The TGA is an essential device for a precise measurement of the mass loss of a substance with changing numerous temperature values. The kinetic analysis was performed by the relative mass loss data at altering temperature values with respect to time.

The pyrolysis tests of WWOS samples were carried out in a single-mode autoclave pyrolyzer unit at different temperatures (350°C, 450°C, and 550°C) by keeping all other conditions (pressure and amount of sample) constant for each run. The autoclave pyrolyzer unit consisted of a 360 ml vessel made up of stainless steel (SS) material with 1 dm³ capacity. An amount of 150 g of wet, oily sludge sample was kept in the vessel (20.32 cm length × 10.16 cm I.D), which was airtight. The experimental setup in terms of a schematic diagram is shown in Figure 1.

The system was heated at 10°C/min to the required temperature and sustained for 0.5 h to provide sufficient time for thermal degradation. Then, the release of pyrolysis products from the autoclave pyrolyzer unit was manually stopped. The vapor produced in the vessel evolved from the top of the vessel and was directed into the condenser, where the liquid products were condensed and collected. The non-condensable gases were usually expelled either into the atmosphere or collected in gas-sampling bags for analysis and characterization. The pyrolyzed solid product was collected from the bottom of the autoclave vessel after completing each experimental run. The yield of solid char and pyrolysis liquid was determined in each experiment by weighing products and the sample used in each experimental run whereas the yield of gases by the difference. The yield of the product is obtained by using Eq. 11. Yield   ( % ) = amount   of   product amount   of   sample × 100. (11)

A multilayer perceptron (MLP) based feed-forward back propagation neural network (FFBPN) was employed in this study to simulate the mass loss of WWOS obtained from the pyrolysis experiments conducted in TGA by maintaining an inert atmosphere. The variation in mass and conversion rates at different temperatures and under different heating rates has been modeled using an ANN. The ANN has been developed for a long time (Klemeš and Ponton, 1992) with many variations (Ponton and Klemeš, 1993; Shahbaz et al., 2020; Rashid et al., 2021). The FFBPN model has been widely used in the literature owing to its good pattern classifier and overall efficiency capability (Hafeez et al., 2020; Arshad et al., 2021). The proposed feed-forward network is a supervised learning technique comprised of three-layered architecture for data prediction, that is, the input, the hidden, and the output layers [(Naqvi et al., 2019a; Naqvi et al., 2018b) Naqvi, 2019 #65]. Each layer is connected through weights and biases. The nonlinear function can be adjusted using the weights and biases in the hidden layer.

For the development of the feed-forward neural network (FFNN), the input layer was fed with heating rate and temperature. The output layer was fed with weight loss (%). The data has been normalized in the range of [0–1] to improve network performance. 70% of the data was used to train the network, while 30% of the remaining data was divided equally in the validation and testing of the network. The number of neurons in the hidden layer has been adjusted iteratively to achieve the minimum mean square error (MSE) and maximum R ² values (Naqvi et al., 2020). For the training of the network, Levenberg–Marquardt (LM) function has been used. Back propagation (BP) is widely used in neural network modeling. It is generally known as a supervised learning technique, which is used for nonlinear mapping. Therefore, it has been used in this study. Table 1 shows the various parameters used for the training of the feed-forward BP network to predict the mass loss (%) of wet oily sludge.

The mass loss (%) of WWOS at various heating rates was simulated using 10 different models for the heating rate of 5°C/min by changing the number of neurons in the hidden layers. This study has also been conducted with two other heating rates (20 and 40°C/min). Table 2 shows the network performance results based on MSE and R ² for the mass loss (%) prediction at the heating rate of 5 °C/min for different topologies. It can be witnessed that the values of MSE and R ² for training, validation, and testing be varied by changing the number of neurons in the hidden layer from 1 to 10. The minimum value of MSE and the maximum value R ² were found to be [6.22 e⁻⁰⁵ 5.34 e⁻⁰⁵ 1.20 e⁻⁰⁴] and [9.99 e⁻⁰¹ 9.99 e⁻⁰¹ 9.99 e⁻⁰¹] for training, validation, and testing, at 9 neurons in the hidden layer and 9 neurons was selected for the hidden layer in the network. The selected network architecture was found to be [2 × 9 × 1].

