\frac{\mathit{Ea}}{\mathit{RT}}}\frac{\mathit{dt}}{\mathit{dT}}\left(1-\mathit{\alpha }\right)$(2)

Where A (min⁻¹) is the pre-exponential factor, E (kJ/mol) is the Ea, k(T) is the reaction rate constant, t (min) is time, T (K) is the absolute temperature, R (J/mol K) is the universal gas constant, α is the pyrolysis conversion factor given by Equation (3)(3) $\propto =\frac{W_0-W_t}{W_0-W_f}$(3) . The value of α and T is determined from the TGA and DTG data.

(3) $\propto =\frac{W_0-W_t}{W_0-W_f}$(3)

Where $W_0$ is the weight of the sample before decomposition (kg), $W_t$ is the weight of the sample at a time t (kg), and $W_f$ is the final weight after decomposition (kg). For a constant heating rate, β = dT/dt, Equation (4)(4) $\mathit{ln}\frac{\mathit{g}\left(\mathit{\alpha }\right)}{T^2}=ln\frac{\mathit{AR}}{\mathit{\beta E}}-\frac{E}{\mathit{RT}}$(4) is the re-arranged and integrated equation based on the original CR equation (Coats and Redfern 1964):

(4) $\mathit{ln}\frac{\mathit{g}\left(\mathit{\alpha }\right)}{T^2}=ln\frac{\mathit{AR}}{\mathit{\beta E}}-\frac{E}{\mathit{RT}}$(4)

In this investigation, 17 reaction CR models, based on five primary reaction mechanisms, including order reactions, diffusion, power laws, and nucleation models. The kinetic parameters, Ea and pre-exponential factor (A), were determined by calculating the slope and intercept of the best fit model’s regression equations. A linear regression model was created in Microsoft Excel, with the formula in the form of y=B + Cx + Dz. The relationship between ln g (α)/T² and I/T was used to derive Ea and A from the slope and intercept of the equation, respectively. The DTG curves were used to identify regions with the highest weight loss for modeling purposes. The CR models studied are shown in Table S1, which includes equations (E1) to (E17). Satisfactory values for Ea and A were obtained from the models with the highest coefficient of determination, R², to the fitted regression line.

Moreover, the kinetic parameters are used to determine the Gibbs free energy (∆G), change in entropy (∆S), and change in enthalpy (∆H). (equations E18–20). Positive and negative values of $\mathrm{\Delta }\mathrm{H}$ reflect endothermic and exothermic reactions, respectively. Also, negative and positive values of $\mathrm{\Delta }\mathrm{G}$ reflect spontaneous and non-spontaneous values. Finally, negative values of $\mathrm{\Delta }\mathrm{S}$ reflect the disorder of products that is less than the initial reactants, while positive values have the opposite effect.

Results and discussion

Characterization

Table S2 shows the characteristics of the studied biomasses. The proximate analysis shows that most of the biomass comprises volatiles (56.62–74.68%), reflecting the potential for bio-oil and gas production during pyrolysis. The volatiles and ash content were the lowest and highest for CM compared to the other two studied biomass. Alternatively, the moisture and volatile content were highest for SCG. The lower moisture content and higher volatiles reflect valuable products being produced from the studied biomass. Therefore, the volatile content obtained from proximate analysis can provide an indication of the organic content of the biomass, which can be useful for determining the potential energy yield of the biomass when used as a biofuel (Mariyam et al. 2022). The variety of results in the proximate analysis of the biomasses indicates a wide range of ash and volatile content in the selected feeds, which is consistent with the diverse types of biomass waste discussed in the overview of their chemical composition by (Vassilev et al. 2010). The ultimate analysis shows high carbon and oxygen content and lower hydrogen and nitrogen content. The high carbon content (34.22–45.95%) suggests that the substance is likely to be a carbon-rich material and is common for biomasses since an extensive review reported a range from 36.89% to 59.05% (Neves et al. 2011). While the fixed carbon was mostly increased for the DS, the calorific value was lowest (19.1 MJ/kg), similar to the woody biomass calorific values ranging from 14.15 to 19.18 MJ/kg (Islam et al. 2019). The calorific values of SCG and CM are higher than DS, at 29.79 and 22.18, respectively. DS has a lower calorific value compared to other biomass sources like woody biomass, SCG, or CM. Therefore, more DS will be required to generate the same amount of energy compared to other biomass sources. It also highlights the potential of SCG and CM as sources of energy due to their higher calorific values. SCG has the highest calorific value of the three substances, indicating that it could be an excellent source of energy if it is utilized efficiently.

