# Towards an eco-social circular economy: exploring the feasibility study of pyrolysis on agricultural feedstocks

## TL;DR

This study explores using pyrolysis to convert agricultural waste into energy, showing potential for a circular economy in farming.

## Contribution

The study evaluates the feasibility of pyrolysis on various crop straws for energy generation in a circular economy context.

## Key findings

- Syngas composition and volume increased with higher pyrolysis temperatures.
- The system's energy conversion efficiencies reached maximum values of 37.1% (cold gas), 30.1% (net), and 174.4% (electrical).
- Wheat, barley, bean, and OSR feedstocks showed high predictability in syngas production.

## Abstract

The agricultural sector is challenging to decarbonise due to its reliance on heavy machinery and fossil fuels, which face issues when decarbonising via methods such as electrification. However, agriculture provides opportunities to generate renewable energy via biomass sources due to their abundance within this sector. This feasibility study used a continuous auger pyrolysis system to assess how straw waste from a medium-scale arable farm could convert energy from an external electrical source into usable chemical potential. Wheat, barley, oil seed rape (OSR), and bean straw have all been processed and pyrolysed under different temperatures and auger feed rates. The syngas product was then analysed, considering its composition and the lower heating value. Results indicate that the percentage of carbon monoxide and hydrogen and the total volume of syngas increased with temperature. In addition, the syngas’ energy quantity increased despite the product’s decreasing heating value. The case study’s annual energy demand was equal to 14.4% of the 3900 GJ maximum potential contained within the syngas, and thus it can be concluded that there is potential for the application of this system towards a circular economy. The system’s cold gas, net, and electrical conversion efficiency were also assessed with maximum values of 37.1%, 30.1%, and 174.4%, respectively. Furthermore, the statistical analysis confirms high predictability for wheat, barley, bean, and OSR feedstocks, with a general linear model showing high accuracy across all.