Journal article
In-Line Diffuse Reflectance Spectroscopy Enables Rapid Monitoring of Full-Scale Anaerobic Co-Digestion
Energy & fuels, Vol.39(48), pp.22756-22767
12/04/2025
DOI: 10.1021/acs.energyfuels.5c04529
Appears in UI Libraries Support Open Access
Abstract
In-line diffuse reflectance spectroscopy with partial least-squares regression was piloted at a full-scale municipal anaerobic codigestion facility to evaluate rapid monitoring of heterogeneous feedstocks and digestate. Models developed from 42 high-strength waste samples and 146 digestate samples yielded operationally useful predictions (root-mean-square error 10–27%) for total solids, volatile solids, fats, chemical oxygen demand, volatile acids, and alkalinity. Protein and carbohydrate predictions were less accurate due to limitations in reference data. Importantly, predicted volatile acid:alkalinity ratios provided rapid indicators of digester stability. Downsampling analysis demonstrated that effective models could be developed with fewer than 50 training samples for several parameters, highlighting opportunities to reduce analytical costs. Field deployment during periods of digester instability, including foaming and failure, further validated the robustness of diffuse reflectance spectroscopy models under dynamic operating conditions. These results establish diffuse reflectance spectroscopy as a potentially cost-effective tool for improving process stability, biogas yield, and decision-making at full-scale anaerobic codigestion facilities.
Details
- Title: Subtitle
- In-Line Diffuse Reflectance Spectroscopy Enables Rapid Monitoring of Full-Scale Anaerobic Co-Digestion
- Creators
- Zoe A. M. Kramin - University of IowaMaclaine K. PutneyCraig L. Just
- Resource Type
- Journal article
- Publication Details
- Energy & fuels, Vol.39(48), pp.22756-22767
- DOI
- 10.1021/acs.energyfuels.5c04529
- ISSN
- 0887-0624
- eISSN
- 1520-5029
- Publisher
- American Chemical Society
- Grant note
- University of Iowa: NA National Institute of General Medical Sciences: T32 GM152268
We are thankful for the assistance from Jon Koch, Muscatine WRRF Director; Patti Fuller-Bloechl, Muscatine WRRF Lab Supervisor; Sean Ryan, Valkyrie Analytics (previously University of Iowa); Jason Knox, IIHR - Hydroscience and Engineering CAD drafter/designer; James Niemeier, IIHR - Hydroscience and Engineering Electronics Shop Manager; Brandon Barquist, IIHR - Hydroscience and Engineering Shops Manager; and several Muscatine WRRF operators. Additional thanks to undergraduate researchers Erin Elshoff, Cullen McDermott, Halle Eisfelder, and Sam Greiner for their assistance with sample collection and lab work. Funding to support Zoe Kramin's efforts was partially provided from the Iowa Biotech Training Program as funded by a T32 grant awarded by the National Institute of General Medical Sciences Predoctoral Institutional Research Training Grant (NIGMS T32 GM152268) and administered by the Center for Biocatalysis and Bioprocessing (CBB) at the University of Iowa. This work was also supported financially by the Bioenergy Innovation Fund at the University of Iowa Center for Advancement and the Donald E. Bently Professorship at the University of Iowa. OpenAI's ChatGPT (GPT-4) was used to assist with writing and troubleshooting Python scripts for data processing, as well as grammar and language editing. All text, code, and results were reviewed and validated by the authors, who take full responsibility for the content of this manuscript.
- Language
- English
- Electronic publication date
- 11/20/2025
- Date published
- 12/04/2025
- Academic Unit
- Civil and Environmental Engineering; IIHR--Hydroscience and Engineering
- Record Identifier
- 9985035041402771
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