Kinetic and Thermodynamic Analysis of Fried Tilapia Fish Waste Pyrolysis for Biofuel Production

Abstract

Converting food waste into biofuel resources is considered a promising approach to address the rapid increase in energy demand, reduce dependence on fossil fuels, and decrease environmental hazards. In Egypt, large quantities of fried tilapia fish waste are produced in restaurants and households, posing challenges for proper waste management due to its decaying nature. The current study investigates the kinetic triplet and thermodynamic parameters of fried tilapia fish waste (FTFW) pyrolysis. Kinetic analysis was carried out using four iso-conversional models, Friedman, Kissinger–Akahira–Sunose (KAS), Flynn–Wall–Ozawa (FWO), and Starink, at heating rates of 10, 15, and 20 °C/min. The study findings indicate that FTFW decomposes within the temperature range of 382–407 °C. The estimated activation energy using the Friedman, FWO, KAS, and Starink methods ranged from 43.2 to 208.2, 31.3 to 148.3, 22.3 to 179.3, and 24.1 to 181.3 kJ/mol, respectively, with average values of 118.4, 96.7, 109.7, and 100.5 kJ/mol, respectively. The average enthalpy change determined using the Friedman, FWO, KAS, and Starink methods was 113.45, 91.78, 95.58, and 104.73 kJ/mol, respectively. The average values of Gibbs free energy change for the Friedman, KAS, FWO, and Starink, methods were 192.71, 171.04, 174.83, and 183.99 kJ/mol, respectively.

1. Introduction

The increasing demand for energy concerns about the depletion of natural resources and the impacts of global warming have obliged energy-consuming countries to explore alternative energy sources, particularly biofuels. Biofuels have generated increasing interest as a source of renewable energy due to their considerable environmental, social, and economic benefits [1,2]. Biofuels are produced through the conversion of different waste using different processes. The selection of proper techniques depends on different parameters, such as feedstock availability, energy output, economic levels, and environmental standards. Among the numerous types of waste used, food waste has emerged as an efficient and biodegradable source of biofuel. Food waste is primarily produced from sources like households, food processing plants, and catering industries, such as restaurants, cafeterias, hotels, and event facilities [3,4].

According to the Food and Agricultural Organization, more than 1.3 billion tons of food, including vegetables, fruits, dairy products, meat, and cereals, are wasted annually [5,6]. Without proper treatment, food waste can lead to severe environmental and health problems, such as harmful gas emissions, foul odors, virus transmission, and bacterial leaching [7,8]. Traditional food waste management techniques, such as animal feeding, anaerobic digestion, composting, and landfilling, are often inefficient and pose additional challenges, including low efficiency, extensive land use, hazardous gas emissions, and risks of virus transmission [9,10].

Fish waste, a by-product of fish-based food production, is another significant component of food waste. It is generated from restaurants, households, fish processing industries, and fish markets. Depending on the processing method, approximately 60% of a fish’s weight is discarded as waste. Common fish waste components include fins, bones (9–15%), skin (1–3%), scales (5%), head (9–12%), and viscera (12–18%) [11]. Improper disposal of fish waste contributes to considerable biological pollution, which is a topic of increasing international concern due to its environmental and economic impacts [12].

Traditional methods for utilizing fish waste, such as incineration, composting, and landfilling, are not economically viable and are discouraged because of associated health risks, environmental hazards, and unpleasant odors [13]. As a result, alternative methods have been developed to convert fish waste into valuable products, particularly biofuels. Fish waste can be converted into biofuel through various thermochemical processes, including combustion, liquefaction, gasification, and pyrolysis [14].

Pyrolysis is a highly effective thermochemical conversion technique that decomposes raw materials under high-temperature, oxygen-free conditions to produce liquid, gas, and solid fuels [15,16,17]. This method has proven to be a reliable approach for recycling various types of waste, including fish waste [18]. In addition to fish waste, other materials have been successfully used as pyrolysis feedstocks, such as plastic waste [19], sewage sludge [20,21], wood waste [22,23], healthcare waste [24], food waste [25,26], and agro-industrial waste [27].

The efficient design and operation of waste pyrolysis requires a thorough analysis of the pyrolysis kinetics and thermodynamics. Proper characterization of the feedstock’s kinetics and thermodynamic properties is essential for reactor design and the optimal utilization of pyrolysis feedstock [28]. Thermogravimetric analysis (TGA) is a beneficial technique for studying the decomposition behavior and chemical kinetics of materials during thermal decomposition. By combining TGA data with kinetic modeling, both thermodynamic and kinetic triplet parameters can be accurately estimated, providing critical insights into the thermal decomposition process [29,30]. TGA can be performed under isothermal or non-isothermal conditions, with non-isothermal conditions receiving more attention due to their ability to study material behavior over a range of temperatures while minimizing variations in physical and chemical properties. Additionally, TGA is characterized by its high reproducibility, reliability, sensitivity, and straightforward data acquisition [31,32,33]. Kinetic models used in TGA analysis are generally categorized into two types: model-free (or iso-conversional) methods and model-fitting methods. Model-free methods are particularly advantageous due to their precision and time-saving attributes [34]. Pyrolysis kinetics have been studied for various types of feedstocks, including freshwater algae Spirogyra [35], mustard stalk [36], watermelon seeds [30], cascabel, Delonix regia, and Manilkara zapota seeds [37], as well as coal [38].

In Egypt, Nile tilapia (Oreochromis niloticus) is one of the most consumed freshwater fish species, accounting for approximately 65.15% of fish production [39]. Its rapid farming cycle and low production costs make it the primary species for aquaculture in the region. Fried tilapia is a staple meal in many Egyptian households, providing an affordable source of protein. However, the widespread consumption of tilapia generates significant amounts of waste, including viscera, heads, scales, skin, and bones, with approximately 60–70% of the fish body being discarded. These wastes, produced in large quantities by restaurants and hotels, pose serious environmental and health challenges. Unlike other fish waste, fried tilapia waste contains residual frying oils and additives used during the frying process, which can influence the pyrolysis process. Therefore, analyzing the thermochemical behavior of fried tilapia waste and understanding its pyrolysis potential is essential for its effective conversion into biofuels.

