Parametric study of waste tires through CO2–aided gasification: An investigation of enhanced H2-production using artificial neural network model

doi:10.21203/rs.3.rs-1652947/v1

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Parametric study of waste tires through CO2–aided gasification: An investigation of enhanced H2-production using artificial neural network model

https://doi.org/10.21203/rs.3.rs-1652947/v1

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This study presents the waste tire gasification in CO₂ atmosphere aiming to produce higher H₂ production. At first stage, the waste tire was gasified in horizontal tube reactor in CO₂ assistance under different ranges of reaction temperature, heating rate, and residence time. The maximum H₂ and gas yields of 46.28 mol % and 72.43 wt. % were obtained at higher temperature of 900 ℃, lower heating rate of 10 ℃/min), and lower residence time of 20 min. While; the tar and char products were decreased considerably from 15.34 to 4.24 wt. % and 35.23 to 23.25 wt. %, respectively. LHV and H₂/CO molar ratio of syngas ranged from 9.80-11.07 MJ/Nm³ and 1.41 to 2.18, respectively. Artificial neural network (ANN) model provided a strong relationship among the three variables and the four responses .i.e. estimated regression coefficient (R²) values were obtained as; 0.90, 0.94, 0.93, and 0.96 for H₂, CO, CO₂, and CH₄ respectively. Later, the waste tire was heated from room temperature to 900°C under the heating rates of 10, 20, and 30 ℃/min in thermogravimetric analyzer (TGA). TGA thermograms showed three different degradation stages correspondence to moisture loss, devolatilization and char gasification. The kinetic parameters (activation energy (E_a)), R² were estimated using Kissinger–Akahira–Sunose (KAS) model. The E_a and R² were found to be 38.99 kJ/mol and 1.0 for stage 1, 63.01 kJ/mol and 0.94 for stage 2, and 84.73 kJ/mol and 0.91 for stage 3.

Waste tires

Hydrogen

CO2 gasification

Artificial neural network

Thermogravimetric analysis (TGA)

Globally, the rapidly growing population and advancement in technology have increased the energy demand hence; causing a rapid depletion of available energy reserves .i.e. fossil fuels (coal, gas, and oil) (Policella et al. 2019). Estimations reveal that the available energy reserve will be consumed in next hundred years whereas the energy demand will be increased to sixty percent by 2040 (Shafiee and Topal 2009). In that quest the enormous sustainable and renewable energy resources (Lu et al. 2009) were searched out that varied from lower energy (biomass and municipal solid waste materials) materials to higher energy (plastics and tire) materials (Xu et al. 2018; Ahmed and Gupta 2011). From all, the waste tires are considered more suitable on the account of significant amount of energy value .i.e. higher than coal (Martínez et al. 2013). In recent times, the tire demand is continuously increasing owing to prompt growth in vehicle industry directly linked with increasing population. In 2017, the global tire demand was about 2.9 billion tons whereas; only 1 billion was properly disposed-off (Policella et al. 2019; Machin et al. 2017). Hence, the major portion turned out as waste directed to landfills (Xu et al. 2018; Kumaravel et al. 2016; Torretta et al. 2015). However; the contaminations resulting from landfilling in the form of toxic leachate and CH₄ emission have faced challenges on its performance which declares the process as incompatible and unreliable (Xu et al. 2018; Zhou et al. 2014). The safe disposal of waste tires along with energy recovery (syngas (Toledo et al. 2018; Al-Rahbi et al. 2017; Martínez et al. 2014) liquid fuels (Yazdani et al. 2019; Wang et al. 2019; Miandad et al. 2018) char (Choi et al. 2014), carbon nanomaterial (Maroufi et al. 2017), and carbon black (Martínez et al. 2019)) can be accomplished by pyrolysis and gasification techniques. From the two, the gasification is a better choice because of offering dual solution .i.e. destruction of the bulk waste with efficient conversion to biofuel. The gasification converts carbonaceous materials in the presence of steam, air, O₂, and or CO₂ gasifying agents at high temperatures into valuable gases (H₂, CO, CO₂, and CH₄) with high heating value (Burra et al. 2018). Among other gasifying agents, the CO₂ gasification is an energy-intensive method. It is considered as a path to the constructive development of carbon capture and storage technologies (CCS) which helps to reduce atmospheric CO₂ (Roncancio et al. 2021). During the CO₂ gasification, the CO₂ adsorbed on the active sites of the available char disintegrates the hydrogen (H) present in char, thereby to weaken the strong bonding among the internal H and the char matrix which increase the possibility of the H mobility (Gao et al. 2013). In addition, in CO₂ gasification the Boudouard reaction (CO₂ + C ↔2CO) and dry reforming of hydrocarbons (xCO₂ + CxHy↔2xCO + y/2H₂) convert CO₂ to produce CO (Lahijani et al. 2015). The subsequent CO can be consumed via water-gas shift reaction (CO + H₂O → H₂ + CO₂) to produce H₂-rich syngas which can be used as an intermediate fuel in the production of various chemicals and/or utilized in the power generation (Ahmed and Gupta 2009). Indeed, CO₂ acts as a catalyst in the gasification process because it not only improves the thermal cracking of the feedstock but also decreases the tar content which ultimately enhances the total gas yield (Yao et al. 2017; Couto et al. 2016). In addition, the resulting product is also rich in CO hence useful to produce value added products (Ahmed and Gupta 2009).

