IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v19y1999i2p249-259.html
   My bibliography  Save this article

Reliability Analysis for Hazardous Waste Treatment Processes

Author

Listed:
  • Robert D. Water
  • Frank L. Parker

Abstract

The reliability of a treatment process is addressed in terms of achieving a regulatory effluent concentration standard and the design safety factors associated with the treatment process. This methodology was then appliedto two aqueous hazardous waste treatment processes: packed tower aeration and activated sludge (aerobic) biological treatment. The designs achieving 95 percent reliability were compared with those designs based on conventional practice to determine their patterns of conservatism. Scoping‐level treatment costs were also related to reliability levels for these treatment processes. The results indicate that the reliability levels for the physicalkhemical treatment process (packed tower aeration) based on the deterministic safety factors range from 80 percent to over 99 percent, whereas those for the biological treatment process range from near 0 percent to over 99 percent, depending on the compound evaluated. Increases in reliability per unit increase in treatment costs are most pronounced at lower reliability levels(less than about 80 percent) than at the higher reliability levels (greaterthan 90 percent, indicating a point of diminishing returns. Additional research focused on process parameters that presently contain large uncertainties may reduce those uncertainties, with attending increases in the reliability levels of the treatment processes.

Suggested Citation

  • Robert D. Water & Frank L. Parker, 1999. "Reliability Analysis for Hazardous Waste Treatment Processes," Risk Analysis, John Wiley & Sons, vol. 19(2), pages 249-259, April.
  • Handle: RePEc:wly:riskan:v:19:y:1999:i:2:p:249-259
    DOI: 10.1111/j.1539-6924.1999.tb00403.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1539-6924.1999.tb00403.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1539-6924.1999.tb00403.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. F. Owen Hoffman & Jana S. Hammonds, 1994. "Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability," Risk Analysis, John Wiley & Sons, vol. 14(5), pages 707-712, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Yuekuan & Zheng, Siqian, 2020. "Uncertainty study on thermal and energy performances of a deterministic parameters based optimal aerogel glazing system using machine-learning method," Energy, Elsevier, vol. 193(C).
    2. T. Chatzivasileiadis & F. Estrada & M. W. Hofkes & R. S. J. Tol, 2019. "Systematic Sensitivity Analysis of the Full Economic Impacts of Sea Level Rise," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1183-1217, March.
    3. J. C. Helton & F. J. Davis, 2002. "Illustration of Sampling‐Based Methods for Uncertainty and Sensitivity Analysis," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 591-622, June.
    4. Jingwen Song & Zhenzhou Lu & Pengfei Wei & Yanping Wang, 2015. "Global sensitivity analysis for model with random inputs characterized by probability-box," Journal of Risk and Reliability, , vol. 229(3), pages 237-253, June.
    5. Helton, Jon C. & Brooks, Dusty M. & Sallaberry, Cédric J., 2020. "Property values associated with the failure of individual links in a system with multiple weak and strong links," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    6. Junyu Zheng & H. Christopher Frey, 2005. "Quantitative Analysis of Variability and Uncertainty with Known Measurement Error: Methodology and Case Study," Risk Analysis, John Wiley & Sons, vol. 25(3), pages 663-675, June.
    7. Liu, Yushan & Li, Luyi & Zhao, Sihan & Song, Shufang, 2021. "A global surrogate model technique based on principal component analysis and Kriging for uncertainty propagation of dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    8. Adam T Biggs & Kyle A Pettijohn, 2021. "Prospect theory and its implications for adversarial decision-making," The Journal of Defense Modeling and Simulation, , vol. 18(2), pages 125-134, April.
    9. Natalie Commeau & Marie Cornu & Isabelle Albert & Jean‐Baptiste Denis & Eric Parent, 2012. "Hierarchical Bayesian Models to Assess Between‐ and Within‐Batch Variability of Pathogen Contamination in Food," Risk Analysis, John Wiley & Sons, vol. 32(3), pages 395-415, March.
    10. Bas Groot Koerkamp & Theo Stijnen & Milton C. Weinstein & M. G. Myriam Hunink, 2011. "The Combined Analysis of Uncertainty and Patient Heterogeneity in Medical Decision Models," Medical Decision Making, , vol. 31(4), pages 650-661, July.
    11. Pieter Busschaert & Annemie H. Geeraerd & Mieke Uyttendaele & Jan F. Van Impe, 2011. "Sensitivity Analysis of a Two‐Dimensional Quantitative Microbiological Risk Assessment: Keeping Variability and Uncertainty Separated," Risk Analysis, John Wiley & Sons, vol. 31(8), pages 1295-1307, August.
    12. Helton, Jon C. & Brooks, Dusty M. & Sallaberry, Cédric J., 2020. "Margins associated with loss of assured safety for systems with multiple weak links and strong links," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    13. Thomas Ying‐Jeh Chen & Valerie Nicole Washington & Terje Aven & Seth David Guikema, 2020. "Review and Evaluation of the J100‐10 Risk and Resilience Management Standard for Water and Wastewater Systems," Risk Analysis, John Wiley & Sons, vol. 40(3), pages 608-623, March.
    14. Sarazin, Gabriel & Morio, Jérôme & Lagnoux, Agnès & Balesdent, Mathieu & Brevault, Loïc, 2021. "Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    15. Junyu Zheng & H. Christopher Frey, 2004. "Quantification of Variability and Uncertainty Using Mixture Distributions: Evaluation of Sample Size, Mixing Weights, and Separation Between Components," Risk Analysis, John Wiley & Sons, vol. 24(3), pages 553-571, June.
    16. S. N. Rai & D. Krewski, 1998. "Uncertainty and Variability Analysis in Multiplicative Risk Models," Risk Analysis, John Wiley & Sons, vol. 18(1), pages 37-45, February.
    17. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    18. Emanuele Borgonovo, 2008. "Epistemic Uncertainty in the Ranking and Categorization of Probabilistic Safety Assessment Model Elements: Issues and Findings," Risk Analysis, John Wiley & Sons, vol. 28(4), pages 983-1001, August.
    19. Amirhossein Mokhtari & H. Christopher Frey, 2005. "Sensitivity Analysis of a Two‐Dimensional Probabilistic Risk Assessment Model Using Analysis of Variance," Risk Analysis, John Wiley & Sons, vol. 25(6), pages 1511-1529, December.
    20. Paul S. Price & Cynthia L. Curry & Philip E. Goodrum & Michael N. Gray & Jane I. McCrodden & Natalie W. Harrington & Heather Carlson‐Lynch & Russell E. Keenan, 1996. "Monte Carlo Modeling of Time‐Dependent Exposures Using a Microexposure Event Approach," Risk Analysis, John Wiley & Sons, vol. 16(3), pages 339-348, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:riskan:v:19:y:1999:i:2:p:249-259. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1539-6924 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.