Ralph A. Perona



Project title: Predicting Volatile Organic Compound Emissions from In-Vessel Municipal Solid Waste Composting

Degree: MS (Thesis) | Program: Environmental Health Technology (Tech) | Project type: Thesis/Dissertation
Completed in: 1992 | Faculty advisor: John C. Kissel

Abstract:

Composting of municipal solid waste (MSW) is being investigated as an alternative to landfilling for the organic fraction of the waste stream. MSW is known to contain volatile organic compounds (VOCs) at concentrations as high as 0.1% by weight. The high temperatures and mechanical aeration associated with composting are likely to contribute to the stripping of VOCs which might present occupational health risks. However, the rates of volatilization and their dependence on the characteristics of both the VOC and the composting conditions have not yet been quantified. Use of finished compost as a biofilter to absorb odorous compounds in an air stream has also been proposed.

The objective of this research is the development of a mathematical model which describes the physical processes involved in phase exchange in composting MSW. The model employs the fugacity approach to predict emissions of a VOC in exhaust air from a composting vessel over time. Initial equilibrium of the VOC among the sorbent, water, and air phases of the MSW is assumed.

Model corroboration was performed utilizing a mini-composting system and media obtained from a commercial MSW composting facility. Waste spiked with volatile tracers was placed in sealed buckets held at a constant temperature. After equilibration, air was drawn through the waste at a fixed rate. At intervals an air sample from each pail was taked and analyzed for tracer concentration.

Experimental results confirm that VOCs are relatively easily stripped from MSW compost. Modeling results indicate that the model is capable of quantitative prediction of initial air phase tracer concentration and qualitative prediction of variation in emissions with air flow. Refinements in data collection methodology are required for more accurate calibration of model output.