WaterTAP v1.0.0

Beattie, K.; Gunter, Daniel; Knueven, Ben; Lee, Andrew; Ladshaw, Austin; Drouven, Markus G.; Bartholomew, Tim; Bi, Xiangyu; Bianchi, Ludovico; Arnold, Travis; Atia, Adam; Wang, Chenyu; Miara, Ariel; Kurban, Sitterley; Allu, Srikanth; Dudchenko, Alexander V.; Amusat, Oluwamayowa; Panda, Kinshuk; Young, Ethan

doi:10.11578/dc.20210527.7

Cited by 1 publication

(1 citation statement)

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“…However, the lack of such studies in the literature can be fulfilled by developing open-source packages by integrating already existing ML tools in Python (e.g., Sci-kit learn) with the LCA and LCCA/TEA methods following our proposed framework. Open-source Python packages for LCA and LCCA/TEA such as QSDSan (Quantitative Sustainable Design of Sanitation and Resource Recovery Systems), , SwolfPy (Solid waste optimization life-cycle framework in Python) and WaterTAP may provide a foundation to develop such integrated tools. A major advantage of these packages is built-in uncertainty and sensitivity analyses methods.…”

Section: Discussionmentioning

confidence: 99%

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

Zaki,

Rowles,

Adjeroh

et al. 2023

ACS EST Eng.

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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.

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