Low Density Polyethylene (LDPE) waste is a significant challenge for both the environment and industrial management, and this can be converted into valuable fuels by thermochemical processes such as pyrolysis. This study explores comprehensive insights into thermal analysis, pyrolysis mechanism evaluation and advanced predictive modeling to explain the complex degradation behavior of LDPE. Thermal behaviour of LDPE was conducted by Thermogravimetric Analyser (TGA) in the nitrogen atmosphere at heating rates of 2.5, 5, 7.5, and 10 °C/min from room temperature to 1000 °C. For the kinetic analysis, iso-conversional (model-free) methods like Friedman, Kissinger-Akahira-Sunose (KAS) and Ozawa-Flynn-Wall (OFW) methods were applied. Furthermore, the multicomponent Distributed Activation Energy Model (DAEM), which identified two pseudo-components (PC1, PC2), was implemented to analyze the multiple reactions that occur during the pyrolysis of LDPE. Additionally, a pyrolysis experiment was conducted at 500 °C in the fixed-bed reactor. The pyrolysis yielded approximately 48.75 % pyro-oil and 51.25 % non-condensable gases. Findings from Gas Chromatography-Mass Spectrometry (GC-MS) revealed the various compounds in pyro-oil which include the aliphatic and aromatic hydrocarbons, alcohols, ethers and esters. The study further validated the pyrolysis findings through various predictive models, which include the Artificial Neural Networks (ANN), Classification and Regression Trees (C&RT), K-Nearest Neighbors (KNN) and Boosted Regression Trees (BRT). This study demonstrates that machine learning proves to be an effective and reliable approach for modeling LDPE pyrolysis kinetics. This work addresses a significant gap in the literature on LDPE and thus provides a framework that integrates kinetic modeling with the machine learning approach to advance development in the plastic waste conversion. 

The global transition to clean energy demands reliable low-carbon fuels, positioning natural gas (NG) as a critical bridge in mitigating climate change. Its lower greenhouse gas emissions compared to coal and oil, combined with abundant reserves, make NG a vital option for sustainable power generation and industrial use. However, its environmental benefits depend on effective purification, particularly CO2 removal, which determines gas quality, efficiency, and processing costs. This study critically reviews recent developments (2000-2024) in CO2 capture from NG using hybrid ionic liquid-amine systems, evaluating techno-economic and environmental performance. A systematic evaluation was performed using published experimental, modelling, and process simulation data. Published data concerning experimental, modelling, and techno-economic data were considered in a systematic evaluation to compare the performance of conventional absorption, adsorption, membrane, cryogenic and hybrid solvent processes. Hybrid IL-amine solvents achieve 93-98 % CO2 capture efficiency with 20-30 % lower regeneration energy compared to MEA, although at TRL 5-6. These developments highlight the potential of NG to serve as a cleaner transitional fuel while reinforcing the need for integrated policies and technologies that ensure responsible production and utilization. Advancing purification technologies are therefore central to maximizing the role of natural gas in the global clean energy transition.

The pyrolysis potential of date seeds (DS) (an abundant agricultural residue that can support sustainable and resilient energy systems) as a renewable bioenergy feedstock was examined. Thermogravimetric analysis (TGA) of date seeds was performed from ambient temperature to 1000 degrees C under nitrogen at heating rates of 6, 9, 12, and 15 degrees C/min. The feedstock showed high volatile matter and a higher heating value (HHV) of 20.185 MJ/kg, confirming its suitability for thermochemical conversion. Isoconversional model-free methods such as Friedman (FR), Kissinger Akahira-Sunose (KAS), Ozawa-Flynn-Wall (OFW) and an advanced Vyazovkin (VZ) approach were applied over a conversion range of 0.2 to 0.8. A linear combined kinetics analysis gave an apparent activation energy (Ea) of 272.4 +/- 11.8 kJ/mol with a correlation coefficient of 0.9948 with the higher reaction order. Four machine learning (ML) models, including Artificial Neural Networks (ANN), Classification and Regression Trees (C&RT), Boosted Regression Trees (BRT), and Multivariate Adaptive Regression Splines (MARS), were used to predict Ea obtained from thermogravimetric data. The ANN achieved the best performance metrics, with a coefficient of determination (R2) of 0.985 and a Root Mean Squared Error (RMSE) of 3.84. The integrated kinetic and machine-learning framework provides reliable estimates of Ea for DS pyrolysis. The predicted Ea determines the temperature sensitivity of pyrolysis, setting the required heating rate, residence time, and temperature profile in the reactor. These results provide process-level input for reactor design, scaling-up, and optimizing bioenergy production from DS waste.