Thermal degradation behavior of studied biomasses

Figure S2 shows the TGA and DTG curves of the different particle-sized feeds, and Table 3 shows the thermal degradation data. The DTG results show three distinct stages, regardless of the biomass type or particle size. The first stage, Stage I, is primarily associated with dehydration. Stage II is mainly related to cellulose, hemicellulose, and lignin degradation. Lastly, Stage III refers to char degradation. The weight loss in the three stages varies from biomass to biomass; however, the majority of the weight loss was in Stage II. Table S3 clearly shows the temperature ranges for the feeds’ degradation. The DTG curves of the three feeds show most of the weight loss in the second stage. However, the weight loss rate varies. The TGA curves show that the total weight loss at the final temperature is between 74% and 78% for DS, 61–74% for CM, and 73–79% for SCG. Furthermore, the moisture content of the SCG was lower than the other two feeds, especially when the particle sizes were bigger.

The DTG graphs also demonstrate that the particle size depends on the decomposition peak, end, and onset temperatures. Moreover, when the particle size decreases, the temperature starts dropping, while the end temperature rises (Table 3). The major decomposition temperatures range from 564 to 571 for DS and SCG; the temperatures for CM are higher and between 602 and 607 K. The weight loss increases with increasing temperature, with the most significant part of biomass degradation occurring between 400 and 875 K. Furthermore, the moisture content is higher when the particle size is bigger for DS. The surface area-to-volume ratio falls off as the particle size increases. As a result, there is less surface area available for the particle’s moisture to evaporate. This indicates that larger particles can hold onto more moisture than smaller ones due to their slower rate of moisture loss. The evaporation stage has the highest weight loss when the particle sizes are larger for all three biomasses. Generally, the moisture content was highest in the following order for S1 (biggest sizes) DS > CM > SCG. Therefore, if most of the feed is larger in particle size, it would require more energy for the first pyrolysis stage. Alternatively, the degradation in Stage II increases with decreasing particle sizes – this is seen in all three biomasses. The volatile content in SCG is generally higher than DS and CM, which could mean enhanced gas and oil yields (also seen from the proximate analysis). The Stage III weight loss decreased with decreasing particle size. However, such conclusions could not be derived for DS and CM. The residual char for all samples ranged from ~22% to 26% for DS and SCG but higher for CM (26–32%), reflecting that the studied biomasses are good candidates for biochar production, irrespective of particle sizes, even at high temperatures. Table S3 shows the maximum weight loss for all the samples in each stage. The char yield and total weight loss are the lowest and highest for the mixed samples (S4) in the case of DS and SCG. However, the volatile yield was highest when the CM’s particle size was largest. The surface area-to-volume ratio falls off as the particle size increases. This implies that less of the material’s surface is available for the volatile chemicals to escape through. As a result, the larger particles often tend to trap more volatiles inside of them, increasing the yield. Additionally, a more gradual and full release of volatiles from the material is possible as a result of the larger particles’ higher energy requirements and slower heating rates. Moreover, this can result in greater volatiles yield. The differences in the three studied biomass are reflected in the thermal degradation behavior and the DTG curves with distinct peak behaviors (figure S2).

Kinetic parameters of studied biomasses

The seventeen reaction models of Coats–Redfern (equation E1-E17) calculated the pyrolysis kinetics in the major weight loss region (Stage II). The models’ linear regression models generated kinetic parameters of Ea and A values. Table 1 shows all the samples’ kinetic parameters when the linear regression fits with R² higher than 0.8 in Stage II. P2, P3, P4, and D3 were the least fit models (<0.8) for all three biomasses. Additionally, R1 also had the least fit model for SCG and CM. The best fit R² values range from 0.9947 (for the largest particle-sized DS) to 0.9631 (for mixed DS). The R² values for SCG and CM range from 0.9774 to 0.9719. Subsequently, the number of models with regression values >0.80 is highest when the particle sizes are smallest in the case of DS and SCG; however, for all particle sizes of CM, the reaction mechanisms have similar regression fits, and five models have R² values of 0.8. Generally, a better fit for the linear models could be due to better heat transfer when the particles are smaller, aiding better reaction mechanisms.