Compared to other types of waste, limited studies have focused on fish waste pyrolysis. Yusof et al. [40] employed TGA to investigate the kinetics of Sardinella fimbriata fish waste. Three model-free models were used to estimate the pre-exponential factors and activation energies. The mean activation energy and pre-exponential factor were 84–124 kJ/mol and between 10² and 10¹¹ s⁻¹, respectively. The kinetic and thermodynamic factors of the Sardinella fimbriata fish waste were determined. The findings of this study provide insights into the possibility of using pyrolysis to produce fish waste as a biofuel source. In another study, Reza et al. [41] analyzed the thermochemical properties and pyrolysis behavior of fish processing waste. Their results showed that fish waste has a higher heating value of 21.53 MJ/kg, highlighting its suitability for biofuel production. TGA and derivative thermogravimetry (DTG) analyses revealed a maximum weight loss rate of 4.57 wt.%/min at 352 °C. The pyrolysis was conducted at a heating rate of 25 °C/min at different pyrolysis temperatures. The percentage of bio-oil, biogas, and biochar was 29.34, 23.46, and 33.96% at 400 °C; 49.32, 33.87, and 47.72% at 500 °C; and 21.34, 42.37, and 18.32% at 600 °C. Abdelrahman et al. [42] studied the production of bio-oil and activated carbon (AC) from fish waste. De-oiled fish pyrolysis was carried out under varying particle sizes, residence times, and temperatures. The highest bio-oil yield of 57.13% was achieved at a particle diameter of 0.25 mm, a pyrolysis time of 60 min, and a temperature of 500 °C. The properties of the produced bio-oil closely resembled those of bio-oils reported in previous studies, demonstrating its comparability as a bioenergy source. Activated carbon was generated from biochar using steam activation. The optimal activated carbon sample was produced at an activation temperature of 500 °C, with a particle size of 60 mesh and an activation time of 60 min. In a related study, Takwa et al. [43] examined the fats derived from fish waste pyrolysis at a canned tuna factory. Their evaluation of bio-oil properties at 500 °C revealed the presence of numerous organic compounds, which hold potential as sustainable and valuable chemical resources. The bio-oil exhibited a high heating value of 9391 kcal/kg, along with viscosity and acidity values of 7 cst and 103 mg KOH/g, respectively. The study demonstrated that the generated bio-oil could potentially be used as engine fuel when blended with fossil fuels.

Although a lot of research studies concern the pyrolysis of food waste, few studies have been conducted on fried fish waste. This type of waste differs significantly from other food waste because of its high content of lipids and exposition to thermal processing. The recent studies on fish waste pyrolysis primarily concern raw fish waste, ignoring the impact of using frying oils and other additives on decomposition and kinetics. This study aims to fill this gap by studying the kinetic triplet and thermodynamic parameters of FTFW pyrolysis, presenting new insights into its potential as a biofuel source. The kinetic triplet parameters were calculated, including activation energy (E_a), pre-exponential factor (A), and reaction model (f(α)). The FTFW is a unique food waste generated in large quantities in Egypt, which poses significant environmental and health challenges due to improper disposal. FTFW has higher contents of lipid and protein compared to other protein-rich waste and lignocellulosic biomass, resulting in a distinct pyrolysis pathway with improved quality and yield of bio-oil. The exitance of frying oil and other additives affects thermal degradation behavior and potentially promotes the heating value of the generated bio-oil.

Triplet kinetic parameters are critical for understanding the thermal decomposition process and optimizing reactor design. Using TGA and four iso-conversional models, this study determined the kinetic triplet parameters and thermodynamic properties to provide a comprehensive understanding of the pyrolysis process of FTFW as a sustainable biomass resource. The four selected iso-conversional models, Friedman, Kissinger–Akahira–Sunose (KAS), Starink, and Flynn–Wall–Ozawa (FWO), and the heating rates of 10, 15, and 20 °C/min, as presented in

Table 1. Summary of previous kinetic triplet parameters for different biomass and waste food.

Feedstock	Methods Used	Heating Rate, β °C/min	Kinetic Triplt Parameters	Ref.
Azadirachta indica biomass	- TGA, DTG - Iso-conversional methods (FWO, KAS, Starink, Friedman, and DAEM) and Coats–Redfern	β = 10, 20, 30 and 40 °C/min	- The average active energy. - Pre-exponential factor (A).	[44]
Macroalgae Spirogyra crassa	- TGA, DTG - Iso-conversional methods (FWO, KAS, Starink, and Friedman) - Compensation effect, Master plot	- Temperature range from 25 to 800 °C. - β = 5, 10, and 20 °C/min	- The average active energy. - Pre-exponential factor (A). - The reaction mechanism f(α).	[35]
Bamboo residues and three main components (cellulose, hemicellulose, and lignin)	- TGA, DTG - Iso-conversional methods (FWO, KAS, and Friedman) - $\mathrm{Master}\mathrm{Plot}\mathrm{Analysis}{\displaystyle \frac{Z\left(\alpha \right)}{Z\left(0.5\right)}}$	- Temperature range: 25 to 850 °C. - β = 5, 10, 20 and 40 °C/min)	- The average active energy. - Pre-exponential factor (A). - The reaction mechanism f(α).	[45]
Waste tires with spent FCC catalyst, red mud, and fly ash	- TGA - Iso-conversional methods (FWO, KAS, Starink, and Friedman)	- Temperature range: 40 °C to 725 °C - β = 10, 20, and 30 °C/min	- The average active energy. - Pre-exponential factor (A). - The reaction mechanism f(α).	[46]
Macadamia nutshell waste (MNSW)	- TGA, Fraser-Suzuki kinetic model - Iso-conversional (FWO, KAS, Friedman, and Starink) - $\mathrm{Master}\mathrm{plots}{\displaystyle \frac{y\left(\alpha \right)}{y\left(0.5\right)}}$	β = 10, 15, 20 and 25 °C/min	- The average active energy. - Pre-exponential factor (A). - The reaction mechanism f(α).	[47]
Waste Food
Food waste (cereals, meat, vegetables, and mixed waste)	TGA, DTG, Friedman method	β = 5, 10, 15, and 20 °C/min	- The average active energy. - Pre-exponential factor (A). - The reaction mechanism f(α).	[48]
Raw Fish waste (Sardinella fimbriata)	Fixed-bed torrefaction reactor	-	-	[49]
Raw Fish processing waste (Neotrygon kuhlii)	TGA, DTG, FTIR, SEM/EDX, Fixed Bed Reactor	β = 25 °C/min	Non	[41]
Fish waste (Sardinella fimbriata)	- TGA - Iso-conversional methods (FWO, KAS, and Starink	β = 10, 20, and 30 °C/min	- The average active energy. - Pre-exponential factor (A). - The reaction mechanism f(α).	[40]
Waste food (pork and rice)	- TGA - Iso-conversional methods (FWO, KAS, and Friedman) - Coats–Redfern	β = 30, 60, and 90 °C/min	- The average active energy. - Pre-exponential factor (A).	[50]
Kitchen Food Waste and Rice Straw	- TGA - Iso-conversional methods (FWO, KAS, and Friedman) - Coats–Redfern	β = 10, 20, and 30 °C/min	- The average active energy. - Pre-exponential factor (A).	[51]
Food waste and waste solid digestate food	- TGA - FWO, Coats–Redfern method	β = 5, 10, 15, 20, and 25 °C/min	- The average active energy. - Pre-exponential factor (A).	[52]
Tea and coffee wastes	- TGA, DTG - Iso-conversional methods (FWO, KAS, Starink, and Vyazovkin, Friedman)	β = 10, 25, 50 and 100 °C/min	- The average active energy. - Pre-exponential factor (A).	[53]
Municipal solid waste and food waste	- TGA - Coats–Redfern method	- Temperature range 30–900°C. - β = 10, 20, 30 °C/min	- The average active energy. - Pre-exponential factor (A).	[54]
Fried tilapia fish waste (FTFW)	- TGA, DTG - Iso-conversional methods (Friedman, KAS, Starink, and FWO) - Compensation effect, Master plot - Fixed-bed Reactor	- Temperature range 25 to 700 °C. - β = 10, 15 and 15 °C/min	- The average active energy. - Pre-exponential factor (A). - The reaction mechanism f(α).	Current study