Until now, several studies have been conducted in the field of waste tire gasification and co-gasification. Besides, the researches regarding char gasification while using CO₂ gasifying agent have also been investigated. Among them (Striūgas et al. 2013) explored the tar destruction by adding CO₂ over activated char obtained from waste tire. Their study revealed the least influence in terms of tar destruction as compared to other thermochemical methods. Moreover, (Karatas et al. 2012) conducted a research on waste tire gasification while employing air and steam, air and CO₂, and steam gasifying agents in fluidized bed gasifier having bubbling effect. Their study focused the effects of the particle size, gasifying agent, and CO₂ to air ratio for attaining better understanding in terms of quality of product gas. Furthermore, (Lahijani et al. 2013) explored out a detailed research on CO₂ gasification of waste tire-char and biomass (fruit bunch). The obtained results disclosed that the tire char had low reactivity, which was mainly due to graphitic structure and non-porous property as well. Besides, (Lee et al. 1996) conducted a study in terms of gasification kinetics while using waste tire-char as raw material in CO₂ atmosphere. The results revealed that rate of gasification was not effected due to size and mass of char. In addition, a modified volume reaction model was used to estimate the reaction order, pre-exponential factor, and activation energy. (Scott et al. 2005) studied gasification rates while using tire chars in CO₂ atmosphere. Among them the reaction rate of catalytic activity was determined from the associated ash whereas the reaction rate of carbon with CO₂ was ascertain through chemical kinetics as the char reactivity is dependent on its pore structure. (Al-Rahbi et al. 2017) studied H₂-rich syngas from co-gasification of biomass and tire char (derived from pyrolysis) in a two stage fixed bed reactor in steam atmosphere. Hence, the certain reduction in the tar content was observed which ultimately increased the H₂ production at higher temperature and higher steam injection with the acquirement of H₂/CO (molar ratio) ranged from 1.3 -2.

Even though, the numerous studies have been conducted on gasification and co-gasification of waste tires/its char in presence of different gasifying agents but it still lacks an optimum H₂ production in presence of CO₂ that is not only an environmental friendly but also an economical process. Therefore; this study aimed to focus maximum H₂ production from waste tires in the CO₂-assitance. Thermogravimetric analyzer (TGA) and horizontal tube reactor (HTR) were used to investigate the waste tire under different process parameters such as; reaction temperature, heating rate, and residence time. The TGA data was validated by using Kissinger–Akahira–Sunose (KAS) model. Whereas; an artificial neural network model was developed for the validation of data obtained from HTR gasification. In addition, the composition of syngas, product distribution, H₂/CO, and heating value of syngas were investigated. The results revealed the maximum H₂ production from waste tires gasification offer an insight of energy reclamation from waste tires using CO₂ medium.