Table 1. Kinetics parameters and R² values of the studied biomass.

	Ea (kJ/mol)	A (s^−1)	R^2	Ea (kJ/mol)	A (s^−1)	R^2	Ea (kJ/mol)	A (s^−1)	R^2
DS
	S1			S2			S3
F1	29.00	6.90E+05	0.9013	38.73	1.29E+05	0.9296	34.61	2.49E+05	0.9274
F2	50.30	8.55E+03	0.9736	41.97	1.73E+03	0.9563	48.11	1.31E+04	0.9221
F3	50.30	1.79E+04	0.9736	57.58	4.75E+03	0.9652	48.11	2.61E+04	0.9221
P2							24.01	4.66E+06	0.9206
P3							24.01	6.98E+06	0.9206
P4							24.01	9.31E+06	0.9206
P2/3	64.13	1.52E+04	0.8365	93.42	5.35E+04	0.905	53.16	2.84E+05	0.9143
D1	39.42	3.59E+05	0.8109	60.73	9.99E+03	0.8946	32.05	6.64E+03	0.942
D2	33.08	9.99E+04	0.8531	66.94	3.40E+03	0.9125	37.77	3.42E+03	0.9428
D4	18.78	1.16E+10	0.9947	18.29	1.00E+10	0.9882	15.43	4.65E+09	0.9636
A2	29.00	1.38E+06	0.9013	38.73	2.59E+05	0.9296	34.61	4.99E+05	0.9274
A3	29.00	2.07E+06	0.9013	38.73	3.88E+05	0.9296	34.61	7.48E+05	0.9274
A4	29.00	2.76E+06	0.9013	38.73	5.17E+05	0.9296	34.61	9.98E+05	0.9274
R1				24.71	2.24E+06	0.8526	24.01	2.33E+06	0.9206
R3	5.25	1.64E+08	0.9156	3.81	6.82E+07	0.8753	4.11	7.73E+07	0.9164
	S4			S5
F1	17.90	7.11E+06	0.9183
F2	20.14	4.20E+06	0.9016
F3	20.14	8.40E+06	0.9016
P2	15.82	2.29E+07	0.9344
P3	15.82	3.44E+07	0.9344
P4	15.82	4.59E+07	0.9344
P2/3	53.98	2.88E+05	0.9143	53.98	2.88E+05	0.9143
D1	39.65	6.02E+05	0.9541	37.65	6.20E+05	0.8251
D2	40.98	8.44E+05	0.9499	40.08	6.76E+05	0.8164
D3	4.80	1.46E+08	0.9395
D4	8.65	5.37E+08	0.9899	10.18	9.29E+08	0.9631
A2	17.90	1.42E+07	0.9183
A3	17.90	2.13E+07	0.9183
A4	17.90	2.85E+07	0.9183
R1	15.82	1.15E+07	0.9344
R2	3.87	1.12E+08	0.9592
R3	6.40	2.08E+08	0.9946	5.87	1.64E+08	0.9596
SCG
	S1			S2			S3
F1	23.43	2.25E+06	0.9164	20.78	3.13E+06	0.9213	23.43	2.33E+06	0.9189
F2	39.68	7.26E+04	0.9574	36.75	1.10E+05	0.9504	38.93	8.47E+04	0.9529
F3	39.68	1.45E+05	0.9574	36.75	2.19E+05	0.9504	38.93	1.69E+05	0.9529
P2/3	58.11	9.90E+04	0.8926	50.89	2.99E+05	0.9026	59.34	8.73E+04	0.9000
D1	35.42	1.21E+06	0.8713	30.63	2.38E+06	0.8803	36.27	1.12E+06	0.8808
D2	40.96	6.93E+05	0.8973	35.98	1.43E+06	0.9061	41.58	6.65E+05	0.9037
D4	16.15	5.97E+09	0.9765	15.97	5.72E+09	0.9749	15.75	5.39E+09	0.9728
A2	23.43	4.50E+06	0.9164	20.78	6.27E+06	0.9213	23.43	4.65E+06	0.9189
A3	23.43	6.75E+06	0.9164	20.78	9.40E+06	0.9213	23.43	6.98E+06	0.9189
