Optimization analysis using response surface methodology / Conrado D. Obar, Jr.

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Indang, Cavite, 2003. Cavite State University- Main Campus,Description: xvi, 151 pages : illustrations ; 28 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
Subject(s): DDC classification:
  • 519.5  Ob1 2003
Online resources: Production credits:
  • College of Arts and Science (CAS)
Abstract: OBAR, CONRADO JR. mv1APILIS. "Optimization Analysis Using response Surface Methodology ' BS Applied Mathematics. Cavite State University, Indang, Cavite. April 2003- Adviser: Engr. Jaime C). Dilidili. The study entitled "Optimization Analysis Using Response Surface Methodology" was conducted at the Physical Science Department, College of Arts and Sciences, Cavite State University, Indang, Cavite, from January to February 2003 to l) determine an appropriate mathematical and graphical model that best fit the accumulated data; 2) find the optimal settings of the input variables that optimized the yield. The data used in this study were taken from the combination of operating parameters of laboratory vegetable seeder and laboratory conical stripper — harvester for rice of Causaren (2003) and Dilidili (1983), respectively. The independent variables of laboratory vegetable seeder were the forward speed of the trolley, height of seeds in the hopper and sizes of seeds (which is considered as the blocking factor). There were 3 levels of forward speed: l, 2, 3 kph; and three sizes of seed: 5, 20, 35, and 50 mm. The forward speed used was defined as the linear velocity of the trolley and seedbed assembly. The dependent variables were seed planted, seed breaks, machine capacity, seeding efficiency, time, and breaking efficiency of the machine. The input variables for rice stripper-harvester were the motor concave clearance, rotor speed, and trolley forward speed while the response variable were feeder loss, ground loss, stripping loss, unstripped loss, collecting efficiency and stripping efficiency. Response Surface Regression, Stepwise Regression and General Linear Model Procedure of the Statistical Analysis System (SAS) were employed to determine the statistical model that best fit the data collected. Two-dimensional plots were drawn showing the relationship of single variable input and the dependent variable. Three-dimensional were plotted between the response and two factor variables. Interpretation and construction of the series of contours or isoquants was made by transforming the two or more process variables into a new coordinate system, which is the canonical form of the fitted model. Response Surface Methodology (RSM) can be used in optimization analysis in agriculture and industrial experimentation
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Theses / Manuscripts Theses / Manuscripts Ladislao N. Diwa Memorial Library Theses Section Non-fiction 519.5 Ob1 2003 (Browse shelf(Opens below)) Link to resource Room use only SP-2554 00004249

Special Problem (BS Applied Mathematics - - Statistics) Cavite State University.

Includes bibliographical references.

College of Arts and Science (CAS)

OBAR, CONRADO JR. mv1APILIS. "Optimization Analysis Using response
Surface Methodology ' BS Applied Mathematics. Cavite State University, Indang,
Cavite. April 2003- Adviser: Engr. Jaime C). Dilidili.

The study entitled "Optimization Analysis Using Response Surface Methodology" was conducted at the Physical Science Department, College of Arts and Sciences, Cavite State University, Indang, Cavite, from January to February 2003 to l) determine an appropriate mathematical and graphical model that best fit the accumulated data; 2) find the optimal settings of the input variables that optimized the yield.
The data used in this study were taken from the combination of operating parameters of laboratory vegetable seeder and laboratory conical stripper — harvester for rice of Causaren (2003) and Dilidili (1983), respectively. The independent variables of laboratory vegetable seeder were the forward speed of the trolley, height of seeds in the hopper and sizes of seeds (which is considered as the blocking factor). There were 3 levels of forward speed: l, 2, 3 kph; and three sizes of seed: 5, 20, 35, and 50 mm. The forward speed used was defined as the linear velocity of the trolley and seedbed assembly. The dependent variables were seed planted, seed breaks, machine capacity, seeding efficiency, time, and breaking efficiency of the machine. The input variables for rice stripper-harvester were the motor concave clearance, rotor speed, and trolley forward speed while the response variable were feeder loss, ground loss, stripping loss, unstripped loss, collecting efficiency and stripping efficiency.

Response Surface Regression, Stepwise Regression and General Linear Model Procedure of the Statistical Analysis System (SAS) were employed to determine the statistical model that best fit the data collected. Two-dimensional plots were drawn showing the relationship of single variable input and the dependent variable. Three-dimensional were plotted between the response and two factor variables. Interpretation and construction of the series of contours or isoquants was made by transforming the two or more process variables into a new coordinate system, which is the canonical form of the fitted model.
Response Surface Methodology (RSM) can be used in optimization analysis in agriculture and industrial experimentation

Submitted to the University Library 05/13/2003 SP-2554

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