The freshly received WWOS had 34.27% of moisture content. The sludge’s oil content was 24%, with a volatile matter of 40.73% and HHV of 15.65 MJ/kg. The amounts of carbon, hydrogen, and oxygen were 35.78, 3.10, and 59.58%. The oily sludge’s physicochemical composition makes it suitable for thermochemical conversion with 14.5% of ash, reducing the requirement of the catalyst during the conversion process.

The thermograms (TG and DTG curves) of WWOS under an inert atmosphere are illustrated in Supplementary Figure S1. It shows the variation in mass and conversion rates at different temperatures and under different heating rates. The mass loss started at a reasonably low temperature owing to the vaporization and evaporation of low boiling volatiles and water. A slight shift in the peaks toward high temperatures can be identified with a significant increase in the degradation rate of WWOS at a higher heating rate. The shift of the peak toward higher temperature indicates the delay in the escaping of the gases from the material structure. The thermal characteristics of WWOS at different heating rates were obtained from (DTG) the curves in Supplementary Figures S2(A–C) at 5, 10, and 20°C/min. The obtained values are reported in Supplementary Table S1 and plotted in Supplementary Figure S2D. It is evident that T i , T p , and ( d α d t ) p increased with the increasing heating rates. T f increased significantly when the heating rate was increased from 5 to 20 °C/min. This value was slightly decreased indicating a delay in completing the mass loss.

The Friedman, KAS, and OFW methods rely on linear curve fittings for a specific conversion (iso-conversion) at various heating rates. E a of WWOS at different extents of conversion can be estimated from the linear fitted line as shown in Figures 2A–C.

Table 3 summarizes the evolution of E a with the extent of conversion, while Figure 3 shows it in a graphical form. The R 2 values of 0.95 and above are an indicator for the effective estimation of E a for the conversion between 0.2 and 0.6. The E a from different methods coincide well until 50%, which is also indicated by the values of R 2 close to 1 and a lower standard deviation in Figure 3. The E a values were increased from 119.35 kJ/mol to 364.41 kJ/mol when estimated using the Friedman method. The increase in the activation energy shows that a multi-step complex process is involved in converting the oil sludge under pyrolytic conditions. The variation in E a indicates the variation in the composition and complexity of the feedstock. A similar trend in the activation energy during conversion has been reported in a recent study (Ma et al., 2019). Lower E a values at initial stages can be related to the physical devolatilization of water and lower molecular weight hydrocarbons. The increase in the E a values at higher conversion (Serio et al., 1987) or temperature can be attributed to the chemical transformation such as cracking (Siddiqui, 2010). The A values were increased from 1.67E07 s⁻¹ to 1.04E09 s⁻¹, when the conversion increased from 0.2 to 0.6. Table 3

A summary of the kinetic studies performed on the oily sludge is given in Supplementary Table S2. The average activation energy of WWOS in this study was higher than the earlier reported values in the literature. Ma et al. (2019) reported a similar trend in the activation energy. The authors attributed the increase in the activation energy at a greater extent of conversion to the complex thermal conversion and variability in the composition, which is normally associated with WWOS obtained from oil refineries.

The constants E a   and A obtained from the kinetic analysis were used to evaluate the thermodynamic characteristics of the conversion process. A summary of the thermodynamic parameters is provided in Table 4. Enthalpy, n H , of the process, remained positive, indicating the endothermic nature of the reaction. A difference of 49.5 kJ/mol between E a   and n H indicates some potential of forming activated complexes during the conversion. Positive ΔG and negative ΔS indicate that the reaction remains reactant-favored and non-spontaneous at all temperatures (Naqvi et al., 2018c; Naqvi et al., 2020).

