Predictive modeling for mitigating fugitive emissions in industrial valve seal stacks: a comparative analysis of configuration efficacies (2024)

Skip Abstract Section

1. 1. Harrison RMHester REVolatile organic compounds in the atmosphere1995CambridgeRoyal Society of Chemistry10.1039/9781847552310Google ScholarCross Ref
2. 2. Pansegrau LSchembecker GBramsiepe CA model to characterize and predict fugitive emissions from flange jointsChem Eng Technol2014377120510.1002/ceat.201300753Google ScholarCross Ref
3. 3. Zhang LXu MChen HLi YChen SGlobalization, green economy and environmental challenges: state of the art review for practical implicationsFront Environ Sci20221587027110.3389/fenvs.2022.870271Google ScholarCross Ref
4. 4. Manisalidis IStavropoulou EStavropoulos ABezirtzoglou EEnvironmental and health impacts of air pollution: a reviewFront Public Health202020814Google Scholar
5. 5. Moores FCClimate change and air pollution: exploring the synergies and potential for mitigation in industrializing countriesSustainability20091435410.3390/su1010043Google ScholarCross Ref
6. 6. Rajabi HMosleh MHMandal PLea-Langton ASedighi MEmissions of volatile organic compounds from crude oil processing–Global emission inventory and environmental releaseSci Total Environ202072713865410.1016/j.scitotenv.2020.138654Google ScholarCross Ref
7. 7. Pellizzari E D, Perritt K, Hartwell T, Michael L, Sparacino C (1987). Total exposure assessment methodology (TEAM) study: Elizabeth and Bayonne, New Jersey, Devils Lake, North Dakota and Greensboro, North Carolina. Research Triangle Inst, Research Triangle Park, NC (USA), parts 1 and 2. Final report.Google Scholar
8. 8. Zhang XGao BCreamer AECao CLi YAdsorption of VOCs onto engineered carbon materials: a reviewJ Hazard Mater201733810212310.1016/j.jhazmat.2017.05.013Google ScholarCross Ref
9. 9. Sotoodeh KPrevention of valve fugitive emissions in the oil and gas industry2021ElsevierGulf Professional PublishingGoogle Scholar
10. 10. Sotoodeh KA review of valve stem sealing to prevent leakage from the valve and its effect on valve operationJ Fail Anal Prevent20202191610.1007/s11668-020-01050-1Google ScholarCross Ref
11. 11. Nwaoha C (2010) Controlling fugitive emissions in Nigeria's oil and gas industry: proper sealing device selection a panacea. Society of petroleum engineers-nigeria annual international conference and exhibition 2010, NAICE, Calabar, Nigeria. doi DOI: https://doi.org/10.2118/140675-MSGoogle ScholarCross Ref
12. 12. Flitney R (2014) Seals and sealing handbook. Sixth edition Ed. iChemE. Amsterdam: Butterworth-Heinemann, an imprint of Elsevier. xiii, 633 pages.Google Scholar
13. 13. [Online]. Available: https://www.callapg.com/blog-getting-to-know-materials-aflas.Google Scholar
14. 14. Raihan AMuhtasim DAPavel MIFaruk ORahman MAn econometric analysis of the potential emission reduction components in IndonesiaCleaner Prod Lett202231000810.1016/j.clpl.2022.100008Google ScholarCross Ref
15. 15. Tan J-SShen S-LZhou AWang Z-NLyu H-MLaboratory evaluation of long-term sealing behaviors of two water-swelling materials for shield tunnel gasketConstr Build Mater20202491187110.1016/j.conbuildmat.2020.118711Google ScholarCross Ref
16. 16. Im HJeoung SMechanical aging test and sealing performance of thermoplastic vulcanizate as sealing gasket in automotive fuel cell applicationsPolymers187220231510.3390/polym15081872Google ScholarCross Ref
17. 17. Onat AA review of fugitive emissionsSeal Technol2006107910.1016/S1350-4789(06)71411-8Google ScholarCross Ref
18. 18. Ilhan ITuran DGibson Iten Klooster RUnderstanding the factors affecting the seal integrity in heat-sealed flexible food packages: a reviewPackag Technol Sci202134632133710.1002/pts.2564Google ScholarCross Ref
19. 19. International Organization for Standardization (2015) Industrial valves — Measurement, test and qualification procedures for fugitive emissions—Part 1: Classification system and qualification procedures for type testing of valves (ISO Standard No. 15848–1). Retrieved October 2019, from http://www.iso.org/iso/catalogue_detailGoogle Scholar
20. 20. Chamberlain, P. (2014, May 8). Are we running out of helium. Retrieved November 2019, from leak testing specialists Inc: http://www.leaktestingspec.com/NewsEvents/Details?id=1259Google Scholar