2.1. Collection, preparation, and characterization of waste tires

Waste tires were collected from a tire scrap shop. The muddy tires were firstly water washed and then sun dried for 2 days. To remove out the steel wires, big pieces were cut down into the smaller pieces. In order to achieve better gaseous results and TGA apparatus requirement the sample size was reduced to about 1 mm. A standard method (GB/T2831-2012) was used to perform the proximate analysis which determined the amounts of moisture, volatiles, and ash in the sample. The CHN analyzer was used to determine the amounts of carbon, hydrogen, sulfur, and nitrogen in the sample. Proximate and ultimate analysis of the tire sample is illustrated in Table 1.

Table 1

Fundamental characteristics of the waste tire
Waste tire sample	Proximate Analysis (wt % db)				Ultimate Analysis (wt % db)
	MC	VM	FC*	Ash	C	H	O*	N	S
	3.72	59.80	13.39	23.09	72.29	6.53	19.08	0.40	1.70

Where; Wt. %, db, MC, VM, FC, C, H, O, N, and S denote the weight percentage, dry basis, moisture, volatile, fixed carbon, carbon, hydrogen, oxygen, nitrogen, and sulfur, respectively. Whereas, * refers to the calculations were made by difference method.

2.2. Waste tire gasification in thermogravimetric analyzer (TGA)

The thermal decomposition of the waste tire was conducted using thermogravimetric analyzer (TGA) with model-TASDTQ600. In all experiments, the mass of 4 ± 0.1 mg was used. The weight loss calculations were automatically documented as a function of time by the TGA. In each experiment, the sample was kept under an isothermal condition at 25 ℃ for 60 minutes and then further heated to 900 ℃ at the heating rates of 10, 20 and 30 ℃/min. Argon (Ar), a carrier gas with a flow rate of 100 mL/min flowed during the isothermal condition. Whereas; the CO₂ as gasifying agent flowed with 100 mL/min till the end of process. The experimental set up is shown in Fig. 1.

2.3. Gasification in horizontal tube reactor

Horizontal tube reactor (HTR) with tube diameter of 14 mm was used to perform the gasification experiments under the range of different parameters. The designed parameters included; temperature of 700, 800, and 900 ℃, heating rate of 10, 20, and 30 ℃/min, and residence time of 20, 30, and 40 min. In each run, the mass (tire sample) of five grams was loaded in sample boat and heated at desired heating conditions with CO₂ flow and pressure of 15 mL/min and of 1 bar, respectively. The produced gas samples were collected in the sampling bag. For authentication of results, the experiments were repeated.

2.4. Chromatography of the gas

Gas chromatography (GC) unit with model-6890 Agilent was used to check the composition of collected gas. Tar and char products were computed by weight difference.

2.5. Data processing methods

The lower heating value (LHV) was calculated by using the formula below;

$$\text{L}\text{H}\text{V}\left(\frac{\text{M}\text{J}}{\text{N}\text{m}3}\right)=\frac{\left[\text{C}\text{O}\text{*}126.36+{\text{H}}_{2}\text{*}107.97+\text{C}{\text{H}}_{4}\text{*}358.18\right]}{1000} \left(1\right)$$

Where; H₂, CO and CH₄ are the molar percentage of the obtained gases generated from the HTR gasification.

Oxygen was calculated by the difference method, as below;

O = 100% – C – H –N – S (2)

Where; C, H, N, and S were the carbon, hydrogen, nitrogen, and sulfur contents obtained from the ultimate analysis of waste tire.

Fixed carbon was calculated by the difference method by using the formula;

FC = 100% — MC–VM–ash (3)

Where; MC, VM, and ash were the moisture, volatile, and ash contents obtained from the proximate analysis of waste tire.

2.6. Artificial neural network modelling:

Artificial neural network (ANN) is an empirical modelling approach used for estimating the relationship among the process parameters (input) and the products (output) via hidden layers. In addition, it also helps to estimates the prediction of the experimental and predicted results. Hence, it is very easily to comprehend the performance of each parameter on product with the less complexities, time saving and without needing a much of information on the experimental procedure. For ANN, the data obtained from tubular gasification was used. For ANN, JMP.pro software was used hence facilitated to develop a model for revealing the correlation amongst the process parameters (reaction temperature, heating rate, residence time) and the obtained gaseous products (H₂, CO, CO₂, CH₄).

