The effect of torrefaction temperature and catalyst loading in Microwave-Assisted in-situ catalytic Co-Pyrolysis of torrefied biomass and plastic wastes

Ramesh Potnuri; Dadi V Suriapparao; Chinta Sankar Rao; Veluru Sridevi; Abhishankar Kumar; Manan Shah

doi:10.1016/j.biortech.2022.128099

The effect of torrefaction temperature and catalyst loading in Microwave-Assisted in-situ catalytic Co-Pyrolysis of torrefied biomass and plastic wastes

Bioresour Technol. 2022 Nov:364:128099. doi: 10.1016/j.biortech.2022.128099. Epub 2022 Oct 12.

Authors

Ramesh Potnuri¹, Dadi V Suriapparao², Chinta Sankar Rao¹, Veluru Sridevi³, Abhishankar Kumar⁴, Manan Shah⁵

Affiliations

¹ Department of Chemical Engineering, National Institute of Technology Karnataka, Surathkal 575025, India.
² Department of Chemical Engineering, Pandit Deendayal Energy University, Gandhinagar 382426, India. Electronic address: dadi.suriapparao@sot.pdpu.ac.in.
³ Department of Chemical Engg, AU College of Engineering (A), Andhra University, Visakhapatnam 530003, India.
⁴ Head of Data Science Technology, MPM India Pvt. Ltd. Ganeshkhind Road, Shivaji Nagar, Pune 411005, India.
⁵ Department of Chemical Engineering, Pandit Deendayal Energy University, Gandhinagar 382426, India.

PMID: 36241069
DOI: 10.1016/j.biortech.2022.128099

Abstract

In the current study, the effect of torrefaction temperatures (125-175 °C) and catalyst quantity (5-15 g) on co-pyrolysis of torrefied sawdust (TSD) and polystyrene (PS) are investigated to obtain value-added products. The role of torrefaction in co-pyrolysis of TSD: PS was analyzed to understand the product yields, synergy, and energy consumption . As the torrefaction temperature increases, oil yield (48.3-59.6 wt%) and char yield (24.3-29 wt%) increase while gas yield (27.4-11.4 wt%) decreases. Catalytic co-pyrolysis showed a significant level of synergy when compared to non-catalytic co-pyrolysis. For the conversion (%), a positive synergy maximum (-2.6) exists at a torrefaction temperature of 175 °C and 15 g of KOH catalyst. To develop the model, polynomial regression-based machine learning was used to predict pyrolysisproduct yields and energy usage variables. The developed models showed significant prediction accuracy (R² > 0.98), suggesting the experimental values and the predicted values matched well.

Keywords: Microwave-assisted pyrolysis; Polystyrene; Sawdust; Synergistic effect; Torrefaction.