A predictive model for biomass waste pyrolysis yield: Exploring the correlation of proximate analysis and product composition

• Explored the impact of volatile content, temperature, and heating rate on yields. • Significant variations in bio-oil, char, and gas yields observed under different conditions. • Predictability of C2-C3, C4-C10, ≥C11 compounds, diesel, gasoline, and volatiles in bio-oil. • Used RSM and ANOVA to confirm complex interactions between key experimental factors. • Empirical equations provided insights for optimizing yields and validated model robustness. This paper presents the outcomes of a comprehensive factorial experiment designed to explore the intricate dynamics of volatile content (ranging from 58 % to 76 %), temperature (ranging from 350 °C to 750 °C), and heating rate (ranging from 5 °C/min to 10 °C/min) on the yields of bio-oil, char, and gas in biomass pyrolysis processes. Through systematic analysis utilizing date stones, spent coffee grounds, and cow manure, significant variations in response variables are unveiled, with the main pyrolysis product of bio-oil yields spanning from 28 % to 61 %, char yields ranging between 23 % and 55 %, and gas yields fluctuating from 6 % to 42 %. In-depth examination of bio-oil compositions (based on carbon number classifications, and bonding arrangements) and gas composition (carbon monoxide, carbon dioxide, hydrogen, and methane) at varying operating conditions elucidates substantial yield differences, emphasizing the nuanced nature of product compositions. Furthermore, advanced statistical techniques, including response surface methodology (RSM) and analysis of variance (ANOVA), reveal and confirm complex interaction patterns among the experimental factors. Results indicate that bio-oil, char, and bio-oil composition, including C2-C3 compounds, C4-C10 compounds, ≥C11 compounds, and aromatics yield, are significantly influenced by the volatiles content and reaction temperature of the feedstock. The paper presents empirical equations offering valuable predictive insights into optimizing product outcomes. Validation studies comparing experimental and predicted yields of ternary biomass feeds highlight the robustness of the proposed models. Additionally, optimization efforts utilizing desirability criteria underscore the multifaceted nature of yield optimization, with tailored conditions demonstrating remarkable success in maximizing desired products while minimizing undesired byproducts. Overall, this study contributes to a deeper understanding of the complex interplay between process parameters and product yields in biomass pyrolysis, offering valuable insights for process optimization and sustainable resource utilization.