2.7. Kinetic study

The model free kinetic fitting is the best tool to analyze the non-isothermal kinetics data obtained from TGA. Kissinger–Akahira–Sunose (KAS) model offers the estimation of Arrhenius parameters; activation energy (Ea) and pre-exponential factor (Ko) during the gasification of any solid such as waste tire. This method is highly accurate in estimating the results in assessing the kinetic parameters without estimating E (Kissinger 1956). This method has been comprehensively employed to calculate the thermal disintegration and behavior of various solid material such as biomasses (Wang et al. 2013; Slopiecka et al. 2012). Therefore, the rate constant (K) with respect to temperature (T) can easily be computed through Arrhenius equation as below;

$$\text{k}={\text{k}}_{0}\text{e}\text{x}\text{p}(-\frac{\text{E}}{\text{R}\text{T}})$$

Where, the k₀, R, and Ea represent the pre-exponential factor ($1/\text{s})$, general gas constant , and activation energy ($\text{J}/\text{m}\text{o}\text{l})$, respectively. In the case, when the temperature T changed (multiple heating rates) with respect to time at a constant heating rate then in that case the heating rate (β) can be stated as;

T = T_i + β_t (5)

Where; T is the temperature at the reaction region and Ti is the initial temperature. Iso-conversional contrivances are performed under a range of experiments by varying the heating rates. The calculation of E was done by using KAS method on the basis of the equations below;

$\text{ln}\left(\frac{{\beta }}{{\text{T}}_{\text{P}}^{2}}\right)$ = -$\frac{\text{E}}{\text{R}{\text{T}}_{\text{p}}}+\text{ln}\frac{{\text{k}}_{\text{O}}\text{E}}{\text{R}}$ (6)

(7)

Where, Tp is the maximum temperature (at peak) of the pyrolysis stage (devolatilization) during the thermal breakdown of the tire sample obtained from the TGA data. Here, the heating rate may be given as;

β = $\frac{{\text{d}}_{\text{T}}}{{\text{d}}_{\text{t}}}$(8)

Hence, the values of Ea at various peaks from TGA curves may be calculated from a scheme $\text{ln} \frac{{\text{d}}_{\text{T}}}{{\text{d}}_{\text{t}}}$versus$\frac{{10}^{3}}{{\text{T}}_{\text{P}}}$.

3.1. Gasification in thermogravimetric analyzer

Prime objective to analyze a solid sample in a thermogravimetric analyzer (TGA) is to examine the composition correspondence to different ranges of the temperature. Figure 2 shows the total weight loss (TG curves) in wt. % and derivative weight loss (DTG curves) in wt. % min^− 1 of waste tire at three heating rates (10, 20, and 30 ℃/min) in the presence of CO₂ gasifying medium. In general, the gasification process is characterized by three reaction stages. In the first stage, the heating and dehydration of the organic material are taken place. In the second stage, the pyrolysis of the dried material is occurred to extract the volatile species (Sircar et al. 2014). The third stage encompasses the oxidation of the char by an oxidizing agent which is primarily an oxygen supplying source to help out the insufficient burning of the solid material. The peaks accounted for stage 1 appeared at ~ 258 ℃, 242 ℃, and 253 ℃ in the temperature ranges of 150–325 ℃, 138–305 ℃, and 135–323 ℃ and the heating rates of 10, 20, and 30 ℃/min respectively. The weight loss during these range were ~ 8%, 4%, and 3% respectively. In stage 2, the peaks appeared at ~ 454 ℃, 411 ℃, and 424 ℃ in the temperature ranges of 336–509 ℃, 313–482 ℃, and 336–498 ℃ and 10, 20, and 30 ℃/min respectively. The weight loss for these range were ~ 58%, 51%, and 50% respectively. Whereas for the last stage 3, the appeared peaks at ~ 717 ℃, 675℃, and 699 ℃ were found in the temperature ranges 605–750 ℃, 572–707 ℃, and 603–731 ℃ for 10, 20, and 30 ℃/min respectively. The weight loss for these range were ~ 9%, 5%, and 6.5% respectively. There was a common peak from 350–405 ℃ was observed for all heating rates which was overlapped by a higher peak adjacent to the right side of it. During the gasification process at all the heating rates, the first peak was displayed after 170 ℃ indicated that very less moisture exist into the tire samples. The final residues (ashes) correspondence with 10, 20 and 30 ℃/min heating rates were obtained (from TG thermographs) as 24.5%, 40% and 39.5%, respectively. The maximum weight losses of 58% was observed at lower heating rate of 10 ℃/min during the stage 2. Hence, this weight loss attributed to the devolatilization of organic materials owing to the samples maximum habitation under the temperature which is in an agreement with the study reported by (Ming et al. 2009) that the lower heating rate leads to higher decomposition in comparison with the higher heating rate while the final temperature kept identical. The slow heating rates over faster offer long lasting temperature to penetrate the intermolecular (polymeric) structure of the material. Therefore, the longer residence time under similar final temperatures consequences in more weight losses. The total and stepwise mass loss of waste tire are illustrated in Fig. 2.

