Response surface methodology (RSM) for predicting and optimizing the pressure drop in submerged multi-jet electrolyte flow system
Yara Sami H ALGHANNAM, Lujain Abdullah A ALTEWAIRQI, Feroz SHAIK, Faizan AHMED, Nayeemuddin MOHAMMED, Ratna Sunil BURADAGUNTA
Abstract. Submerged multi-jet electrolyte flow systems require efficient design and operation to support a variety of industrial operations, including electrochemical machining and electroplating. In this work, we give a thorough analysis of the optimization and prediction of pressure drop in such systems through the use of Response Surface Methodology (RSM). First, under various operating conditions, experimental data are gathered from a prototype submerged multi-jet electrolyte flow configuration. Further, empirical models illustrating the connection between process factors and pressure drop are produced using the Response Surface Methodology. There is less than 0.2 discrepancy between the adjusted R² of 0.9988 and the expected R² of 0.9986, indicating a fair agreement in the pressure drop measurement. In submerged multi-jet electrolyte flow systems, the suggested methodology provides a methodical and effective way to forecast and optimize pressure drop. This allows for improved process performance and efficiency in industrial applications.
Keywords
Response Surface Methodology, Multi Jet Electrolyte Flow, Pressure Drop, ANNOVA, Optimization
Published online 2/25/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Yara Sami H ALGHANNAM, Lujain Abdullah A ALTEWAIRQI, Feroz SHAIK, Faizan AHMED, Nayeemuddin MOHAMMED, Ratna Sunil BURADAGUNTA, Response surface methodology (RSM) for predicting and optimizing the pressure drop in submerged multi-jet electrolyte flow system, Materials Research Proceedings, Vol. 48, pp 697-706, 2025
DOI: https://doi.org/10.21741/9781644903414-76
The article was published as article 76 of the book Civil and Environmental Engineering for Resilient, Smart and Sustainable Solutions
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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