2021
DOI: 10.1021/acs.est.0c07484
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Estimation of Unit Process Data for Life Cycle Assessment Using a Decision Tree-Based Approach

Abstract: Lacking unit process data is a major challenge for developing life cycle inventory (LCI) in life cycle assessment (LCA). Previously, we developed a similarity-based approach to estimate missing unit process data, which works only when less than 5% of the data are missing in a unit process. In this study, we developed a more flexible machine learning model to estimate missing unit process data as a complement to our previous method. In particular, we adopted a decision tree-based supervised learning approach to… Show more

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Cited by 43 publications
(17 citation statements)
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“…Zhao et al. (2021) show how unit process data can also be estimated using machine learning. Such developments could not only benefit LCA but also input–output databases in mapping activities and products as well as their environmental extensions.…”
Section: Envisioning the Role Of Ai In Iementioning
confidence: 99%
See 1 more Smart Citation
“…Zhao et al. (2021) show how unit process data can also be estimated using machine learning. Such developments could not only benefit LCA but also input–output databases in mapping activities and products as well as their environmental extensions.…”
Section: Envisioning the Role Of Ai In Iementioning
confidence: 99%
“…For example, there are opportunities to be investigated in the conversion of domain-specific data into data useful for life cycle inventories, as shown by Mittal et al (2018) in the use of data mining to convert industrial process databases in data useful for LCI. Zhao et al (2021) show how unit process data can also be estimated using machine learning.…”
Section: Envisioning the Role Of Ai In Iementioning
confidence: 99%
“…When the learning rate is set to 0.5 and the number of center points is 2 and 3, respectively, the center points of the four attributes of the dataset are shown in Figure 7. It can be seen that there is a big difference between the four major attributes under different numbers of center points [18]. e maximum and minimum number of center points of browsertime are 7.1 and 9.58, respectively, the maximum and minimum number of center points of onlinenotes are 0.1 and 43.75, respectively, the maximum and minimum number of center points of bbspost are 25.37 and 43.85, respectively, and the maximum and minimum number of center points of test-Score are 0.57 and 43.75, respectively.…”
Section: Evaluation and Analysis Of Moocs Model In College Physical E...mentioning
confidence: 99%
“…There are many developments on different knowledge-based and data-driven simulation approaches to estimate life cycle inventories, such as missing inventory estimation tool (MIET), artificial neural networks (ANN), , bill of materials (BOM), and unit process data. , However, there are limitations with these methods. More specifically, MIET suffers from data aggregation, relying on the national input–output tables with respect to input requirements; in this way, the inventory data suffers from scarce accuracy .…”
Section: Introductionmentioning
confidence: 99%
“…List of LCA Studies on PVDF Applied in Emerging Technologies (Note: several methods that have been proposed to address data gaps include (1) new data collection, 17 (2) creation of aggregated datasets, 17 (3) extrapolated data, 18 (4) proxy or surrogate data, 18,19 (5) estimate gate-to-gate life cycle information via engineering process techniques, 20 (6) estimate inventory through the use of stoichiometric equations, 20 (7) purchase from commercial LCA databases, 21 (8) estimate LCI using a generic input−output scheme for product production, with parameter values derived from on-site data and heuristics; 22 There are many developments on different knowledge-based and data-driven simulation approaches to estimate life cycle inventories, such as missing inventory estimation tool (MIET), 38 artificial neural networks (ANN), 39,40 bill of materials (BOM), 41 and unit process data. 42,43 However, there are limitations with these methods. More specifically, MIET suffers from data aggregation, relying on the national input−output tables with respect to input requirements; in this way, the inventory data suffers from scarce accuracy.…”
Section: ■ Introductionmentioning
confidence: 99%