, were chosen due to their extensive applications in biomass and food waste kinetic analyses, ensuring accurate and reliable results. Employing multiple iso-conversional models minimizes the influence of reaction mechanisms and experimental conditions, offering deeper insights into the complex thermal behavior of lipid-rich wastes. The results demonstrate that FTFW is a sustainable feedstock for pyrolysis, yielding valuable bio-oil, gas, and char, and contributing to waste valorization and renewable energy applications. This work highlights the importance of kinetic modeling in optimizing pyrolysis processes for effective waste management and energy recovery.

2. Materials and Method

2.1. Sample Preparation

The selected sample utilized in this study was fried tilapia fish waste (FTFW), which consisted of fried oil, bones, fish heads, skin, and other additives introduced in the frying of fish. The absorbed frying oil during the cooking process may influence the pyrolysis reaction by affecting the pathways of FTFW thermal decomposition and increasing bio-oil yield due to the frying oil’s lipid-rich composition. Using the frying oil can result in the release of light volatiles at lower temperatures because of low molecular weight compounds and the evaporation of absorbed oil. Frying oil may lead to excessive reaction pathways during pyrolysis, like hydrocarbon chain reforming and cracking. These reactions can clearly influence the kinetics and degradation of fish waste. The sample was obtained from household residuals. Before the pyrolysis process, the samples were dried under sunlight at an ambient temperature of 30–38 °C for 10 days to remove moisture. Fish waste samples were crushed and sieved to achieve a uniform particle size of <1 mm, as shown in Figure 1.

2.2. Thermogravimetric Experiment

The TGA and DTG analyses were used to assess the FTFW decomposition behavior. A thermogravimetric analyzer (TGA Q50 V20.13 Build 39) was used to conduct both analyses. The TGA experiments were carried out under pyrolysis conditions, starting from an ambient temperature and increasing to a maximum temperature of 700 °C, at different heating rates. As shown in Table 1, a temperature of 700 °C or higher was selected to ensure the complete decomposition of FTFW, which can optimize the yields of pyrolysis products and is consistent with standard practices in the pyrolysis field. The selected heating rates are widely selected in TGA studies of pyrolysis of different feedstocks, which can ensure the variation of decomposition behavior under a range of conditions. Additionally, the selected heating rates are commonly used in both laboratory and pilot-scale pyrolysis systems, which elucidates the applicability of the obtained results to real-world applications. The decomposition of the samples was tested under a nitrogen carrier gas to create an inert atmosphere. To achieve reliability and repeatability of experiments, every test was repeated 3–5 times. For each experimental run, the sample weight was maintained at 10–30 mg ± 0.5 µg, with a weighing precision of ±0.01%. Platinum crucibles were utilized to achieve optimal heat transfer between the thermocouples and crucibles, with a temperature accuracy of ±1 °C.

3. Kinetic Analysis

The TGA and DTG test results were utilized to determine the kinetic triplet parameters. Additionally, these data were also employed to calculate thermodynamic parameters, including enthalpy change (ΔH), Gibbs free energy change (ΔG), and entropy change (ΔS). These parameters were derived by analyzing the mass loss of the sample as a function of temperature. The mechanism of FTFW pyrolysis involving numerous reactions together could be a single-step or an n-order step reaction; however, it generally follows the following reaction:

$$F_{Solid}\stackrel{K\left(T\right)}{\to }{C}_{char}+V_{volatile}$$

(1)

The reaction of solid-state at a certain heating rate for non-isothermal conditions is given by the equation of kinetics known as the Arrhenius equation:

$${\displaystyle \frac{d\alpha }{dt}}=K\left(T\right)f\left(\alpha \right)$$

(2)

Regarding Equation (2), K(T) is known as the temperature-dependent constant, f(α) is the reaction model function, which describes the decomposition mechanism, and α is the extent of conversion and represents the fractional change in the mass of a solid sample by a thermal decomposition process, defined as:

$$\alpha ={\displaystyle \frac{m_{inital}-m_t}{m_{inital}-m_{final}}}$$

(3)

where m_inital, m_t, and m_final are the initial sample mass, the mass at a specific time, and the mass after the heating process (char mass), respectively. Equation (2) includes the global reaction rate constant (K(T)), which is a temperature-dependent constant, defined as follows:

$$K\left(T\right)=A{e}^{{\displaystyle \frac{-E_a}{RT}}}$$

(4)

where A is a pre-exponential factor (min⁻¹), that represents the number of effective molecular collisions resulting in decomposition, Ea is the apparent activation energy, which is the energy required to begin the reaction and decomposition of material under pyrolysis conditions, T is the sample temperature, and R is the universal gas constant (8.314 J/K. mol) [55,56]. By substituting Equations (2) and (4), the Arrhenius equation can be expressed as follows:

$${\displaystyle \frac{d\alpha }{dt}}=A{e}^{{\displaystyle \frac{-E_a}{RT}}}f\left(\alpha \right)$$

(5)

According to Equation (5), the relationship between the time dependence on temperature is the constant heating rate (β) °C/min:

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

(6)

Combining Equations (5) and (6) yields:

$${\displaystyle \frac{d\alpha }{dT}}={\displaystyle \frac{A}{\beta }}{e}^{{\displaystyle \frac{-E_a}{RT}}}f\left(\alpha \right)$$

(7)

Rearranging Equation (7) gives:

$${\displaystyle \frac{d\alpha }{f\left(\alpha \right)}}={\displaystyle \frac{A}{\beta }}{e}^{{\displaystyle \frac{-E_a}{RT}}}dT$$

(8)

Equation (7) is known as the fundamental differential equation for evaluation of the kinetic triplet parameters (Ea, f(α), and A), which are obtained from TGA data. By integrating both of its sides to obtain g(α), the following equation results:

$$g\left(\alpha \right)={\int }_0^{\alpha }{\displaystyle \frac{d\alpha }{f\left(\alpha \right)}}={\displaystyle \frac{A}{\beta }}{\int }_{T_i}^{T_f}{e}^{{\displaystyle \frac{-E_a}{RT}}}dT={\displaystyle \frac{A{E}_a}{\beta R}}\pi \left(X\right)$$

(9)

where the integrated form of reaction model function f(α) is g(α), and the integral temperature function is π(X), where X = Eα⁄RT. π(X) is in integral form and does not have an exact solution, but it has several suggested approximations [44,57,58,59]. Also, T_i represents the initial temperature, and T_f refers to the final temperature, where the sample reaches 700 °C. The kinetic parameters, identified as the kinetic triplet parameters in Equation (8), were determined for the non-isothermal decomposition of wastes using both the model-fitting and model-free methods. The model-fitting method relies on the assumption of a specific reaction order. In contrast, the model-free method, also known as the iso-conversional method, can effectively describe the complex reaction mechanisms by analyzing the degree of conversion at various heating rates [60,61,62,63].