Pyrolysis requires extensive experimentation to achieve optimum thermochemical conversion, which can be addressed by integrating machine learning (ML) predictive solutions. The abundant availability of algae with low volume footprint makes it viable green biomass to achieve thermochemical products. For optimum algal biochar (BC) yield production, the relation of ultimate, proximate analysis with process conditions is critical. This study's objective is twofold: It aims to develop a robust ML model, trained on diverse literature data and optimized using particle swarm optimization and genetic algorithm, that predicts BC yield across various feedstocks and conditions. Secondly, the optimum process parameters are derived to maximize BC yield with the experimental validation for the collected samples at their respective chemical and structural compositions. Limited data points for algal biomass induce a comparative analysis of ML models, including Gaussian process regression, ensembled tree (ET), decision tree and support vector machine. The predictive capability of ET enhanced through optimization performed exceptionally well for BC yield prediction with testing R2 = 0.77993 and RMSE = 6.9792. 2D and 3D partial dependence plots imply that BC yield is primarily influenced by pyrolysis temperature, volatile matter, and heating rate with SHAP values of 1.2785, 0.3972, and 0.2949, respectively. Monte Carlo simulation and Sobol sensitivity analysis substantiate statistically the impact of selected features on algal BC yield. Inverse optimization of ET model suggests that the maximum BC yield production is 76.33% at a temperature of 500 degrees C, a heating rate of 10 degrees C/min, a residence time of 60 min, a N2 flow rate of 0.5 L/min, and particle size of 1.5mm.

Liquefied natural gas (LNG) production is an energy-intensive process, and mixed-refrigerant cycles dominate industrial applications. This study proposes and optimises a novel propane-free dual mixed refrigerant (DMR) configuration for LNG liquefaction using Aspen HYSYS v14 with the Peng-Robinson EOS. The system eliminates propane to enhance operational safety while maintaining high thermodynamic efficiency. Energy, exergy, and advanced exergy analyses reveal a specific energy consumption (SEC) of 0.29 kW/kg LNG and an exergy efficiency of 67.27 %. The advanced exergy assessment identifies that 68.40 % of total exergy destruction is avoidable, indicating significant potential for further efficiency gains. Comparative benchmarking confirms that the proposed configuration achieves one of the lowest reported SEC values among both propane-based and propane-free DMR cycles. This research provides a technically validated and safer pathway towards sustainable LNG production.

Landfill mixed waste plastics are difficult to recycle mechanically and often end up in open burning or uncontrolled disposal, especially in low- and middle-income countries. This study evaluates supercritical water liquefaction as a contamination-tolerant, water-based route to recover transport fuel range oils from real landfillsourced mixed waste plastics and mechanically recycled HDPE, LDPE, and PP, including their blends. Batch experiments were carried out in a 400 mL stainless steel reactor at 425 degrees C for 3 h and 450 degrees C for 1 h, with a feed plastic-to-water mass ratio of about 1:2. Phase-separated products were quantified and characterized by thermogravimetric fractionation, Fourier transform infrared spectroscopy, and gas chromatography mass spectrometry. At 425 degrees C and 3 h, oil yields reached 75 f 4 wt% for HDPE, 70 f 3.5 wt% for LDPE, 60 f 3 wt% for PP and 22 f 1.5 wt% for landfill mixed waste plastics, with mass closures of 92 to 95 percent in experiments with gas analysis. At 450 degrees C and 1 h, resulted in enhanced gas formation and a pronounced shift toward aromatic hydrocarbons, with PP-rich systems exhibiting aromatic peak areas reaching about 90% and gasoline-range (C6 -C12) fractions of 56 to 78 wt% by thermogravimetric pseudo distillation. The results show structure-dependent reaction pathways in supercritical water: PP favors aromatic products, PE-rich feeds favor paraffinic and olefinic products, and blends with landfill-mixed waste plastics still deliver significant gasoline-range fractions. These findings indicate that supercritical water liquefaction can couple water-based chemical recycling of contaminated plastic streams with recovery of high-quality hydrocarbon fuels, with direct relevance for integrated energy and waste management systems at the energy environment nexus.

Greenhouse gas emissions from human activities pose a significant threat to the ecosystem, causing climate change and ecological disruptions. Ionic liquids (ILs) show promise for gas separation and carbon capture, but predicting gas solubility in ILs is challenging due to limited data and complex thermodynamics. Artificial intelligence (AI) offers an innovative approach to improve the efficiency and accuracy of solubility predictions. This review analyzes recent advancements in AI-enabled solubility predictions, focusing on methodologies, models, and applications in gas separation and carbon capture. It examines artificial neural networks, deep learning models, and support vector machines for predicting solubility in ILs, and presents valuable results demonstrating the potential of these techniques. The study highlights AI’s transformative power in understanding gas-IL interactions and inspiring environmentally friendly separation processes. It also discusses integrating AI-driven predictions with process modeling tools like Aspen Hysys and Aspen Plus, aiming to stimulate further research in gas separation technologies and pave the way for practical implementation. 

