Comparative Analysis of Response Surface Methodology (Rsm) Method and Taguchi Method: Optimization Hydraulic Ram Pump Performance

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This paper compares Response Surface Methodology (RSM) and the Taguchi method to optimize hydraulic ram pump performance, using experimental designs with three input variables (input height, input length, and vacuum tube length) and discharge as the response. RSM used 20 experiments and identified optimal settings at input height 3 m, input length 12 m, and vacuum tube length 120 cm, whereas Taguchi used 9 experiments with optimal settings at input height 3 m, input length 6 m, and vacuum tube length 120 cm, with RSM producing a more complex interaction-heavy model and Taguchi yielding a simpler equation. In ANOVA results, input height was reported as the most significant variable for optimizing discharge. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract Hydraulic ram pumps offer an energy-efficient solution for water lifting, crucial in rural areas with limited electricity access. Comparative analysis using Response Surface Methodology (RSM) and Taguchi method reveals distinct experimental designs and optimization outcomes. RSM entails 20 experiments, yielding optimal points at Input Height (3 m), Input Length (12 m), and Vacuum Tube Length (120 cm). In contrast, Taguchi employs 9 experiments, with optimal points at Input Height (3 m), Input Length (6 m), and Vacuum Tube Lenght (120 cm). For the equation model, the RSM method shows a complex mathematical equation involving interactions between variables, while the Taguchi method provides a simpler equation. As for the most optimal variable when viewed from the Significant value in both methods in the ANOVA table, it is found that the input height variable is the most significant variable in optimising the response (discharge). A better understanding of these two methods can help the selection of appropriate methods for specific situations, strengthen the understanding of hydram pump performance, and contribute to the development of more efficient and sustainable hydram pump technology.
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Comparative Analysis of Response Surface Methodology (Rsm) Method and Taguchi Method: Optimization Hydraulic Ram Pump Performance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Analysis of Response Surface Methodology (Rsm) Method and Taguchi Method: Optimization Hydraulic Ram Pump Performance Chahyani Romelin, Zahedi Zahedi, Badai Charamsar Nusantara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4700608/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Sep, 2024 Read the published version in Operations Research Forum → Version 1 posted 9 You are reading this latest preprint version Abstract Hydraulic ram pumps offer an energy-efficient solution for water lifting, crucial in rural areas with limited electricity access. Comparative analysis using Response Surface Methodology (RSM) and Taguchi method reveals distinct experimental designs and optimization outcomes. RSM entails 20 experiments, yielding optimal points at Input Height (3 m), Input Length (12 m), and Vacuum Tube Length (120 cm). In contrast, Taguchi employs 9 experiments, with optimal points at Input Height (3 m), Input Length (6 m), and Vacuum Tube Lenght (120 cm). For the equation model, the RSM method shows a complex mathematical equation involving interactions between variables, while the Taguchi method provides a simpler equation. As for the most optimal variable when viewed from the Significant value in both methods in the ANOVA table, it is found that the input height variable is the most significant variable in optimising the response (discharge). A better understanding of these two methods can help the selection of appropriate methods for specific situations, strengthen the understanding of hydram pump performance, and contribute to the development of more efficient and sustainable hydram pump technology. Hydraulic Ram Pump RSM Taguchi Irrigation Technology Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Sep, 2024 Read the published version in Operations Research Forum → Version 1 posted Editorial decision: Revision requested 30 Jul, 2024 Reviews received at journal 29 Jul, 2024 Reviews received at journal 29 Jul, 2024 Reviewers agreed at journal 18 Jul, 2024 Reviewers agreed at journal 18 Jul, 2024 Reviewers invited by journal 17 Jul, 2024 Editor assigned by journal 08 Jul, 2024 Submission checks completed at journal 08 Jul, 2024 First submitted to journal 07 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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