3.1. Determination of the Activation Energy

The kinetic triplet parameters describe the reaction rate of the pyrolysis of materials as a function of temperature at a certain conversion. This study employed four methods (Friedman, Starink, Kissinger–Akahira–Sunose, and Flynn–Wall–Ozawa) to determine the activation energy of FTFW. The selected methods are widely used to estimate the pyrolysis of different feedstocks, as listed in Table 1. These models do not require a predefined reaction model, making them appropriate for complex feedstock pyrolysis. They are applicable for activation energy estimation across various conversion levels, which gives deeper insight into the decomposition behavior of various feedstock fractions. The widespread use of these models in kinetic studies of different materials demonstrates their reliability in estimating kinetic parameters. The Friedman method [64] calculates Eₐ differentially based on α values for a single β. It is widely utilized as a differential method because no assumptions regarding the reaction mechanism are required [65]. The other three methods, Starink, KAS, and FWO, are integral methods and are extensively applied due to their accuracy and versatility across a wide range of conditions. A concise summary of the governing equations for each method is provided in

Table 2. Summary of different methods utilizing in the current study to obtain activation energy.

Method	Governing Equation	Plotting Method	Slope	Equation No.
Friedman [64]	$\ln \left({\displaystyle \raisebox{1ex}{$d\alpha $}\!\left/ \!\raisebox{-1ex}{$dt$}\right.}\right)=\mathit{ln}\left[Af\left(\alpha \right)\right]-{\displaystyle \raisebox{1ex}{$E_a$}\!\left/ \!\raisebox{-1ex}{$RT$}\right.}$	Plotting y-axis $\mathit{ln}\left({\displaystyle \raisebox{1ex}{$d\alpha $}\!\left/ \!\raisebox{-1ex}{$dt$}\right.}\right)$ against x-axis $1/T$	$(-{\displaystyle \raisebox{1ex}{$E_a$}\!\left/ \!\raisebox{-1ex}{$R$}\right.})$	(10)
KAS [66,67]	$\ln ({\displaystyle \raisebox{1ex}{$\beta $}\!\left/ \!\raisebox{-1ex}{$T^2$}\right.})=-{\displaystyle \raisebox{1ex}{$E_a$}\!\left/ \!\raisebox{-1ex}{$RT$}\right.}+\mathit{ln}{\displaystyle \frac{AR}{E_{a}g\left(\alpha \right)}}$	Plotting y-axis $\ln ({\displaystyle \raisebox{1ex}{$\beta $}\!\left/ \!\raisebox{-1ex}{$T^2$}\right.})$ against x-axis $1/T$	$(-{\displaystyle \raisebox{1ex}{$E_a$}\!\left/ \!\raisebox{-1ex}{$R$}\right.})$	(11)
Starink [68,69,70]	$\ln ({\displaystyle \raisebox{1ex}{$\beta $}\!\left/ \!\raisebox{-1ex}{$T^{1.92}$}\right.})=\mathit{ln}{\displaystyle \frac{A{R}^{0.92}}{{E_a}^{092}g\left(\alpha \right)}}-1.0008\left({\displaystyle \raisebox{1ex}{$E_a$}\!\left/ \!\raisebox{-1ex}{$RT$}\right.}\right)-0.312$	Plotting y-axis $\ln ({\displaystyle \raisebox{1ex}{$\beta $}\!\left/ \!\raisebox{-1ex}{$T^{1.92}$}\right.})$ against x-axis $1/T$	$(-1.0008{\displaystyle \raisebox{1ex}{$E_a$}\!\left/ \!\raisebox{-1ex}{$R$}\right.})$	(12)
FWO [71,72]	$\mathit{ln}\beta =\mathit{ln}{\displaystyle \frac{A{E}_a}{Rg\left(\alpha \right)}}-5.331-1.052\left({\displaystyle \raisebox{1ex}{$E_a$}\!\left/ \!\raisebox{-1ex}{$RT$}\right.}\right)$	Plotting y-axis $\mathit{ln}\beta$ against x-axis $1/T$	$(-1.052{\displaystyle \raisebox{1ex}{$E_a$}\!\left/ \!\raisebox{-1ex}{$R$}\right.})$	(13)

. The activation energy was determined from the slope of the y-axis plotted against the x-axis.

3.2. Determination of the Reaction Mechanism

Determining the reaction mechanism f(α) is essential for estimating the kinetic behavior of a reaction, classifying it as accelerated, decelerated, or sigma-based, based on the shape of the conversion–temperature (α–T) curves. Accurate reaction models for solid-state reactions are described by Criado et al. [73] using the Z(α) and y(α) master plot methods. The Šesták–Berggren (S*) method [74,75], shown in Equation (14), offers a versatile experimental approach for representing sigma interaction patterns using different sets of parameters (m, n, and P). The adaptability of the Šesták–Berggren (S*) model makes it a valuable framework for understanding and predicting the behavior of a wide range of thermal decomposition processes.

$$f\left(\alpha \right)=m{(1-\alpha )}^n{\left[-\ln (1-\alpha )\right]}^p$$

(14)

The autocatalytic reaction mode f(α) F* by Snegirev [76,77,78] describes the kinetics of reactions where the presence of its products accelerates the reaction rate. The general form of the autocatalytic model according to Snegirev is as follows:

$$f\left(\mathsf{\alpha }\right)={(\mathsf{\alpha }}^{\mathrm{m}}+{\mathsf{\alpha }}^{\ast }){\left(1-\mathsf{\alpha }\right)}^{\mathrm{n}}$$

(15)

where n and m are reaction order parameters, and α* is a parameter representing the autocatalytic effect. This model helps to describe complex reactions that involve autocatalytic behavior, in which the reaction rate initially increases as more products are formed, leading to a peak in the reaction rate before it starts to decline as the reactants are consumed. Z(α) is define as the following [75]:

$$Z\left(\alpha \right)=\left({\displaystyle \frac{d\alpha }{dt}}\right)T^2\left[{\displaystyle \frac{\pi \left(X\right)}{\beta T}}\right]=f\left(\alpha \right)\times g\left(\alpha \right)$$

(16)

For comparison between the experimental kinetic curves and the models in the graph curves in Table S1, both Z(α) and Z(0.5) were divided.

