The combustion characteristics of biochar obtained from barley straw (BS) and brown algae (BA) are explored. Four different heating rates are utilized to determine the respective activation energies. A master plot analysis is used to identify the appropriate reaction model. The results show that the activation energies vary in the ranges of 6.86–48.36 and 46.34–77.51 kJ mol−1 for BS and BA biochar combustion, respectively. As the heating rate increases, most of the combustion characteristic parameters increase, while the combustion stability index decreases. These observations help provide a deeper understanding of the combustion of lignocellulosic and algal biomass-derived biochar.

This study investigates the co-pyrolysis behavior of two lignocellulosic biomass blends, bamboo (B), and rice straw (R) with a plastic polyethylene (P). A total of 15 samples, including binary and ternary blends, were analyzed. Firstly, X-ray diffraction (XRD) analysis was performed to reveal high crystallinity in the B25R75 blend (I/I_c = 13.39). Whereas, the polyethylene samples showed persistent ZrP₂O₇ and lazurite phases (I/I_c up to 3.12) attributed to additives introduced during the manufacturing of the commercial plastic feedstock. In addition, scanning electron microscopy with energy-dispersive X-ray (SEM-EDX) spectroscopy was performed to characterize the surface morphology and elemental composition of the feedstock. Moreover, thermogravimetric analysis (TGA) was employed at temperatures up to 700 °C at three different heating rates (5, 10, and 20 °C/min) under pyrolysis conditions. Kinetic analysis used TGA data to calculate activation energy via Friedman’s isoconversional method, and the blended samples exhibited a decrease in activation energy compared to the individual components. Furthermore, the study evaluated transient interaction effects among the components by assessing the deviation between experimental and theoretical weight loss. This revealed the presence of significant synergistic behavior in certain binary and ternary blends. The results demonstrate that co-pyrolysis of bamboo and rice straw with polyethylene enhances thermal decomposition efficiency and provides a more favorable energy recovery route.

Catalytic pyrolysis has emerged as a pivotal technology for converting renewable diverse feedstocks (i.e., lignocellulosic biomass, algal biomass, and plastic wastes) into biofuels and chemicals. This review comprehensively examines the reaction kinetics in catalytic pyrolysis, addressing the fundamental gap between lab-scale research and industrial applications. The mechanisms of conventional (i.e., electrical heating) and microwave-assisted catalytic pyrolysis are detailed, highlighting the role of catalysts in altering reaction rates, reaction pathways, and decreasing activation energies. This paper delves into kinetic analysis techniques by comparing the model-free and model-fitting approaches and exploring the emerging role of machine learning in predicting kinetic parameters. In addition, it extensively explores the feedstock specific kinetic models, highlighting the behavior of pseudo-components of lignocellulosic feedstocks, plastic wastes, and their mixtures with a specific focus on synergistic effect during co-pyrolysis. Further, an essential framework to integrate molecular-scale phenomena with reactor-scale process performance was presented by exploring the advanced modelling techniques such as microkinetic modelling using density functional theory (DFT), lumped system analysis using process simulations, and catalyst deactivation kinetics. Despite its promise, challenges such as catalyst deactivation, heat and mass transfer limitations, and feedstock variability remain critical hurdles. This review concludes by identifying future research directions, emphasizing the in-situ characterization, integration of machine learning and artificial intelligence for process optimization, and kinetics of emerging catalyst systems to facilitate the commercial deployment of predictive models for catalytic pyrolysis technologies.