A4	23.43	9.00E+06	0.9164	20.78	1.25E+07	0.9213	23.43	9.30E+06	0.9189
R2	3.81	8.00E+07	0.8005	4.11	9.05E+07	0.8535	3.88	8.30E+07	0.8204
R3	23.43	2.25E+06	0.9164	6.43	2.05E+08	0.9685	6.31	1.98E+08	0.9640
	S4			S5
F1	32.43	4.71E+05	0.9691	21.94	3.19E+06	0.9100
F2	57.14	2.20E+03	0.8887	35.06	1.97E+05	0.9487
F3	57.14	4.41E+03	0.8887	35.06	3.95E+05	0.9487
P2	18.75	1.55E+07	0.8726
P3	18.75	2.33E+07	0.8726
P4	18.75	3.11E+07	0.8726
P2/3	75.55	4.09E+03	0.9305	58.33	1.18E+05	0.9014
D1	47.15	1.58E+05	0.9200	35.60	1.35E+06	0.8820
D2	53.54	7.30E+04	0.9423	40.46	8.90E+05	0.9030
D4	16.54	6.78E+09	0.9582	15.20	4.55E+09	0.9742
A2	32.43	9.41E+05	0.9691	21.94	6.39E+06	0.9100
A3	32.43	1.41E+06	0.9691	21.94	9.58E+06	0.9100
A4	32.43	1.88E+06	0.9691	21.94	1.28E+07	0.9100
R1	18.75	7.77E+06	0.8726
R2				4.09	9.25E+07	0.8498
R3	5.57	1.48E+08	0.9719	6.45	2.10E+08	0.9686
CM
	S1			S2			S3
F1	20.99	3.33E+06	0.9012	22.61	2.66E+06	0.9139	18.75	4.26E+06	0.9058
F2	36.86	1.15E+05	0.9495	38.14	9.63E+04	0.9514	34.53	1.54E+05	0.9417
F3	36.86	2.30E+05	0.9495	38.14	1.93E+05	0.9514	34.53	3.09E+05	0.9417
P2/3	52.50	2.97E+05	0.8804	57.26	1.32E+05	0.8944	46.15	7.41E+05	0.8892
D1	31.67	2.42E+06	0.8542	34.87	1.45E+06	0.8732	27.45	4.19E+06	0.8614
D2	36.82	1.50E+06	0.8830	40.08	8.84E+05	0.8977	32.49	2.68E+06	0.8910
D4	15.77	5.49E+09	0.9754	15.68	5.34E+09	0.9729	15.65	5.33E+09	0.9739
A2	20.99	6.65E+06	0.9012	22.61	5.33E+06	0.9139	18.75	8.52E+06	0.9058
A3	20.99	9.98E+06	0.9012	22.61	7.99E+06	0.9139	18.75	1.28E+07	0.9058
A4	20.99	1.33E+07	0.9012	22.61	1.07E+07	0.9139	18.75	1.70E+07	0.9058
R2	4.40	1.05E+08	0.8414	4.09	9.16E+07	0.8326	4.61	1.13E+08	0.8771
R3	6.66	2.25E+08	0.9616	6.45	2.08E+08	0.9638	6.75	2.30E+08	0.9685
	S4			S5
F1	20.17	4.29E+06	0.8933	18.76	5.03E+06	0.9139
F2	33.46	2.61E+05	0.9469	31.73	3.46E+05	0.9568
F3	33.46	5.21E+05	0.9469	31.73	6.93E+05	0.9568
P2/3	53.66	3.02E+05	0.8836	49.63	5.06E+05	0.9020
D1	32.43	2.47E+06	0.8584	29.74	3.37E+06	0.8779
D2	37.15	1.68E+06	0.8845	34.48	2.33E+06	0.9041
D3				3.55	7.72E+07	0.8015
D4	15.24	4.66E+09	0.9759	15.34	4.68E+09	0.9774
A2	20.17	8.59E+06	0.8933	18.76	1.01E+07	0.9139
A3	20.17	1.29E+07	0.8933	18.76	1.51E+07	0.9139
A4	20.17	1.72E+07	0.8933	18.76	2.01E+07	0.9139
R2	4.58	1.15E+08	0.8626	4.58	1.15E+08	0.8880
R3	6.79	2.38E+08	0.9656	6.79	2.37E+08	0.9720