$${\displaystyle \frac{Z\left(\alpha \right)}{Z\left(0.5\right)}}={\displaystyle \frac{f\left(\alpha \right)\times g\left(\alpha \right)}{f\left(0.5\right)\times g\left(0.5\right)}}={\displaystyle \frac{{\left(\raisebox{1ex}{$d\alpha $}\!\left/ \!\raisebox{-1ex}{$dt$}\right.\right)}_{\alpha }}{{\left(\raisebox{1ex}{$d\alpha $}\!\left/ \!\raisebox{-1ex}{$dt$}\right.\right)}_{\alpha =0.5}}}\times {\left({\displaystyle \frac{T_{\alpha }}{T_{0.5}}}\right)}^2$$

(17)

The special function y(α) is defined as a function of α corresponding mostly used kinetic models, as listed in Table S1, which is illustrated in the master plots method of y(α):

$$y\left(\mathsf{\alpha }\right)={\left({\displaystyle \raisebox{1ex}{$d\alpha $}\!\left/ \!\raisebox{-1ex}{$dt$}\right.}\right)}_{\alpha }\exp \left(-E_a/RT(\alpha \right)=Af\left(\mathsf{\alpha }\right)$$

(18)

where T(α) and dα⁄dt are measured at different heating temperatures by comparing the experimental data with the normalized function in Equation (19) [75,79]

$$\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d}{\displaystyle \frac{y\left(\mathsf{\alpha }\right)}{y\left(0.5\right)}}={\displaystyle \frac{{\left(\raisebox{1ex}{$d\alpha $}\!\left/ \!\raisebox{-1ex}{$dt$}\right.\right)}_{\alpha }}{{\left(\raisebox{1ex}{$d\alpha $}\!\left/ \!\raisebox{-1ex}{$dt$}\right.\right)}_{\alpha =0.5}}}EXP\left[{\displaystyle \frac{E_a}{R}}\left({\displaystyle \frac{1}{T_{\alpha }}}-{\displaystyle \frac{1}{T_{0.5}}}\right)\right]$$

(19)

3.3. Pre-Exponential Factor Estimation

The pre-exponential factor represents the frequency of successful molecular collisions leading to decomposition, reflecting the reaction rate regardless of temperature. A higher A value indicates a higher tendency for bond scission [80]. There is a reliance between a pre-exponential factor and activation energy, and there is a compensating manner in which they could correlate together. This compensation effect is utilized to distinguish the reliance of Ea and A on the conversion degree [81], where a linear correlation is considered depending on the following relationship:

$$\ln A=a{E}_a+b$$

(20)

where a and b are the compensation factors for the kinetic process at a given heating rate.

3.4. Thermodynamic Parameters

After calculating the activation energy and pre-exponential factor using different models, the thermodynamic properties, including ∆H, ∆G, and ∆S, were calculated via Equations (21), (22), and (23), respectively. These calculations provide a comprehensive understanding of the reaction kinetics and thermodynamics and provide information on the energy demands and stability of the reaction [36,40,82].

$$∆H=E_a-RT$$

(21)

$$∆G=E_a+R{T}_{peak}\ln \left(B_{Bol}{\displaystyle \frac{{\mathrm{T}}_{peak}}{P_{plank}A}}\right)$$

(22)

$$∆S={\displaystyle \frac{∆H-∆G}{T_{Peak}}}$$

(23)

where B_Bol is the Boltzmann constant (1.3819 × 10⁻²³ J/K), P_plank is the Plank constant (6.6269 × 10⁻³⁴ J s), and T_peak is the maximum temperature calculated from derivative mass losses from DTG.

4. Results and Discussions

4.1. TGA and DTG Results Discussions

The TGA and DTG plots for FTFW pyrolysis at various heating rates at a temperature range of 40–700 °C are presented in Figure 2a,b. According to Figure 2a, the decomposition of fish waste has three stages of reaction, the characterization of which is presented in

Table 3. TGA/DTG measurements of FTFW at different heating rates.

Heating Rate β °C/min	Peak	Peak Temperature °C	Peak Mass Loss %	DTG %/min
10	1st peak	143.9	96.1	0.6615
	2nd peak	387.3	54.68	3.912
15	1st peak	147.5	96.72	1.21485
	2nd peak	398.92	51.27	6.267
20	1st peak	183.1	96.29	1.1356
	2nd peak	407.57	62.22	7.658

. The first stage occurs between 40 °C and around 205 °C, and the weight loss is around 8%. The lower percentage of moisture released is due to the drying process prior to the TGA tests. This stage takes place under 205 °C and is attributed to the release of light volatiles due to the oil absorbed during the frying process. At this stage, the peak derivative mass loss occurs at approximately 147 °C, signifying the release of moisture and some light volatiles. The second stage is the most significant phase in waste decomposition, beginning immediately after the first stage and continuing up to about 540 °C across all three heating rates. The peak temperature during this stage falls within the range of 382–407 °C, corresponding to the decomposition of carbohydrates, proteins, and fats with the most stable structures. The broad width of this stage and the appearance of a secondary peak, lower than the primary peak, within the 205–540°C range, results from the overlapping decomposition of carbohydrates and proteins, as noted in previous studies [40,83,84]. The effect of heating rates on the decomposition of raw materials is significant, as higher heating rates lead to increased volatility, which, in turn, influences residence time and weight loss. Specifically, there is an inverse relationship between heating rates and residence time: as the heating rate increases, the residence time decreases. Consequently, the peak temperature shifts to a higher value during the second stage of decomposition. The third stage represents the conclusion of the pyrolysis process, where the continued formation of char results in a slight weight loss. Figure 3 depicts the relationship between temperature and the extent of conversion at various heating rates. The figure shows that the extent of conversion consistently increases with rising temperatures across all heating rates.

4.2. Kinetic Triplet Parameters Calculations

4.2.1. Activation Energy (Ea)

In the present study, four iso-conversion methods were employed: the Friedman, FWO, Starink, and KAS methods. Figure 4a–d displays the iso-conversion plots for each method separately, showcasing the behavior of FTFW at various heating rates with a conversion degree (α) ranging from 0.1 to 0.9 in steps of 0.05. The plots demonstrate an excellent linear relationship, with correlation coefficients R² exceeding 0.85, as depicted in Figure 5. By calculating the appropriate slope for each α value across the four methods, as outlined in Table 2, the activation energy values were determined from the slope of the linear regression line at each specific conversion level for the different heating rates.

Figure 6 illustrates the variation of activation energy with conversion degree for the different models. The Friedmann method revealed significant fluctuations in Ea, ranging from 43.2 to 208.2 kJ/mol, which could be attributed to the complex multi-step reactions, such as parallel and continuous reactions, occurring during thermal decomposition. The sensitivity of the Friedmann method, which relies on the differential derivatives of experimental mass-change or conversion data as functions of time or temperature, allows it to detect subtle changes in activation energy along the reaction path. However, this high sensitivity also makes it susceptible to noise and random fluctuations, resulting in challenges in distinguishing true changes in Ea from random noise. The FWO method showed an increase in Ea from 31.3 to 148.3 kJ/mol, with an average of 109.7 kJ/mol. For the KAS method, Ea increased from 22.3 to 179.3 kJ/mol, with an average value of 96.6 kJ/mol. For the Starink method, Ea increased from 24.1 to 181.3 kJ/mol, with an average value of 100.5 kJ/mol.

