Development of an automated, multi-vessel respirometric system to evaluate decomposition of composting feedstocks. (December 2022)

• Record Type: Journal Article
• Authors: Dsouza, Ajwal; Kiselchuk, Connor; Lawson, Jamie A.; Price, Gordon W.; Dixon, Mike; Graham, Thomas
• Is Part Of: Biosystems engineering. Volume 224(2022)
• Page Start: 283
• Page End: 300
• Publication Date: 2022-12
• Subjects: Aerobic composting -- Carbon dioxide -- High-resolution respirometry -- Hardware design -- Reproducibility -- Respiration index; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2022.10.014
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : Aerobic respirometry, which involves measuring the carbon dioxide (CO2 ) evolved during decomposition, is an invaluable metric for evaluating biomass decomposability, characterising compost feedstocks, and studying decomposition dynamics over time. However, respirometric systems and CO2 sampling methods can be expensive, operationally cumbersome, and produce temporally low-resolution data. This paper details the technical development and validation of an automated, multi-vessel respirometric system using off-the-shelf microcontrollers and miniature non-dispersive infrared (NDIR) CO2 sensors to produce temporally high-resolution and accurate CO2 data generated from decomposing biomass. The accuracy of the NDIR CO2 sensors, as given by the cumulative CO2 (g), was validated through an acetic acid-sodium bicarbonate reaction test. In this test, a mean cumulative CO2 evolution of 0.99 g ( n = 8) was measured with the sensors from an expected stoichiometric yield of 1 g CO2, with a standard deviation of ±0.137 g. The operation, reliability, and reproducibility of the system were tested through a series of biomass decomposition experiments. Through these experiments, an airflow rate of 0.25 L min −1 was found to be most effective at preventing the excessive drying of biomass at an initial moisture content of 50%. The system sensed, per 5-s sampling event, a peak CO2 concentration of ∼28, 000 ppm at a temperature of 35 °C, which resulted in the largest mean cumulative CO2 evolution of 27.93 g from 200 g dry biomass. As indicated by the CO2 curves, the system can produce reliable and high-resolution CO2 datasets on an individual vessel basis, making it a useful tool for respirometric studies. Highlights: An aerobic respirometric system with high-resolution CO2 sampling was developed. The reproducibility of the data in organic matter decomposition trials was high. Accuracy of the CO2 sensors was shown through acetic acid-sodium bicarbonate test. The system produced reliable CO2 data for aerobic decomposition of organic waste.