A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Mohammed T. Zaki, Lewis S. Rowles, Donald A. Adjeroh, Kevin D. Orner

Research output: Contribution to journalSystematic reviewpeer-review

7 Scopus citations

Abstract

Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002-2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to cost-effectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

Original languageEnglish
Pages (from-to)1424-1467
Number of pages44
JournalACS ES and T Engineering
Volume3
Issue number10
DOIs
StatePublished - Oct 13 2023

Keywords

  • Decarbonization
  • Energy Recovery
  • Life Cycle Assessment
  • Machine Learning
  • Nutrient Management

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