Regarding the conversion values α, the thermal decomposition of FTFW as modeled by KAS, FWO, and Starink is challenging to describe as a single-stage reaction. At the onset of decomposition (α = 0.1), minimal heat is required to remove moisture, some fried oil, and light volatile substances from the fish waste samples. Following this, Ea increases rapidly, peaking at α = 0.3, consistent with the initiation of the thermal decomposition of the main components in the fried fish waste samples, as shown in Figure 4. A stabilization phase follows during α = 0.3−0.65, after which Ea steadily increases until α = 0.9, corresponding to the decomposition and char formation processes.

Overall, the methods used in this study demonstrated a significant reduction in activation energy, underscoring the economic importance of this type of waste as it requires minimal energy input for pyrolysis. While the activation energy values obtained using the four methods were generally consistent, slight differences were observed. The Ea values determined by the Friedmann method were slightly higher than those obtained using KAS, FWO, and Starink. This discrepancy arises from the numerous approximations and assumptions inherent to the latter methods, whereas the Friedmann method, as a differential approach, does not rely on such assumptions and approximations.

The activated energy values determined using the different integral methods were compared with those of other feedstocks reported in the literature, as shown in

Table 4. Comparison of activation energy of the present study with other feedstocks.

Feedstock	Method	Average Ea Range (kJ/mol)	Ref.
Fried fish tilapia waste	KAS, Starink, FWO	(96.7–109.7)	Present study
Sardinella fimbriata fish waste	KAS, Starink, FWO	(83.75–123.75)	[40]
Petroleum sludge	KAS, Starink, FWO	41.31–133.89	[85]
Doum shell	KAS, Starink, FWO	118.15–142.81	[86]

. The results showed that the activation energy of FTFW was lower than that of Sardinella fimbriata fish waste. This can be attributed to the differences in the fish type and frying process of FTFW, which can enhance and accelerate the decomposition of fish waste. The calculated value of the activation energy was close to that of the other feedstocks, indicating competition for pyrolysis. Furthermore, the lower value of activation energy demonstrates the ability to use FTFW as a promising pyrolysis feedstock, as it requires low input heat for reaction initiation.

The change in activation energy calculated in this study is attributed to the heterogeneous composition of FTFW. The existence of lipid-rich residues from frying oil promotes a decrease in the activation energy at lower conversion stages (α < 0.3) because lipids are highly volatile and decompose more easily than minerals and proteins. In contrast, protein-rich fractions, such as fish bones and connective tissues, need higher activation energy for decomposition, which leads to fluctuations of activation energy at higher conversion extents. This observation agrees with previous studies related to the pyrolysis of raw fish waste, where higher ash content and protein resulted in increasing the activation energy. As indicated in Table 4, the average values of Ea for FTFW (96.7–109.7 kJ/mol) are lower than those for Sardinella fimbriata fish waste (83.75–123.75 kJ/mol). This difference demonstrates the effect of frying oil in promoting the decomposition kinetics and lowering the energy requirement. The presence of frying oil residual in FTFW distinguishes it from other raw fish wastes, likely improving bio-oil yield and declining the required energy for thermal degradation. In addition, other additives used during the frying process, like salt and flour, can affect the thermal decomposition behavior of FTFW, as these additives can introduce additional pathways of the reaction or catalyze certain decomposition steps, resulting in E_a fluctuations.

4.2.2. Reaction Mechanism F(α)

Figure 7 and Figure 8 provide a detailed comparison of the main plots of Z(α)/Z(0.5) and y(α)/y(0.5) between experimental measurements at a heating rate of 10 °C/min and several ideal reaction models presented in Table S1. The comparison was based on shape similarity rather than the exact accuracy of the fitting because these curves exhibited uniform properties.

The y(α)/y(0.5) comparison facilitates evaluating experimental approaches that are challenging to directly compare with Z(α)/Z(0.5). By normalizing the transformation curves at the midpoint α = 0.5, differences in reaction mechanisms and corresponding model fits become more distinguishable. Figure 7 shows that the bell-shaped symmetry of the experimental curve up to a fractional transformation of 0.7 suggests the relevance of the second-order F2 and D2 diffusion models. This finding implies that reaction kinetics are significantly influenced by the rapid decomposition processes in the initial stage, particularly for α > 0.1 at temperatures below 193 °C. This stage is critical, as it determines the activation energy Ea.

Experimental data were further analyzed using the Šesták–Berggren model (Equation (14)), and the autocatalytic reaction model proposed by Snegirev (Equation (15)). The Šesták–Berggren model, with its flexible combination of parameters (m, n, and p), effectively represents various reaction mechanisms and is particularly useful for capturing complex sigmoidal reaction patterns. In contrast, the Snegirev model targets autocatalytic reactions, where n and m define the reaction order, and α* represents the autocatalytic effect. These models were systematically compared with the experimental y(α) plot to identify the best-fitting reaction mechanisms. Notably, the experimental analysis of fish waste does not match any predefined models because the pyrolysis process involves complex conversion chemistry. Owing to its complex nature, it is difficult to relate FTFW pyrolysis to a single-conversion model.

The analysis revealed that for α < 0.5, the experimental data closely aligned with the Šesták–Berggren model [74], reflecting the complexity and multistep nature of the initial reaction stages. For α > 0.5, the autocatalytic reaction model by Snegirev [76] provided the strongest agreement, likely due to the impact of reaction products on the reaction rate during later stages.

Table 5. Analysis of the most suitable model validity from the results of the y(α) master plot.

α	$\mathit{f}\left(\mathit{\alpha }\right)$ Model	Coefficients	Stage
0.1–0.5	$f\left(\alpha \right)=m{(1-\alpha )}^n{\left[-\ln (1-\alpha )\right]}^p$	m = 1, n = 2.1, P = 2	First stage
0.5–0.9	$f\left(\alpha \right)=({\alpha }^m+{\alpha }^{\ast }){(1-\alpha )}^n$	n = 1.23 m = 1.4 ${\alpha }^{\ast }$ = 0.05	Second stage

summarizes the comprehensive comparison and detailed parameter fitting, highlighting how each model aligns with different reaction stages. This multifaceted analysis emphasizes the importance of using a suite of models to accurately describe the kinetics of complex thermal decomposition processes.

In conclusion, the Snegirev autocatalytic and Šesták–Berggren models offered the best fits for the experiment. The Šesták–Berggren model is efficient for describing the initial stages of decomposition (α < 0.5) due to its flexible combination of parameters (m, n, and p), which allow the model to describe a wide range of reaction mechanisms, such as sigmoidal patterns that characterize the complex reactions involving multiple steps. On the other hand, the Snegirev autocatalytic model considers an autocatalytic effect, represented by the parameter α*, which is responsible for the acceleration of the reaction as more products are formed.