3.2. Gasification in horizontal tube reactor

3.2.1. Influence of reaction temperature on H₂ and other products of the process

Gasification of waste tire was performed in horizontal tube reactor (HTR). In gasification, the temperature is considered to be the most important parameter dominates the overall conversion of feedstock with significant impact on the composition of final product. Indeed, the temperature provides the sufficient heat to decompose the internal bonds along with the provision of favorable (thermodynamically) conditions for reverse Boudourad (C + CO₂ → 2CO) reaction, steam methane reforming (CH₄ + H₂O ↔ CO + 3H₂) reaction, and conversion of tar and char products to CO and H₂ species. (Figueira et al. 2015; Lopez-Gonzalez et al. 2014) identified that the upper-level temperature caused more promising conditions for the thermal decomposition of C-C bonds, thus the much probability of occurring steam methane reforming and water gas shift reactions (Figueira et al. 2015; Lopez-Gonzalez et al. 2014).

The H₂ concentration and total gas yield increased from 35.95 to 46.28 mol% and 49.36 to 72.43 wt. % respectively when temperature raised from 700 to 900 ℃, as shown in Fig. 3. On the other hand, the tar and char products were reduced from 15.34 to 4.24 wt. % and 35.23 to 23.25 wt. % respectively. This phenomenon is attributed to the occurring of secondary reactions via water gas-shifting and steam-methane reforming reactions. Indeed, the aforementioned reactions occurring at high temperatures resulting in high increase the H₂ concentration (see Fig. 3a) followed the Le Chatelier’s principle. Le Chatelier principle states that the higher temperature supports the reactants in exothermic reactions and products in endothermic reactions (Lv et al. 2004). The endothermic reactions for instance; Boudouard and steam-methane reforming reactions were played a significant role in the increment of H₂ and CO and marginal reduction in the concentrations of CH₄ (12.76 to 3.91 mol %) and CO₂ (30.51 to 22.84 mol %). It can be seen that, the CO production increased from 20.76 to 27.95 mol % when temperature raised from 700 to 800 ℃. Whereas; its concentration slightly decreased to 26.95 mol% when temperature raised to 900°C. This fluctuating CO phenomenon also heavily affected the LHV of syngas which was declined from 11.07 to 9.80 MJ/Nm³. Another key reason in the LHV declination was the reasonable declination in CH₄ which contributes a higher share in the heating value. Extensive studies have been conducted to explore the influence of reaction temperature in the gasification of different solid wastes. A research conducted by (He et al. 2009) on the gasification of polyethylene (a polymer material) in the presence of steam gasification agent reported that the amount of H₂ and CO was increased from 16.92%-36.98% and 20.33%-27.37% respectively when process temperature elevated from 700 to 900 ℃. While; the concentration of CO₂ and CH₄ were declined from 35.28%-20.78% and 21.44%-9.95% respectively. (Skoulou et al. 2008) carried out a co-gasification study of two biomasses in a downdraft fixed bed gasifier hence reported that the CO increase is highly reliant on the temperature which increased the CO concentration from 11.5%-20% when changed from 650 ℃-1050 ℃. In addition, a slight rise in the H₂ (5.65–9.33%) was also experienced. They also reported that, the CO₂ and CH₄ concentrations were decreased which follows the similar trends as our study. Hence, the obtained H₂-rich syngas (46.28 mol %) is highly valuable which may be utilized for certain industrial applications .i.e. transportation fuel, power generation etc.