21. 21. Su MZhong QPeng HLi SSelected machine learning approaches for predicting the interfacial bond strength between FRPs and concreteConstr Build Mater202127012145610.1016/j.conbuildmat.2020.121456Google ScholarCross Ref
22. 22. [Online]. Available: https://hdl.handle.net/11244/325365.Google Scholar
23. 23. Kia SNambiar MKThé JGharabaghi BAliabadi AAMachine learning to predict area fugitive emission fluxes of ghgs from open-pit minesAtmosphere20221321010.3390/atmos13020210Google ScholarCross Ref
24. 24. Cao YMiraba SRafiei SGhabussi ABokaei FBaharom SHaramipour PAssilzadeh HEconomic application of structural health monitoring and internet of things in efficiency of building information modelingSmart Struct Syst202026555957310.12989/sss.2020.26.5.559Google ScholarCross Ref
25. 25. Peng DChen SDarabi Ret al.Prediction of the bending and out-of-plane loading effects on formability response of the steel sheetsArchiv Civ Mech Eng2021217410.1007/s43452-021-00227-1Google ScholarCross Ref
26. 26. Zhang WZhang MWu SLiu FA complex path model for low-carbon sustainable development of enterprise based on system dynamicsJ Cleaner Prod202132112893410.1016/j.jclepro.2021.128934Google ScholarCross Ref
27. 27. Tufail SRiggs HTariq MSarwat AIAdvancements and challenges in machine learning: a comprehensive review of models, libraries, applications, and algorithmsElectronics202312178910.3390/electronics12081789Google ScholarCross Ref
28. 28. Jiang TGradus JLRosellini AJSupervised machine learning: a brief primerBehav Ther202051567568710.1016/j.beth.2020.05.002Google ScholarCross Ref
29. 29. Schneider AHommel GBlettner MLinear regression analysisDtsch Arztebl Int20101074477678210.3238/arztebl.2010.0776Google ScholarCross Ref
30. 30. Michelle C, Kathryn EH, (2023) Chapter 27 - regression, translational radiation oncology, Handbook for designing and conducting clinical and translational research. pp 145–152. doi DOI: https://doi.org/10.1016/B978-0-323-88423-5.00076-5Google ScholarCross Ref
31. 31. Kumar VKedam NSharma KVMehta DJCaloiero TAdvanced machine learning techniques to improve hydrological prediction: a comparative analysis of streamflow prediction modelsWater202315257210.3390/w15142572Google ScholarCross Ref
32. 32. Debopriya GJavier CEnriched random forest for high dimensional genomic dataIEEE/ACM Trans Computat Biol Bioinform2022195Google Scholar
33. 33. Kazeminia MBouzid AEvaluation of leakage through graphite-based compression packing ringsASME J Pressure Vessel Technol2016139101160210.1115/1.4033937Google ScholarCross Ref
34. 34. Clarke V (2019) Clarke valve cements leadership in fugitive emissions reduction with ISO 15848–1 certification. Retrieved from https://www.clarkevalve.com/clarke-valve-achieves-iso-15848-1-certification/Google Scholar
35. 35. Clarke V Fugitive emissions technology. Retrieved from https://www.clarkevalve.com/fugitive-emissions-technology/Google Scholar

• Numerical Modeling of Soot and NOx Emissions of a Diesel/Methanol Dual Fuel Engine

  CDCIEM '11: Proceedings of the 2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring

  In this work, a modeling study was carried out using the KIVA-3V code with the Zeldovich thermal NOx model, the improved soot formation model and the combustion model for a diesel/methanol dual fuel engine. The effects of ratios (10%,20% and 30%) of ...

  Read More

• Emissions predictive modelling by investigating various neural network models

  Highlights ANN model accurately predicts exhaust emissions with minimal inputs. ANN optimization-layer-by-layer model proves to be the most accurate. Models act as virtual emission sensor without the need of additional equipment. A single ECU is just ...

  Read More

• Prediction on Net Carbon Emissions of Yunnan under Emissions and Forest Carbon Sinks

  CDCIEM '11: Proceedings of the 2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring

  This paper predicts Net Carbon Emissions of Yunnan form 2007 to 2050 through energy consumption carbon emissions based on economic growth model and forest carbon sinks based on CO2FIX model. Further, we analyze the influencing factors of carbon ...

  Read More