Assessing the synergies between thermal energy and the torrefaction severity index by elucidating the effects of biomass torrefaction conditions on product characteristics is still a relevant research question. This study explores the optimization of torrefaction of spruce wood sawdust by analyzing the chemical and physical features of the resultant material. The study employs thermogravimetric analysis and Thermal Desorption-Gas Chromatography/Mass Spectrometry (TD-GC/MS) to examine the changes in the components during torrefaction. The torrefaction process has a profound effect on the composition, causing conversion of up to 30 % of the initial mass to volatile organic compounds and incondensable gases when subjected to a temperature of 300 °C. The correlation matrix highlights the relationships between variables, including time, temperature, heating value, mass yield, energy yield, and ratios of C, H, and O. The matrix visually represents the interplay between these factors during the torrefaction process. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that torrefaction severity index, cellulose, lignin, torrefaction time, and temperature correlate positively, while H/C, O/C, and hemicellulose content correlate negatively. The ANN model exhibited superior predictive accuracy (R² = 0.99982, RMSE = 0.00359), surpassing C&RT (R² = 0.99483, RMSE = 0.01943), KNN (R² = 0.99467, RMSE = 0.01974), and SVM (R² = 0.99022, RMSE = 0.02674), thus validating the efficacy of machine learning for precise torrefaction severity index (TSI) prediction. This finding enhances the efficiency of biomass processing and provides reliable tools for future research in the field, thereby informing and guiding future studies and industrial applications. 

Managing the textile sludge has become a significant global challenge because it is often disposed of in landfills without recovering its energy potential. Pyrolysis has emerged as a promising technology to extract energy and valuable chemicals from textile sludge. This study investigates the pyrolysis of textile sludge through thermogravimetric analysis (TGA) conducted from ambient temperature to 1000 degrees C at three different heating rates of 2.5, 5 and 7.5 degrees C/min in an inert environment. The kinetic study complemented was isoconversional method which includes both differential and integral (Friedman, Ozawa-Flynn-Wall & Kissinger-Akahira-Sunose) method. Moreover, five pseudo-components (pc1, pc2, pc3, pc4, pc5) were obtained by multi distributed activation energy model (M-DAEM). Additionally, combined kinetics retrieves the Ea of 98.3 (+3.6) kJ/mol with R2 value of 0.9924. Furthermore, pyrolysis of textile sludge was performed in fixed bed reactor (FBR) at 500 degrees C that results in a pyro-oil, non-condensable gases and the biochar yields 17.5(+1.7) %, 13.7 (+3) % and 68.8 (+1.3) % respectively. To optimize the pyrolysis of textile sludge, it is essential to comprehend the complex kinetics involved in its degradation. Machine learning models like Artificial Neural Networks (ANN), Classification and Regression Trees (C&RT), Boosted regression trees (BRT), and K Nearest Neighbors (KNN) employed to predict the activation energy (Ea) with ANN emerging as superior predicting capabilities (R2 = 0.999) over other models. This study demonstrates the remarkable ability of ANN, C&RT, BRT and KNN to accurately analyze complex relationships, predicts the textile sludge pyrolysis kinetics and confirms the potential of textile sludge pyrolysis as a sustainable and efficient energy source.

In response to escalating concerns over climate change and rising CO2 emissions, this research investigates the efficiency, environmental impact, and economic feasibility of amine-based carbon capture processes from natural gas. The study focuses on optimizing solvent blends to enhance CO2 capture efficiency while minimizing energy consumption and operational costs. Various solvent combinations of primary, secondary, and tertiary amines use a ‘comprehensive process systems engineering’ approach. The exergy analysis of amine-based carbon capture from natural gas showed considerable solvent performance differences. Diisopropanolamine (DIPA) performed the best, saving 91% of energy and minimizing energy losses. Monoethanolamine (MEA)+DIPA had the highest exergy efficiency (99.204%), while DIPA had the lowest CO₂ emission rate (1207.04 kg/h) (5.7% lower than the maximum reported emissions). DIPA used 1.10×10⁸ kJ/h of energy efficiently, saving 58.8% and 63.3% compared to MEA+DIPA and diethanolamine (DEA)+MEA, respectively. DEA's exergy destruction factor was 0.74, 77.7% lower than MEA's (3.32), showing improved efficiency. DIPA had the highest exergy stability, 0.027, 96.9% more thermodynamically stable than MEA. DIPA was the most cost-effective option, with an annual exergy destruction cost of USD 3.86×10⁷, 91.3% cheaper than MEA's (USD 4.43×10⁸). Additionally, DIPA had the most considerable yearly revenue from CO₂ sales at USD 4.83×10⁵, exceeding other solvent combinations. These results demonstrate DIPA's superior efficacy across various parameters, suggesting it may be the best solvent.