3.2.2. Influence of heating rate on H₂ and other products of the process

Figure 4a represents the results of the production of H₂ and other gasification products obtained from the waste tire CO₂ assisted gasification. It can be seen that, the H₂ yield and total gas yield declined from 43.86 to 40.81 mol% and 71.03 to 64.81 wt. % when heating rate increased from 10 to 30°C/min. The increasing heating rates also decreased CH₄ content from 5.09 to 3.13 mol % (see Fig. 4b). Whereas; CO and CO₂ production were slightly increased from 26.32 to 27.18 mol% and 24.71 to 28.86 mol% respectively. Hence, no obvious influence of varying heating rate on syngas composition was experienced. In general, the higher heating rate which is indeed a rapid supply of thermal energy consequences a minimum retaining time by the solid causes an insufficient burning. Therefore, a lengthiest time might be needed for the partial oxidation to maintain the equilibrium temperature conditions. In our case, this could be the reason behind the minimum conversion of tire waste (~ 70% feedstock conversion at lower heating rate of 10 ℃/min and ~ 60–65% conversion at higher heating rates of 20 and 30 ℃/min) which might not be attained at higher heating rate owing to lower solid conversion rates and heat transfer restrictions (Jayaraman et al. 2015; Wang et al. 2008). In addition, the lower heating rate not only promoted the higher feedstock conversion but also provided the probability to occurring and completion of secondary reactions therefore; enhanced the overall syngas product (Demirbas 2005). Hence, it can be established that the influence of heating rate on the waste tire under CO₂ atmosphere was insignificant. However; the lower heating rates in comparison with higher heating rates were evidenced to be favorable in terms of higher H₂ production .i.e. 43.86 mol%, 41.39 mol%, and 40.81 mol% at 10, 20, and 30 ℃/min, respectively.

3.2.3. Influence of residence time on H₂ and other products of the process

It can be seen that, the increment in the residence time (20 to 40 min) decreased the H₂ production from 42.86 to 39.48 mol% whereas the total gas yield increased from 69.64 to 71.01 wt. %. The tar and char products varied between 7.43 to 5 wt. % and 22.84 to 23.94 wt. % respectively. On the other hand, the concentration of CO and CO₂ were slightly increased from 24.02.76 to 24.32 mol% and 26.91 to 32.75 mol% respectively whereas the CH₄ concentration decreased from 6.19 to 3.43 mol%. It was noteworthy that, the increase in residence time increased the CO content to about 0.5 mol% however; CH₄ was decreased to about 3 mol% (from 6.19 to 3.43 mol%) which consequently decreased the LHV of syngas from 9.88 to 8.56 MJ/Nm³. In summary, the residence time of 20 min was the advantageous to H₂ production which suggests that after the time duration of 20 min the feedstock could be under gone secondary reactions prominent to boost the CO₂ production that can also be seen in Fig. 5 (a-b). It means, by varying the residence time a sufficient time interval is therefore needed for optimal H₂ production. On the other hand, by providing a higher residence time may lead to occur some surplus unrequired secondary reactions that may decreased the possibility of producing much H₂.

3.3. Artificial neural network model

For validation of the data, artificial neural network (ANN) model was used. Indeed, the ANN model simulated the effect of process parameters (reaction temperature, heating rate, and residence time) on the final products obtained of tire gasification. Very similar approaches have also been applied by previous researchers (Puig-Arnavata et al. 2013; Guo et al. 2001) hence; obtained a better results. Figure 6 represents the ANN model for the waste tire gasification while Fig. 7 illustrates the variables analysis profile for the releasing gases against the applied parameters, which has already been discussed above. Further comprehensive data regarding the ANN models and properties may be found somewhere else (Demuth et al. 2010).

