Catalyst deactivation is a complex phenomenon and identifying an appropriate deactivation model is a key effort in the catalytic industry and plays a significant role in catalyst design. Accurate determination of the catalyst deactivation model is essential for optimizing the catalytic process. Different mechanisms of catalyst deactivation by coke and metal deposition lead to different deactivation models for catalyst activity decay. In the rigorous mathematical models of the reactors, the reaction kinetics were coupled with the deactivation kinetic equation to evaluate the product distribution with respect to conversion time. Finally, selective and nonselective deactivation kinetic models were designed to identify catalyst deactivation through the propagation of heterogeneous chemical reactions. Therefore, the present review discusses the catalyst deactivation models designed for CO₂ hydrogenation, Fischer–Tropsch, biofuels and fossil fuels, which can facilitate the efforts to better represent the catalyst activities in various catalytic systems.

The interest in Gas-to-Liquid technology (GTL) is growing worldwide because it involves a two-step indirect conversion of natural gas to higher hydrocarbons ranging from Liquefied Petroleum Gas (LPG) to paraffin wax. GTL makes it possible to obtain clean diesel, naphtha, lubes, olefins, and other industrially important organics from natural gas. This article is a brief review discussing the state-of-the-art of GTL, including the basics of syngas manufacturing as a source for Fischer-Tropsch synthesis (FTS), hydrocarbons synthesis (Fischer-Tropsch process), and product upgrading. Each one is analyzed, and the main characteristics of traditional and catalysts technologies are presented. For syngas generation, steam methane reforming, partial oxidation, two-step reforming, and autothermal reforming of methane are discussed. For Fischer–Tropsch, we highlight the role of catalysis and selectivity to high molecular weight hydrocarbons. Also, new reactors technologies, such as microreactors, are presented. The GTL technology still faces several challenges; the biggest is obtaining the right H₂:CO ratio when using a low steam-to-carbon ratio. Despite the great understanding of the carbon formation mechanism, little has been made in developing newer catalysts. Since 60–70% of a GTL plant cost is for syngas production, it needs more attention, particularly for developing the catalytic partial oxidation process (CPO), given that modern CPO processes using a ceramic membrane reactor reduce the plant’s capital cost. Improving the membrane’s mechanical, thermal, and chemical stability can commercialize the process. Catalytic challenges accompanying the FTS need attention to enhance the selectivity to produce high-octane gasoline, lower the production cost, develop new reactor systems, and enhance the selectivity to produce high molecular weight hydrocarbons. Catalytically, more attention should be given to the generation of a convenient catalyst layer and the coating process for a given configuration.

In this study, an artificial neural network (ANN) model was developed and compared with a rigorous mathematical model (RMM) to estimate the performance of an industrial heavy naphtha reforming process. The ANN model, represented by a multilayer feed forward neural network (MFFNN), had (36-10-10-10-34) topology, while the RMM involved solving 34 ordinary differential equations (ODEs) (32 mass balance, 1 heat balance and 1 momentum balance) to predict compositions, temperature, and pressure distributions within the reforming process. All computations and predictions were performed using MATLAB^® software version 2015a. The ANN topology had minimum MSE when the number of hidden layers, number of neurons in the hidden layer, and the number of training epochs were 3, 10, and 100,000, respectively. Extensive error analysis between the experimental data and the predicted values were conducted using the following error functions: coefficient of determination (R²), mean absolute error (MAE), mean relative error (MRE), and mean square error (MSE). The results revealed that the ANN (R² = 0.9403, MAE = 0.0062) simulated the industrial heavy naphtha reforming process slightly better than the rigorous mathematical model (R² = 0.9318, MAE = 0.007). Moreover, the computational time was obviously reduced from 120 s for the RMM to 18.3 s for the ANN. However, one disadvantage of the ANN model is that it cannot be used to predict the process performance in the internal points of reactors, while the RMM predicted the internal temperatures, pressures and weight fractions very well.