With the exception of the smallest particle size (125 μm), which follows the contracting sphere model, all particle sizes are represented by comparable reaction mechanisms for DS and SCG in the best fit models (diffusion model; Ginstling-Brounshtein or D4) (refer to Table 1). On the contrary, all the particle sizes for CM follow D4. The difference could be attributed to the significantly higher ash content in CM (~19%) compared to the other two (<2%); ash influences the chemical process and often relates to reduced energy efficiency (Tan et al. 2019). As a result, it was observed that the mixed wastes exhibited S1, S2, and S3 kinetics, indicating that the majority of the mixture had a particle size greater than 125 μm and followed the D4 diffusion model. Furthermore, a recent study on date palm fibers, at the same heating rate as this study and particle sizes of 160 μm revealed the same reaction mechanism (Raza et al. 2022). There are other examples of biomass from the literature that follows D4: alfa (Ea = 20.52 kJ/mol), cattle manure (16.47), Eucalyptus sawdust (23.11), olive kernels (27.46), sugar cane (22.69), wood sawdust (17.41) (Boumanchar et al. 2020); two Indian varieties rice husks (Singh et al. 2020). Furthermore, table S5 compares the kinetic parameter ranges for all the models to other similar feed studies. From 18.78 to 6.40 kJ/mol, the kinetic parameters Ea and A dropped with diminishing particle sizes, and for DS, 1.16 E + 10 and 2.08 E +08, 16.15–5.57 kJ/mol, and 5.97E +09 – 1.48 E+08 for SCG; and 15.77–15.24 kJ/mol and 5.49E +09–4.66E+09. Similar observations have been made for lignocellulosic materials at heating rates ranging from 5 to 20 K/min and particle sizes (26.5–925 µm) (Suriapparao and Vinu 2018). Another study utilized the Coats–Redfern method and found reducing Ea when studying particle sizes ranging between 1.400 and −0.150 mm (Haykiri-Acma 2006).

Thermodynamics of studied biomasses

Table S4 shows the different particle-sized DS, SCG, and CM thermodynamic parameters during pyrolysis, when the model achieved an R² higher than 0.8. The positive values of ∆H and ∆G reflect endothermic and spontaneous reactions for the best fit models for all biomasses and particle sizes. Additionally, negative values of $\mathrm{\Delta }\mathrm{S}$ reflect the disorder of products that is less than the initial reactants. Additionally, the best-fit reaction model, which describes the kinetics, D4 and R3 for the larger and smaller DS and SCG particles, respectively, and D4 and R3 for all sizes of particles for CM, revealed that as particle size decreased, the Gibb’s free energy increased slightly and the change in enthalpy and entropy decreased. $\mathrm{\Delta }\mathrm{H}$ values are positive for the samples. S1, S2, S3, S4, and S5 for DS was 14.09, 13.58, 10.71, 1.67, and 5.48, for SCG was 11.44, 11.25, 11.01, 0.81, and 10.47 and CM was 10.73, 10.67, 10.64, 10.22, 10.29 kJ/mol. Like Ea, the $\mathrm{\Delta }\mathrm{H}$ values decrease with particle sizes and are highest for S1 DS and lowest for mixed particle sizes. Since the difference between the enthalpy and Ea are below 10 kJ/mol, the product formation is favored for all feeds and particle sizes (Rasool et al. 2021). ∆G is positive in all cases, irrespective of the type of biomass and particle size; therefore, the reactions are spontaneous. $\mathrm{\Delta }\mathrm{G}$ values for DS are 51.23, 51.61, 52.44, 58.13 and 54.59; for SCG were 51.95, 52.03, 52.18. 59.29 and 52.34; for CM 54.77, 54.59, 54.57, 54.93, and 55.20 kJ/mol. The $\mathrm{\Delta }\mathrm{G}$ values increase with decreasing particle sizes for DS and SCG but vary in the case of CM. However, the range of the values for CM is the smallest (54.81 ± 0.23 kJ/mol). Comparably, all ΔS values for all biomasses and particle sizes are negative. $\mathrm{\Delta }\mathrm{S}$ are −0.0658, −0.0671, −0.0735, −0.0993 and −0.0869 kJ/mol K for DS, −0.1421, −0.1365, −0.1428, −0.1022, −0.0737 for SCG and −0.0727, −0.0729, −0.0728, −0.0740, and −0.0740 for kJ/mol K for CM for S1, S2, S3, S4, and S5, respectively.

