Predictive Optimal Control of Mild Hybrid Trucks

dc.contributor.authorPramanik, Sourav
dc.contributor.authorAnwar, Sohel
dc.contributor.departmentMechanical and Energy Engineering, Purdue School of Engineering and Technology
dc.date.accessioned2024-06-07T14:30:47Z
dc.date.available2024-06-07T14:30:47Z
dc.date.issued2022
dc.description.abstractNumerous per- and polyfluoroalkyl substances (PFASs) occur in consumer food packaging due to intentional and unintentional addition, despite increasing concern about their health and environmental hazards. We present a substance flow analysis framework to assess the flows of PFASs contained in plant fiber-based and plastic food packaging to the waste stream and environment. Each year between 2018 and 2020, an estimated 9000 (range 1100–25 000) and 940 (range 120–2600) tonnes per year of polymeric PFASs were used in 2% of food packaging in the U.S. and Canada, respectively. At least 11 tonnes per year of non-polymeric PFASs also moved through the food packaging life cycle. Approximately 6100 (range 690–13 000) and 700 (range 70–1600) tonnes per year of these PFASs were landfilled or entered composting facilities in the U.S. and Canada, respectively, with the potential to contaminate the environment. The results suggest that minimal food packaging contains intentionally added PFASs which, nonetheless, has the potential to contaminate the entire waste stream. Further, this indicates that PFASs are not needed for most food packaging. These results serve as a benchmark to judge the effectiveness of future industry and government initiatives to limit PFAS use in food packaging.
dc.eprint.versionFinal published version
dc.identifier.citationPramanik, S.; Anwar, S. Predictive Optimal Control of Mild Hybrid Trucks. Vehicles 2022, 4, 1344–1364. https://doi.org/10.3390/vehicles4040071
dc.identifier.urihttps://hdl.handle.net/1805/41293
dc.language.isoen_US
dc.publisherMDPI
dc.relation.isversionof10.3390/vehicles4040071
dc.relation.journalVehicles
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.sourcePublisher
dc.subjectDynamic program
dc.subjectFuel economy
dc.subjectGlobal optimization
dc.subjectPredictive control
dc.titlePredictive Optimal Control of Mild Hybrid Trucks
dc.typeArticle
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