Yes / Polypropylene is one type of plastic that is widely used in our everyday life. This study focuses on the identification and justification of the optimum process parameters for polypropylene production in a novel pilot plant based fluidized bed reactor. This first-of-its-kind statistical modeling with experimental validation for the process parameters of polypropylene production was conducted by applying ANNOVA (Analysis of variance) method to Response Surface Methodology (RSM). Three important process variables i.e., reaction temperature, system pressure and hydrogen percentage were considered as the important input factors for the polypropylene production in the analysis performed. In order to examine the effect of process parameters and their interactions, the ANOVA method was utilized among a range of other statistical diagnostic tools such as the correlation between actual and predicted values, the residuals and predicted response, outlier t plot, 3D response surface and contour analysis plots. The statistical analysis showed that the proposed quadratic model had a good fit with the experimental results. At optimum conditions with temperature of 75 °C, system pressure of 25 bar and hydrogen percentage of 2%, the highest polypropylene production obtained is 5.82% per pass. Hence it is concluded that the developed experimental design and proposed model can be successfully employed with over a 95% confidence level for optimum polypropylene production in a fluidized bed catalytic reactor (FBCR).

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This thesis illustrates analysis of the influence of the non-machining processes on product quality by experimental design and statistical analysis. For the analysis objective / dishwasher production in Arcelik Dishwasher plant is examined. Sheet metal forming processes of dishwasher production constitutes the greatest portion of production cost and using the Pareto analysis technique / four pieces among twenty six pieces are determined to be investigated. These four pieces are the U Sheet, L Sheet, Inner Door and Side Panel of the dishwasher. By the help of the flow diagrams production process of the determined pieces are defined. Brainstorming technique and cause&amp / effect diagrams are used to determine which non-machining process parameters can cause pieces to be scrapped. These parameters are used as control factors in experimental design. Taguchi&amp / #8217 / s L16(215) orthogonal array, Taguchi&amp / #8217 / s L16(215) orthogonal array using S/N transformation and 28-4 fractional factorial design are used on purpose. With repetitions and confirmation experiments the effective parameters are determined and optimum level of these parameters are defined for the improvements on scrap quantity and quality of production.

This thesis illustrates parameter optimization of compressive strength, flexural strength and impact resistance of steel fiber reinforced high strength concrete (SFRHSC) by statistical design and analysis of experiments. Among several factors affecting the compressive strength, flexural strength and impact resistance of SFRHSC, five parameters that maximize all of the responses have been chosen as the most important ones as age of testing, binder type, binder amount, curing type and steel fiber volume fraction. Taguchi and regression analysis techniques have been used to evaluate L27(313) Taguchi&amp / #65533 / s orthogonal array and 3421 full factorial experimental design results. Signal to noise ratio transformation and ANOVA have been applied to the results of experiments in Taguchi analysis. Response surface methodology has been employed to optimize the best regression model selected for all the three responses. In this study Charpy Impact Test, which is a different kind of impact test, have been applied to SFRHSC for the first time. The mean of compressive strength, flexural strength and impact resistance have been observed as around 125 MPa, 14.5 MPa and 9.5 kgf.m respectively which are very close to the desired values. Moreover, this study is unique in the sense that the derived models enable the identification of underlying primary factors and their interactions that influence the modeled responses of steel fiber reinforced high strength concrete.

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