Tree-Based Automated Machine Learning to Predict Biogas Production for Anaerobic Co-digestion of Organic Waste
[1];
[3]
- Energy & Biosciences Institute, University of California, Berkeley, Berkeley, California 94704, United States, Life-Cycle, Economics, and Agronomy Division, Joint BioEnergy Institute, Emeryville, California 94608, United States, Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Life-Cycle, Economics, and Agronomy Division, Joint BioEnergy Institute, Emeryville, California 94608, United States, Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Energy & Biosciences Institute, University of California, Berkeley, Berkeley, California 94704, United States, Life-Cycle, Economics, and Agronomy Division, Joint BioEnergy Institute, Emeryville, California 94608, United States, Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States, Energy Analysis & Environmental Impacts Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
The dynamics of microbial communities involved in anaerobic digestion of mixed organic waste are notoriously complex and difficult to model, yet successful operation of anaerobic digestion is critical to the goals of diverting high-moisture organic waste from landfills. Machine learning (ML) is ideally suited to capturing complex and nonlinear behavior that cannot be modeled mechanistically. This study uses 8 years of data collected from an industrial-scale anaerobic co-digestion (AcoD) operation at a municipal wastewater treatment plant in Oakland, California, combined with a powerful automated ML method, Tree-based Pipeline Optimization Tool, to develop an improved understanding of how different waste inputs and operating conditions impact biogas yield. The model inputs included daily input volumes of 31 waste streams and 5 operating parameters. Because different wastes are broken down at varying rates, the model explored a range of time lags ascribed to each waste input ranging from 0 to 30 days. The results suggest that the waste types (including rendering waste, lactose, poultry waste, and fats, oils, and greases) differ considerably in their impact on biogas yield on both a per-gallon basis and a mass of volatile solids basis, while operating parameters were not good predictors of yield at this facility.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Bioenergy Technologies Office (BETO); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Bioenergy Technologies Office
- Grant/Contract Number:
- AC02-05CH11231; EE0008934
- OSTI ID:
- 1820677
- Alternate ID(s):
- OSTI ID: 1829204; OSTI ID: 2318734
- Journal Information:
- ACS Sustainable Chemistry & Engineering, Journal Name: ACS Sustainable Chemistry & Engineering Vol. 9 Journal Issue: 38; ISSN 2168-0485
- Publisher:
- American Chemical SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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