IDEAS home Printed from https://ideas.repec.org/h/ito/pchaps/129861.html
   My bibliography  Save this book chapter

Design of Experiments Applied to Industrial Process

In: Statistical Approaches With Emphasis on Design of Experiments Applied to Chemical Processes

Author

Listed:
  • Neelesh Kumar Sahu
  • Neelesh Kumar Sahu
  • Atul Andhare

Abstract

Response optimization and exploration are the challenging task in front of experimenter. The cause and effect of input variables on the responses can be found out after doing experiments in proper sequence. Generally relationship between response of interest y and predictor variables x1, x2, x3, ... xk is formed after carefully designing of experimentation. For examples y might be biodiesel production from crude 'Mahua' and x1, x2 and x3 might be reaction temperature, reaction time and the catalyst feed rate in the process. In the present book chapter, design of experiment is discussed based on predictor variables for conducting experiments with the aim of building relationship between response and variables. Subsequently a case study is also discussed for demonstration of design of experiments for predicting surface roughness in the machining of titanium alloys based on response surface methodology.

Suggested Citation

  • Neelesh Kumar Sahu & Neelesh Kumar Sahu & Atul Andhare, 2018. "Design of Experiments Applied to Industrial Process," Chapters, in: Valter Silva (ed.), Statistical Approaches With Emphasis on Design of Experiments Applied to Chemical Processes, IntechOpen.
    • Handle: RePEc:ito:pchaps:129861
    DOI: 10.5772/intechopen.73558
    as

    Download full text from publisher

    File URL: https://www.intechopen.com/chapters/59114
    Download Restriction: no
    File URL: https://libkey.io/10.5772/intechopen.73558?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
    ---><---

    More about this item

    Keywords

    design of experiments; response surface methodology; optimization; ANOVA;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

    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:ito:pchaps:129861. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Slobodan Momcilovic (email available below). General contact details of provider: http://www.intechopen.com .

    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.