4.2.3. Pre-Exponential Factor

The pre-exponential factor (A) physically represents the molecules’ vibration frequency found in the reaction. Figure 9 and

Table 6. The variation and linear fitting equations of Ea and ln(A) for FTFW obtained by Friedman, KAS, Starink, and FWO methods.

α	Friedman		KAS		Starink		FWO
	Ea [kJ/mol]	A [min−1]	Eα [kJ/mol]	A [min−1]	Ea [kJ/mol]	A [min−1]	Ea [kJ/mol]	A [min−1]
0.1	43.23	1.29 × 10+04	21.95	6.09 × 10+01	24.06	1.04 × 10+02	31.61	6.93 × 10+02
0.15	105.83	6.40 × 10+09	46.82	1.03 × 10+04	50.95	2.63 × 10+04	58.72	1.52 × 10+05
0.2	92.06	1.38 × 10+08	71.16	1.55 × 10+6	77.26	5.75 × 10+06	84.74	2.87 × 10+07
0.25	106.63	1.62 × 10+09	88.39	3.6 × 10+07	95.88	1.74 × 10+08	103.14	7.85 × 10+08
0.3	109.93	1.99 × 10+09	95.43	1.06 × 10+08	103.49	5.41 × 10+08	110.86	2.40 × 10+09
0.35	94.06	5.89 × 10+07	95.36	7.62 × 10+7	101.61	3.77 × 10+08	110.95	1.67 × 10+09
0.4	108.96	8.04 × 10+08	94.78	5.09 × 10+07	97.09	7.99 × 10+07	110.53	1.09 × 10+09
0.45	83.18	3.96 × 10+06	94.35	3.35 × 10+7	95.53	4.20 × 10+07	110.27	7.05 × 10+08
0.5	87.07	6.54 × 10+06	94.82	2.80 × 10+07	96.01	3.51 × 10+07	110.95	5.83 × 10+08
0.55	105.54	1.61 × 10+08	96.71	3.13 × 10+07	99.08	4.86 × 10+07	113.12	6.54 × 10+08
0.6	124.51	4.05 × 10+09	99.77	4.43 × 10+07	102.22	6.93 × 10+07	116.49	9.38 × 10+08
0.65	141.60	6.91 × 10+10	103.64	7.46 × 10+07	106.16	1.17 × 10+08	120.71	1.61 × 10+09
0.7	119.62	1.06 × 10+09	107.80	1.29 × 10+08	110.41	2.06 × 10+08	125.24	2.87 × 10+09
0.75	162.26	1.44 × 10+12	110.69	1.70 × 10+08	113.37	2.73 × 10+08	128.45	3.83 × 10+09
0.8	129.63	3.05 × 10+09	114.60	2.28 × 10+08	120.82	6.66 × 10+08	132.76	5.23 × 10+09
0.85	190.41	3.87 × 10+13	128.80	1.183 × 10+09	131.86	1.98 × 10+09	148.00	3.02 × 10+10
0.9	208.20	2.63 × 10+14	179.30	2.380 × 10+12	181.23	3.26 × 10+12	148.00	1.46 × 10+10

illustrate the linear relationship between Ea and ln A, for the decomposition of FTFW using four methods. The data show a clear linear trend with high accuracy of fit, confirming the compensation effect. As temperature increases, the molecular vibrations intensify, leading to greater bending and stretching of molecules. This affects the compensation effect found in the data points, particularly from medium to high conversion values, as indicated by an excellent fit factor in Table 6. The variability in pre-exponential factor values obtained using the Friedman method ranging from 1.29 × 10⁴ to 2.64 × 10¹⁴, with an average of 1.78 × 10¹³ min⁻¹, reflects the activation energy fluctuations during conversion, attributed to noise in the results and the complexity of FTFW decomposition. The KAS method showed pre-exponential factor values ranging from 6.09 × 10² to 2.38 × 10¹³, with an average of 1.4 × 10¹¹ min⁻¹. The FWO method’s pre-exponential factor values ranged from 6.93 × 10² to 3.02 × 10¹⁰, with an average of 3.95 × 10⁹ min⁻¹, while the Starink method had an average pre-exponential factor value of 1.92 × 10⁹ min⁻¹. A low value of pre-exponential factor (lower than 10⁹) indicates closed complex formation, whereas a higher value suggests the formation of a simpler complex during the thermal decomposition process. The consistent linear relationship between Ea and ln A across the different methods underscores the validity of the compensation effect in analyzing the FTFW decomposition. The change in pre-exponential factor values ranging from 10⁹ to 10¹³ min⁻¹ across different models indicates the complex multi-step nature of FTFW pyrolysis, affected by the thermal breakdown of proteins, carbohydrates, and fats. This illustrates the need to optimize the heating rates in reactor design for maximum bio-oil production.

4.3. Thermodynamic Analysis Results

The thermodynamic properties are critical for the design, expansion, and optimization of pyrolysis reactors and their operational parameters. These properties provide valuable insights into the energy dynamics and spontaneity of the reactions within the reactor, helping assess the feasibility, nature, and efficiency of the pyrolysis process. Accurate thermodynamic data enable the prediction of system behavior under varying conditions, ensuring that the reactor is designed and operated to maximize yield and energy efficiency [87]. The thermodynamic evaluations in this study relied on kinetic data derived from activation energy estimates obtained using four methods: Friedman, KAS, FWO, and Starink.

One of the primary thermodynamic properties analyzed was the enthalpy change ΔH, which reflects the energy required for the thermal decomposition of FTFW. It represents the energy difference between the initial raw material and its conversion into various bioproducts, as illustrated in Figure 10a. This property is fundamental for understanding the energy demands of the decomposition process. The enthalpy change depends on activation energy, as lower enthalpy changes facilitate the formation of an activated complex [88,89]. The calculated ΔH range for FTFW thermal decomposition varied by method: 39.56–202.32 kJ/mol for the Friedman method, 18.03–173.46 kJ/mol for the KAS model, 20.9–175.35 kJ/mol for the Starink model, and 27.94–142.59 kJ/mol for the FWO model.

The second parameter is ∆G, a fundamental parameter in thermodynamics that determines the amount of energy available in FTFW for chemical transformation and generation of the activated compound. Gibbs free energy accounts for changes in enthalpy and entropy. The ∆G was estimated at a heating rate of 10 °C/min, and the results are presented in Figure 10b and Table 6. The average values of ∆G using the Friedman, KAS, Starink, and FWO methods were 192.71, 171.04, 183.99, and 174.83 kJ/mol, respectively. In comparison, most of the Gibbs free energy values calculated by the four methods are high, indicating the presence of sufficient useful energy to make them sources of biofuel production compared to different types of solid biofuels. The ∆G always showed positive values throughout the process, indicating that the FTFW pyrolysis is a non-spontaneous reaction, which means that it needs an external thermal energy source. It can be seen from Figure 10a,b that both ∆G and ∆H depend on the activation energy results, raising at the start of the process and then staying almost constant within the conversion extent, ranging from 0.2 to 0.75 and rapidly increasing towards the end. The char produced during the reaction may result in a decrease in mass transfer in the final stages of the reaction, which can decrease the process feasibility, as can be seen in the reference [56]. Notably, the effect of fluctuations in the activation energy resulting from the Friedmann method dramatically affects the results of both ∆G and ∆H. As the activation energy is the largest in the Friedmann method, higher values of ∆G and ∆H were also introduced. The current results are compared with previous results in

Table 7. Average values of thermodynamic parameters computed by the current study and those from literature.