Ultrafiltration membranes are often considered a highly efficient technique for purifying oily wastewater. The primary objective of this research was to improve the performance and antifouling properties of PLA/PBAT membranes used in oily wastewater treatment by incorporating banana peel (BP) nanoparticles. Various characterization techniques, including field emission scanning electron microscopy (FESEM), wettability analysis, pure water flux measurement, porosity assessment, tensile analysis, and FTIR analysis, were employed to describe the prepared membranes. The results of the FT-IR test revealed that BP nanoparticles were effectively integrated into the PLA/PBAT membrane matrix. The contact angle decreased from 73.7° for the pristine PLA/PBAT membrane to 38.99° for the membrane incorporating 0.05 wt.% BP-NPs, indicating that the nanoparticles enhanced the hydrophilic characteristics of the membranes. A similar trend was observed for the pure water flux of PLA/PBAT/BP membranes, suggesting that membranes with a BP-NP concentration of 0.05 weight percent exhibited the highest pure water flux. This improvement can be attributed to the synergistic effects of the nanoparticles. Additionally, the presence of BP-NPs enhanced the mechanical properties of the membranes. Finally, an ultrafiltration system using oily wastewater as feed was employed to evaluate the performance of the prepared membranes. The finding demonstrated that PLA/PBAT/BP membranes exhibited a higher flux and a greater oil removal efficiency of 105.3 L/m²h and 95.2% compared to neat PLA/PBAT membranes (62 L/m²h and 88%), respectively.

This review emphasized the role of mathematical models and correlations in thermogravimetric analysis to evaluate the thermal stability of various materials, including biomass, polymers, recycled plastics, and solid fuels of carbon and biomass material. Numerous thermogravimetric analysis kinetic models are driven, and they are broadly divided into model-free and model-based categories. Integral models have proven to be more effective for fitting, particularly for materials with wide decomposition temperature ranges in biomass material and mixed recycled plastic waste. The nth order model showed superior predictive accuracy compared with the first-order model, particularly for solid biomass, highlighting the significance of model selection. Traditional thermogravimetric analysis mathematical models are limited in accounting for mass loss as a function of all effective variables. In contrast, artificial neural networks (ANNs) efficiently represent and incorporate these variables, marking a significant advancement in predicting thermogravimetric analysis kinetics. ANNs provide powerful tools for managing complex analysis data, enabling robust predictions and deeper insights into material thermal behavior.

In this work, the conversion of waste polypropylene to alternative fuels (liquid and gas) was performed through non-catalytic thermal and catalytic hydrocracking over NiMo/Al₂O₃ and Pt/Al₂O₃ catalysts. The process was carried out in an autoclave batch reactor at a temperature of 450 °C and a pressure of 20 bar, which were selected based on experimental optimization. The spent catalyst was also successfully regenerated at 700 °C under a hot airflow. Experiments were conducted to determine the optimum conditions to completely separate the deactivated catalyst from the solid residue easily. The regenerated catalyst was reused to facilitate the economic cost reduction of the process. The reactivated catalysts have almost the same catalytic properties as the fresh catalysts; this was confirmed by several characterization techniques, such as TGA, XRD, SEM, EDX, BET and FTIR. The produced liquids/gases were quantified and classified into their fractions by the number of carbon atoms and gasoline to diesel ratio using GC/MS. The viscosity, density, API gravity, pour point and flash point of oil cuts were also investigated to evaluate the quality of the resulting liquid from the reactions. The NiMo/Al₂O₃ catalyst gave the highest liquid hydrocarbons yield of 86 wt%, while the highest weight products of gasoline range hydrocarbon fractions of 49.85 wt% were found over the Pt/Al₂O₃ catalyst. Almost the same catalytic behavior was found with the regenerated catalysts compared to the fresh catalysts. However, the highest gaseous products at 20.8 wt% were found in the non-catalytic thermal products with an increase in the diesel fuel range of 80.83 wt%. The kinetic model was implemented using six lumps and fifteen reactions, and the apparent activation energies for the gasoline and diesel fractions were calculated. In general, all primary and secondary reactions show greater activation energy values on the Pt/Al₂O₃ catalyst than on the NiMo/Al₂O₃ catalyst.