Prediction of dimethyl disulfide levels from biosolids using statistical modeling

Area of Science:

• Environmental Engineering
• Wastewater Treatment
• Odor Control

Background:

• Biosolids produced by advanced wastewater treatment plants (WWTPs) can release odorants like dimethyl disulfide (DMDS).
• Controlling DMDS emissions is crucial for managing biosolids odor and improving public acceptance of WWTP operations.

Purpose of the Study:

• To develop and validate statistical models for predicting DMDS concentrations in biosolids.
• To identify key operational variables that influence DMDS release for proactive odor management.

Main Methods:

• Utilized two regression models to predict DMDS concentrations based on operational data.
• Identified control variables including oxidation-reduction potential (ORP) in gravity thickeners (GT) and dissolved air flotation (DAF) units, centrifuge dewatering rates, and sludge blend ratios.

Main Results:

• Both models demonstrated strong predictive accuracy, with adjusted R2 values of 0.79 and 0.77.
• Model coefficients had expected signs, confirming the influence of identified control variables on DMDS levels.
• The models successfully explained observed DMDS levels in sludge headspace samples.

Conclusions:

• The developed statistical models are effective tools for predicting and managing DMDS release from biosolids.
• Plant operators can use these models to adjust operational parameters and proactively reduce biosolids odors.
• These models contribute to improved biosolids management strategies in WWTPs.