In addition, it can also be seen a good agreement between the experimental and predicted results .i.e. the value of regression coefficient (R²) for H₂, CO, CO₂, and CH₄ was estimated to be 0.90, 0.94, 0.93, and 0.96 respectively. Figure 8 demonstrates the tire gasification responses as functions of temperature, heating rate, and residence time played a vital role hence acknowledged as key parameter that highly influenced the waste tire gasification. Higher reaction temperature not only supported the higher H₂, CO, and total gas yield but also reduced a substantial amount of CO₂ which is a beneficial process preventing from the greenhouse gas emission. Furthermore, the higher temperature has also a very important role in the reduction of tar amount. It can be summarized that in order to get an optimum condition it is very essential to study the effect of dynamic parameters for gasification system such as; the reaction temperature and heating rate.

3.4. Kinetic Model

In order to calculate the energy consumed during the gasification of waste tire, the estimation of kinetic parameters is essential which can be calculated by Arrhenius equation. To calculate the kinetic parameters, the TGA results were evaluated by model free fitting method. In this study, the pre exponential factor (K₀) and activation energy (Ea) were estimated by the non-isothermal iso-conversional technique by Kissinger–Akahira–Sunose (KAS) by drawing $\frac{\beta }{{\text{T}}_{\text{P}}^{2}}$versus $\frac{{10}^{3}}{{\text{T}}_{\text{P}}}$ which produces a straight line. The values of E and activation energy and K₀ factor computed at stage 1, stage 2 and stage 3 analogous to the heating rates; 10, 20, and 30 are illustrated in Table 2. The stages denote the burning and releasing of different entities specific to the range of their temperature for instance; the dehydration, devolatilization, and gasification of char etc. High value of R² of 1.0 for stage 1, 0.94 for stage 2, and 0.91 for stage 3 showed an analytic of suitability and authentication of the model to define the gasification behavior of waste tire, as shown in Fig. 9. The estimated values of Ea for stage 1, stage 2, and stage 3 were 38.99 kJ/mol, 63.01 kJ/mol, and 84.73 kJ/mol respectively. The rate of the weight loss of any solid during the gasification depends on its releasing or breaking components such as; moisture removal, volatile releasing, and conversion of char (carbon) into gases through gasifying agent hence; the required activation energy may fluctuate with temperature according to the nature of specific portion of the subjected material (Liu et al. 2012; Liu et al. 2002;). Hence, the results found in our study may provide a significant fact for the waste tire gasification in CO₂ atmosphere.

Table 2

Values of the activation energies and other Arrhenius parameters for the different degradation stages (obtained from TGA) at the heating rates of 5, 10, and 15 ℃/min.
Parameters	Stage 1			Stage 2			Stage 3
	Heating rate ( β) in °C/min
	10	20	30	10	20	30	10	20	30
Peak, Tmax (°C)	330	313	309	531	507	498	747	706	733
Ea (kJ/min)	38.99			63.01			84.73
K₀ (1/min)	2.943 x 10¹³			4.128 x 10¹²			3.793 x10¹³
R²	1			0.94			0.91
Slope (-Ea/R)	− 4.69			− 7.57			− 10.19

Gasification of waste tires was performed in thermogravimetric analyzer (TGA) and horizontal tube reactor (HTR) in CO₂ atmosphere to check the maximum H₂ production under different parameters. The main findings are set out as below:

CO₂ as gasifying agent was remained beneficial in both perspectives .i.e. thermal degradation to ~ 60% and H₂ production to ~ 50%. During TGA gasification, the maximum feedstock conversion of ~ 58% was occurred at lower heating rate of 10 ℃/min. While; during HTR gasification the maximum H₂ production of 46.28 mol % was obtained at higher temperature of 900°C, lower heating rate of 10°C/min, and lower residence time of 20 minutes. Maximum LHV (11.07 MJ/Nm³) was obtained at a moderate temperature of 800°C which was decreased to 9.80 MJ/Nm³ when the temperature increased to 900°C. The key reason behind the LHV decrease was the substantial decrease in CH₄ (from 12.76 to 3.91 mol %). While; the H₂/CO ranged from 1.41 to 2.18 hence reveals its use on industrial scale. The validation of the model through Kissinger–Akahira–Sunose (KAS) revealed the accurate (value of R² near to 1) results. Hence; the R² values for the respective thermal stages were obtained as; 1.0 for stage 1, 0.94 for stage 2, and 0.91 for stage 3. The application of artificial neural network (ANN) provided very best model on the waste tire gasification .i.e. the R² values were lied very close to 1 for instance; 0.90, 0.94, 0.93, and 0.96 for H₂, CO, CO₂, and CH₄ respectively.

In overall, the process temperature showed a strong influence on the characteristics of syngas such as; composition and yield (49.36 to 72.43 wt. %) of syngas. In addition, higher temperature also highly affected the tar and char products which were decreased from 15.34 to 4.24 wt. % and 35.23 to 23.25 wt. % respectively.

Ethics approval- All ethical standards have been followed during this research.
Consent to participate- Not applicable.
Availability of data and materials- The data will be provided on demand and all data generated or analysed during this study are included in this published article. The used materials (waste tires, carrier gases and chemicals) were collected and bought locally .i.e. from Pakistan.
Competing interests- The authors declare no competing interest.
Funding- This study work was funded by National Key R&D Project of China (2020YFC1908604). High Technology Support Project of Tianjin (18ZXSZSF00120).
Authors’ Contribution- The conception, experimental work, and writing of initial draft were accomplished by Imtiaz Ali Jamro.

The kineting modelling and proof reading of the draft and experimental work was co-jointed by Ali Mujahid

Aritfical neural netwrok (ANN) modelling was done by Syyed Adnan Raheel Shah.

The proof reading was performed by Muhammad Saffar Korai.

Thermogravimetric data was analyzed by Humair Ahmed Baloch.

The dart reviewing were done by Guanyi Chen.

The entire research work was supervised and funded by Wenchao Ma.

Acknowledgements- The authors are thankful to Tianjin University and School of Environmental Science and Engineering for completing this work.

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Parametric study of waste tires through CO2–aided gasification: An investigation of enhanced H2-production using artificial neural network model

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Collection, preparation, and characterization of waste tires

2.2. Waste tire gasification in thermogravimetric analyzer (TGA)

2.3. Gasification in horizontal tube reactor

2.4. Chromatography of the gas

2.5. Data processing methods

2.6. Artificial neural network modelling:

2.7. Kinetic study

3. Results And Discussion

3.1. Gasification in thermogravimetric analyzer

3.2. Gasification in horizontal tube reactor

3.2.1. Influence of reaction temperature on H₂ and other products of the process

3.2.2. Influence of heating rate on H₂ and other products of the process

3.2.3. Influence of residence time on H₂ and other products of the process

3.3. Artificial neural network model

3.4. Kinetic Model

Conclusions

Declarations

References

Status:

Version 1

Parametric study of waste tires through CO2–aided gasification: An investigation of enhanced H2-production using artificial neural network model

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Collection, preparation, and characterization of waste tires

2.2. Waste tire gasification in thermogravimetric analyzer (TGA)

2.3. Gasification in horizontal tube reactor

2.4. Chromatography of the gas

2.5. Data processing methods

2.6. Artificial neural network modelling:

2.7. Kinetic study

3. Results And Discussion

3.1. Gasification in thermogravimetric analyzer

3.2. Gasification in horizontal tube reactor

3.2.1. Influence of reaction temperature on H2 and other products of the process

3.2.2. Influence of heating rate on H2 and other products of the process

3.2.3. Influence of residence time on H2 and other products of the process

3.3. Artificial neural network model

3.4. Kinetic Model

Conclusions

Declarations

References

Status:

Version 1

3.2.1. Influence of reaction temperature on H₂ and other products of the process

3.2.2. Influence of heating rate on H₂ and other products of the process

3.2.3. Influence of residence time on H₂ and other products of the process