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

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 costeffectively 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.

A framework for informing context-sensitive sustainable management of organic waste in rural agricultural regions

“…Spearman's rank correlation is helpful to understand the pair-wise linear and nonlinear relationships (i.e. sensitivities) between input and output factors in a model (correlation coefficients of −1 and 1 refer to perfectly negative and positive relationship, respectively, and 0 refers to no relationship) [37,81]. The major drivers identified through Spearman's rank correlation analysis along with stakeholder meetings helped identify relevant policies that can be informed to promote sustainable organic waste management in rural agricultural regions through RRCC in Hardy County.…”

“…Secondly, these existing studies lacked assessment of social impacts, one of the three important sustainability metrics for context-sensitive assessment of RRCC implementation [14]. One reason for the lack of social impact assessments (SIAs) in the RRCC literature could be because of the absence of specific guidelines related to quantifying social factors in waste management studies, unlike the environmental and economic factors that are typically quantified using LCA and TEA, respectively [36,37]. Therefore, based on the limitations of past studies, there is a need to develop a data-driven framework to quantify region-specific organic waste streams and determine social factors that can be easily linked to the LCA processes (similar to TEA) to conduct a context-sensitive assessment of sustainable organic waste management in rural agricultural regions for better informing relevant policies.…”

Understanding Anaerobic Co-digestion of Organic Wastes through Meta-Analysis

“…In contrast, meta-analysis, a statistical analysis of consolidated quantitative results from multiple studies addressing similar research questions, offers a clearer picture of the effects of AcoD compared to mono-digestion. − For instance, meta-analyses have been conducted to evaluate the effects of co-digestion on specific methane yield (SMY) and to understand the synergistic effects of livestock manure AcoD with other feedstocks (e.g., crop residues, kitchen waste, and microalgae) . For example, Ma et al (2020) conducted a meta-analysis examining how AM in AcoD impacts SMY, considering factors like the proportion of AM, the concentration of substrates (gVS/L), C/N ratio, OLR, and HRT .…”