The yield of liquid, solid, and gaseous products obtained from the pyrolysis of WWOS at various isothermal temperatures of 350°C, 450°C, and 550°C are depicted in Figure 4. The calculated values shown in the table are the mean values and the standard deviations of at least three pyrolyses operated at the specific temperature. It was found that as the pyrolysis temperature increased, the yield of pyrolysis liquid increased (30.67–39.00 wt%), the gas yield increased (16.98 wt%–19.84 wt%), and the solid yield decreased (52.35–41.16 wt%). The higher yield of pyrolysis liquid and gas occurred owing to secondary cracking at higher temperatures. The solid yield decreased at elevated temperatures, which was also confirmed by the thermogravimetric analysis (Tang et al., 2019). Similar observations were observed in the literature (Gong et al., 2020). Lin et al. (2018) investigated the oily sludge in the U-shaped reactor for saturation-enriched light oil production. It was observed that temperature played a significant role in maximum liquid production. Song et al. (2019) reported different properties of the products obtained from the pyrolysis of oily sludge carried out in a pyrolysis-magnetic separation reactor. The pyrolysis liquid was composed of water and tar. Pyrolysis product yields were observed at different temperatures. It was also reported that the pyrolysis temperature increased the yield of water, tar, and gas, while a corresponding decrease in the yield of solids was also reported. These studies conclude that 550°C was the optimum temperature to obtain the maximum liquid yield using pyrolysis.

GC–MS obtained the composition of oils produced from pyrolysis of wet oily sludge at different temperatures: 350°C, 450°C, and 550°C. The elements acknowledged in the fractions analyzed have been classified into six aliphatic groups, methyl aliphatic, aromatic and polycyclic aromatic compounds, nitriles and amides, alcohols, and oxygen-containing compounds such as carboxylic acids, esters, and ketones. Supplementary Table S3 shows the distribution according to the percentage area of various compounds. These main compounds consisted of hydrocarbons, such as alkanes, aromatics, oxygenates, and others. The aliphatic compounds included alkanes, alkenes, alkynes, their derivates, and compounds with a carbon number from C₅ to C₂₀ like trichloromethane, pentadecane, cetane, hexadecane, nonene, and nonadecane. The aromatic and polycyclic aromatic compounds included phenol, toluene, styrene, pyrrole, benzene, acetonitrile, pyridine, methylpyridine, phenols, benzene propane nitrile, indole, and p-cresol. The alcohol group contained compounds having an OH group attached, for example, 2-Furanmethanol and 1-Dodecanol. The carboxylic acids (O=C-OH), esters (O-C=O), nitriles (C≡N), and amides (O=C-NH₂) were heavy compounds linked with long aliphatic chains with carbon atoms up to 20. As the temperature increased from 350°C to elevated temperatures such as 450°C and 550°C, the area percentage of alkanes was the most abundant in the following order: 75.48% > 76.41% > 76.92%. The higher temperature facilitated the formation of aromatics, polycyclic aromatics, alcoholic compounds, and heavy compounds.

In this study, the predictive modeling based on the neural network has been successfully proposed for the TGA of WWOS at different heating rates of 5°C/min, 20°C/min, and 40°C/min. The model results for TGA at a heating rate of 5°C/min, such as training performance, error histogram, and regression plots, have been discussed in this section. However, the results obtained from the predictive modeling of TGA at a heating rate of 5°C/min, 20°C/min, and 40°C/min are shown in Figures 5A–C; Supplementary Figures S3A–C; Supplementary Figures S4A–C). As mentioned earlier, the best neural network architecture was found to be [2 × 9 × 1] with a minimum training MSE of 6.219e⁻⁵ and a maximum R ² value of 99.99.

The model performance, training, and regression plots are shown in Figure 5. The error histogram is shown in Figure 5A, representing the normal distribution of errors across zero. Figure 5A shows that the error between the experimental and simulated mass% lies in the range of [−0.06 to +0.06]. Since the errors between actual and predicted values are low, it depicts that the model is well trained and can be further utilized. For the prediction of new output data, Figure 5B shows the performance plot using MSE for mean square error for training, validation, and testing of a network with 9 hidden neurons in the hidden layer. It can be observed that the minimum error has been achieved at 253 epochs. Figure 5C shows the training, validation, and testing regression plots. It can be observed that very few errors have been obtained between the target and output values, which demonstrates a strong correlation. The results show that the value of R ² in all the training, validation, and testing plots is close to 1. A good fit between the experimental and predicted data was achieved, as shown in Figure 6. The results explained that the proposed network is in good agreement with the experimental results and could be extensively applied to predict TGA for various waste sludge with nonlinear nature (Bong et al., 2020; Prabhakaran et al., 2020).