Conclusion

The findings demonstrate that the biomass’s particle size influences the pyrolysis’s kinetic and thermodynamic features, including the temperature at which degradation begins, the activation energy, the change in enthalpy, and the decrease in entropy when particle sizes are reduced. The Coats–Redfern models were applied to calculate the pyrolysis kinetics of three types of biomass and showed that smaller particle sizes generally have better linear model fits and follow the diffusion model (D4) except for the smallest particle size of DS and SCG, which follows the contracting sphere model (R3). CM follows D4 for all particle sizes due to its high ash content, which influences the chemical process and reduces the energy efficiency. The kinetic parameter ranges for all models decreased with reducing particle sizes. Therefore, the results show that mixed and lower particle-sized date stones are preferred due to the lower Ea.

The thermodynamic analysis showed that endothermic and spontaneous reactions occurred for all particle sizes and biomass types, and the disorder of products was less than the initial reactants. The enthalpy and entropy change decreased with decreasing particle size, while the Gibbs free energy increased slightly. The product formation was favored for all feeds and particle sizes. The range of ΔG values was smallest for CM. All ΔS values for all biomasses and particle sizes were negative.

This study focused on the major loss region (Stage II – devolatilization) since it guides the pyrolysis system; however, the effect of particle sizes on dehydration and char degradation kinetics needs to be analyzed. Additionally, model-free kinetics is necessary to validate the kinetic and thermodynamic parameters to avoid the shortcomings of assuming that the major loss region follows a single reaction mechanism. Lastly, more research is required to understand and build pyrolysis systems for effective waste management, including studied on techno-economic feasibilityand effects of co-pyrolyzing diverse types of wastes

Acknowledgements

Open Access funding provided by the Qatar National Library.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, S.M., upon reasonable request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15567036.2023.2196945

Additional information

Funding

This publication was made possible by NPRP – Standard (NPRP-S) 11 cycle grant - NPRP11S-0117-180328 from the Qatar National Research Fund (a member of the Qatar Foundation). The findings herein reflect the work and are solely the responsibility of the authors.

Notes on contributors

Sabah Mariyam

Sabah Mariyam is a Ph.D. candidate in the Division of Sustainable Development at Hamad Bin Khalifa University (HBKU) in Qatar. Her research interests are focused on sustainable energy processes, with a particular emphasis on waste-to-energy generation using thermochemical processes like pyrolysis. Sabah has published several works in her field, including empirical prediction modeling of products, multi-biomass pyrolysis kinetics and thermodynamics, and product characterization. Through her research, she aims to develop innovative solutions for sustainable energy generation that can contribute to a cleaner, more sustainable future for all.

Tareq Al-Ansari

Dr. Tareq Al-Ansari acquired a BEng. in Mechanical Engineering from the University College of London and an MPhil. in Engineering for Sustainable Development from the University of Cambridge and completed his Ph.D. at Imperial College London in Sustainable Development and Environmental Engineering. Currently, he is an Associate Professor at the College of Science and Engineering at HBKU within the Division of Sustainable Development, where he is the division head.

Gordon McKay

Professor McKay has almost four decades of experience in academia and industry. He is presently a Professor of Sustainable Development at Hamad Bin Khalifa University. He has held previous positions at the Hong Kong University of Science and Technology (HKUST) as Head of the Department and Professor of Chemical and Biomolecular Engineering. Before his academic career, he spent 10 years in industry and established his own company, Consultancy Process Engineering and Management Systems, which was later subcontracted to Foster Wheeler, Ireland. Professor McKay has authored eight books, over 800 publications in international refereed journals, and co-authored the third most cited paper in the SCI Chemical Engineering sector.