Feedstock	Method	∆H (kJ/mol)	∆G (kJ/mol)	∆S (J/mol·K)	Ref.
FTFW	Friedman	113.45	192.71	−130	Current study
	KAS	91.78	171.04	−130
	Starink	95.58	174.83	−126
	FWO	104.73	183.99	−126
Sardinella fimbriata fish wastes	FWO	109.53	185.5	−150	[40]
	KAS	95.38	185.5
Pine needles and styrofoam	FWO	104.24	131.24	−0.04	[89]
Pistachio shell	KAS	160.87	182.15	−33.99	[56]
	Friedman	185.16	181.42	3.83
Butia seed waste	KAS	133.98			[57]
Pea factory waste (PW)	Mean value	207.62	174.5		[90]
Rice husk	KAS	214.60	175.5	63.1	[91]
Pine wood	Mean value	198.39			[92]
Canola residue	Mean value	131.7	154.7		[93]

.

The variation in entropy, as shown in Figure 10c, illustrates the disorder occurring in the system during the thermal decomposition of the FTFW. The negative values of ΔS demonstrated that the product’s disorder degree was lower than that of the reactant. This shows that the results of the decomposition of processes, such as bio-oil, char, and gas, are more organized than those of fish waste samples.

5. Error Analysis

The application of iso-conversional methods to determine the triplet kinetic and thermodynamic parameters may contain errors. The sources of error involving the selected models include the following:

▪

Assumption of a single reaction mechanism while the FTFW pyrolysis consists of multiple overlapping reactions (degradation of lipids, proteins, and residual carbohydrates).

▪

Uncertainties in TGA experiments, including heating rate changes, mass loss fluctuations, and instrumental sensitivity.

▪

The linear regression model’s limitation, which supposes an ideal reaction pathway without considering the impact of heat and mass transfer.

6. Economic or Feasibility Study

There are various key parameters, such as availability, input energy, feedstock characterizations, and waste valorization, that affect the economic feasibility of the pyrolysis system. As a consequence of the high consumption of Nile tilapia in Egypt, there is abundant fish waste, which ensures the FTFW availability. The high heating value (about 24–28 MJ/kg) of FTFW, along with high lipid and energy contents, makes FTFW more favoravle than biomass-based biofuels (18–25 MJ/kg) and comparable to traditional fuels like diesel (about 42–45 MJ/kg) [41]. The high heating value of FTFW signifies its capability to generate large amounts of high-energy bio-oil, which can be subsequently used in various applications as efficient liquid fuels. On the other side, FTFW had lower activation energy (100–120 kJ/mol) compared to other biomass materials, giving it a significant advantage. This implies that the FTFW pyrolysis requires low energy for its decomposition, potentially offering low initial energy costs and making it economically viable. By converting FTFW into biofuel, the costs of waste management can be minimized, solving both economic and environmental problems. The recovery of valuable by-products from pyrolysis, such as biochar, which can be used for soil amendment as well as syngas, which is used for industrial heating, promotes the overall economic viability of the FTFW pyrolysis process. An economically viable return on using FTFW as biofuel can be achieved depending on the costs of processing, transportation, and waste gathering [26]. Compared to traditional biofuel feedstocks, FTFW provides an economically viable option, such as vegetable oils or algae, because it is primarily produced in restaurants and households. Pyrolysis of food waste has been scientifically proven to be more effective for waste management than disposing of it in landfills, reducing the environmental impact. The industrial viability of pyrolysis of fish waste depends on a reliable supply chain, waste collection, and pretreatment, such as drying to optimize the pyrolysis of high-fat FTFW to produce a rich bio-oil, but this may require refining. Integrating the process with waste-to-energy systems or decentralized units in the fishery industry aims to improve economic viability and enhance the recovery of products such as biochar and biogas.

7. Conclusions

Fish waste, like other wastes, can serve as an energy source through the pyrolysis process. The evaluation of kinetic triplet parameters achieves optimal mass reduction and provides valuable insights into the pyrolysis process. Fried tilapia is a staple meal in Egypt, leading to significant waste generation from restaurants, hotels, and households. This waste cannot be recycled and is discarded as garbage, causing health and environmental problems. This study presents a detailed kinetic analysis of fried tilapia fish waste (FTFW) pyrolysis. The decomposition of FTFW was examined using TGA and DTG over a temperature range of 35–700 °C. The kinetic triplet parameters, including activation energy (Ea), reaction mechanism (f(α)), and pre-exponential factor (A), along with thermodynamic parameters such as enthalpy change (∆H), Gibbs free energy change (∆G), and entropy change (∆S), were calculated. Four iso-conversional methods, Friedman, KAS, Starink, and FWO, were employed for the kinetic analysis. The significant outcomes of this study are highlighted as follows.

The decomposition of FTFW occurred within a temperature range of 205–540 °C.

▪

The average activation energy values calculated by the Friedman, KAS, Starink, and FWO methods were 118.4, 96.7, 109.7, and 100.5 kJ/mol, respectively.

▪

The low activation energy of FTFW, compared to other feedstocks, highlights its potential as a promising candidate for the pyrolysis process.

▪

The pre-exponential factor values obtained by the Friedman, KAS, Starink, and FWO methods were 1.78 × 10¹³, 1.4 × 10¹¹, 3.95 × 10⁹, and 1.92 × 10⁹ min⁻¹, respectively.

▪

The average enthalpy changes estimated using the Friedman, FWO, KAS, and Starink methods were 113.45, 91.78, 95.58, and 104.73 kJ/mol, respectively.

▪

The average Gibbs free energy values for the Friedman, KAS, Starink, and FWO methods were 192.71, 171.04, 183.99, and 174.83 kJ/mol, respectively.

▪

The results of this study offer essential information needed in the efficient design of pyrolysis reactors, indicating the need for optimizing pyrolysis conditions. The results guide the design of efficient pyrolysis reactors, which are useful for waste-to-energy applications, and thus will enhance sustainable biofuel production and improve the valorization strategies for waste.

Other research areas should be considered, including experimental pyrolysis, bio-oil and biochar characterization, and techno-economic aspects for large-scale feasibility, investigating the catalytic and co-pyrolysis techniques to improve bio-oil productivity, quality, and stability. The impact of other frying conditions, such as oil type, frying temperature, and duration, on thermal degradation should also be considered.