國立虎尾科技大學 |

Life Cycle Assessment Based Greenhouse Gas Emission Reductions, Cross Country Analysis and Algorithm Aided Prediction for Lightweighted Composite Auto Parts.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Life Cycle Assessment Based Greenhouse Gas Emission Reductions, Cross Country Analysis and Algorithm Aided Prediction for Lightweighted Composite Auto Parts./
作者:	Akhshik, Masoud.
面頁冊數:	1 online resource (162 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Environmental engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798209907442

Life Cycle Assessment Based Greenhouse Gas Emission Reductions, Cross Country Analysis and Algorithm Aided Prediction for Lightweighted Composite Auto Parts.
Akhshik, Masoud.

Life Cycle Assessment Based Greenhouse Gas Emission Reductions, Cross Country Analysis and Algorithm Aided Prediction for Lightweighted Composite Auto Parts. - 1 online resource (162 pages)

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2022.

Includes bibliographical references

Algorithms made our life simpler and enables Machine to learn and predict and seems like a promising revolutionary science of the future. Even though this has been a hotspot in many aspects of science; there are some areas still behind. One of these areas is the Environmental and lifecycle assessments (LCA). The main problem with these areas is that the collected data are not comparable as every research has its own unique details even though LCA has a standard set of guidelines to perform. The second main problem is that we have very limited data avail-able for machines to be able to use in these areas. Here in this thesis, several normalization methods and a coefficient were used to make these data comparable. Also, because we had lim-ited data available, we performed several LCA studies to generate more data to be able to use it in Machine learning algorithms for the purpose of testing, validation and training. One of the advantages of having limited data here was that it enabled us to implement several machine learning algorithms and methods to compare their performances. The results of this study created a powerful tool that can help researchers, original equipment manufacturers, policymakers and automotive companies to make better environmental decisions prior to any design stages just by knowing the percentage of lightweighted automotive parts. This tool is specifically created to work with lightweighting that is resulted from replacing a glass fibre automotive part with natural fibre-reinforced composites. Also, with the developed coefficient of countries, one can transfer the LCA data from one country to another by using a simple equation. These coefficients were developed by using the countries posted total primary energy supply and electricity grid mix. By using these tools, we will have a better understanding of our emissions prior to any design with relatively high accuracy.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798209907442Subjects--Topical Terms:

557376
Environmental engineering.
Subjects--Index Terms:

Automotive partsIndex Terms--Genre/Form:

554714
Electronic books.

Life Cycle Assessment Based Greenhouse Gas Emission Reductions, Cross Country Analysis and Algorithm Aided Prediction for Lightweighted Composite Auto Parts.
LDR:03397ntm a2200397K 4500 001 1141183
005 20240116054218.5
006 m o d
007 cr mn ---uuuuu
008 250605s2022 xx obm 000 0 eng d
020 $a 9798209907442
035 $a (MiAaPQ)AAI28964767
035 $a AAI28964767
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Akhshik, Masoud. $3 1464831
245 1 0 $a Life Cycle Assessment Based Greenhouse Gas Emission Reductions, Cross Country Analysis and Algorithm Aided Prediction for Lightweighted Composite Auto Parts.
264 0 $c 2022
300 $a 1 online resource (162 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
500 $a Advisor: Sain, Mohini ; Tjong, Jimi.
502 $a Thesis (Ph.D.)--University of Toronto (Canada), 2022.
504 $a Includes bibliographical references
520 $a Algorithms made our life simpler and enables Machine to learn and predict and seems like a promising revolutionary science of the future. Even though this has been a hotspot in many aspects of science; there are some areas still behind. One of these areas is the Environmental and lifecycle assessments (LCA). The main problem with these areas is that the collected data are not comparable as every research has its own unique details even though LCA has a standard set of guidelines to perform. The second main problem is that we have very limited data avail-able for machines to be able to use in these areas. Here in this thesis, several normalization methods and a coefficient were used to make these data comparable. Also, because we had lim-ited data available, we performed several LCA studies to generate more data to be able to use it in Machine learning algorithms for the purpose of testing, validation and training. One of the advantages of having limited data here was that it enabled us to implement several machine learning algorithms and methods to compare their performances. The results of this study created a powerful tool that can help researchers, original equipment manufacturers, policymakers and automotive companies to make better environmental decisions prior to any design stages just by knowing the percentage of lightweighted automotive parts. This tool is specifically created to work with lightweighting that is resulted from replacing a glass fibre automotive part with natural fibre-reinforced composites. Also, with the developed coefficient of countries, one can transfer the LCA data from one country to another by using a simple equation. These coefficients were developed by using the countries posted total primary energy supply and electricity grid mix. By using these tools, we will have a better understanding of our emissions prior to any design with relatively high accuracy.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Environmental engineering. $3 557376
650 4 $a Environmental science. $3 1179128
650 4 $a Environmental studies. $3 1180247
653 $a Automotive parts
653 $a Cross country
653 $a Greenhouse gases
653 $a Lifecycle assessments
653 $a Machine learning
655 7 $a Electronic books. $2 local $3 554714
690 $a 0477
690 $a 0768
690 $a 0775
710 2 $a University of Toronto (Canada). $b Forestry. $3 1180172
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 83-09B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28964767 $z click for full text (PQDT)