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Yes / The deodorization of the refined palm oil process is simulated here using ASPEN HYSYS. In the absence of a library molecular distillation (MD) process in ASPEN HYSYS, first, a single flash vessel is considered to represent a falling film MD process which is simulated for a binary system taken from the literature and the model predictions are compared with the published work based on ASPEN PLUS and DISMOL. Second, the developed MD process is extended to simulate the deodorization process. Parameter estimation technique is used to estimate the Antoine’s parameters based on literature data to calculate the pure component vapor pressure. The model predictions are then validated against the patented results of refining edible oil rich in natural carotenes and vitamin E and simulation results were found to be in good agreement, within a 2% error of the patented results. Third, Response Surface Methodology (RSM) is employed to develop non-linear second-order polynomial equations based model for the deodorization process and the effects of various operating parameters on the performance of the process are studied. Finally, an optimization framework is developed to maximize the concentration of beta-carotene, tocopherol and free fatty acid while optimizing the feed flow rate, temperature and pressure subject to process constrains. The optimum results of feed flow rate, temperature, and pressure were determined as 1291 kg/h, 147 C and 0.0007 kPa respectively, and the concentration responses of beta- carotene, tocopherol and free fatty acid were found to be 0.000575, 0.000937 and 0.999840 respectively. / Prince of Songkla University, Songkhla, Thailand for providing financial support (Grant code: PSU2554-022)

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In robust parameter design it is often possible to set the levels of the controllable factors to produce a zero gradient for the transmission of variability from the noise variables. If the number of control variables is greater than the number of noise variables, a continuum of zero-gradient solutions exists. This situation is useful as it provides the experimenter with multiple conditions under which to configure a zero gradient for noise variable transmission. However, this situation requires a confidence region for the multiple-solution factor levels that provides proper simultaneous coverage. This requirement has not been previously recognized in the literature. In the case where the number of control variables is greater than the number of noise variables, we show how to construct critical values needed to maintain the simultaneous coverage rate. Two examples are provided as a demonstration of the practical need to adjust the critical values for simultaneous coverage. The zero-gradient confidence region only focuses on the variance, and there are in fact many such situations in which focus is or could be placed entirely on the process variance. In the situation where both mean and variance need to be considered, a general confidence region in control variables is developed by minimizing weighted mean square error. This general method is applicable to many situations including mixture experiments which have an inherit constraint on the control factors. It also gives the user the flexibility to put different weights on the mean and variance parts for simultaneous optimization. It turns out that the same computational algorithm can be used to compute the dual confidence region in both control factors and the response variable. / Statistics

Yes / Reverse Osmosis (RO) process can be considered as one of the intensively used pioneering equipment for reusing wastewater of several applications. The recent study presented the development of an accurate model for predicting the dimethylphenol removal from wastewater via RO process. The Response Surface Methodology (RSM) was applied to carry out this challenge based on actual experimental data collected from the literature. The independent variables considered are the inlet pressure (5.83-13.58) atm, inlet temperature (29.5-32) ° C, inlet feed flow rate (2.166-2.583) × 10-4 m3/s, and inlet concentration (0.854-8.049) × 10-3 kmol/m3 and the dimethylphenol removal is considered as the response variable. The analysis of variance showed that the inlet temperature and feed flow rate have a negative influence on dimethylphenol removal from wastewater while the inlet pressure and concentration show a positive influence. In this regard, F-value of 240.38 indicates a considerable contribution of the predicted variables of pressure and concentration against the process dimethylphenol rejection. Also, the predicted R2 value of 0.9772 shows the high accuracy of the model. An overall assessment of simulating the performance of RO process against the operating parameters has been systematically demonstrated using the proposed RSM model.

This research contributes to the development of a structural optimization software system designed to support design optimization. The focus of this thesis work is on formulating strategies to obtain accurate solutions and enhance the efficiency of the optimization process, particularly when dealing with large and complex finite element (FE) models, utilizing statistical concepts. A potential avenue explored in this study is the adaptive response surface optimization process. The adaptive response surface optimization method involves the adaptive control of samples selected through the design of experiments and empirical models constructed via the response surface methodology, with the sampling of the design space and empirical model terms dynamically adjusted throughout the optimization progression. The empirical models are constructed with statistically significant terms to maximize the utilization of information from each sample generated using the design of experiments. If the available information is fully utilized by the empirical model and the adaptive response surface optimization process needs to progress further until an optimal solution is identified, additional samples are generated. The methodology is applied to a benchmark bell crank problem, optimizing the bell crank for maximum operational value by simultaneously increasing fatigue life and reducing the overall component cost. This demonstration showcases the structural optimization software's capability to handle both design and manufacturing aspects seamlessly. The approach to solving the structural optimization problem involves constructing a constrained parametric bell crank part in Abaqus/CAE as it facilitates easy manipulation of the geometry. The entire process of geometry generation, meshing, simulation, and output extraction was supported by developing Python scripts. Response surface model building and other statistical analyses are conducted using the JMP statistical software. Nonlinear constrained optimization is executed through the sequential quadratic programming (SLSQP solver) from the SciPy library, allowing optimization on the response surfaces representing the objective function and constraints to identify the optimal solution. The optimal solution is obtained utilizing a small composite design with individual response surface models for the objective function and each constraint, is compared with results from the Abaqus finite element model, and the percentage difference was 0.9% at the optimal design variable values. / Master of Science / Optimization processes, in general, require multiple iterations to converge to the optimal solution. Structural optimization, dealing with large and complex computationally intensive models are typically very time-consuming. To address this challenge, approximations of the actual design space, called response surfaces, are created using the statistical concept known as response surface methodology. Response surfaces are developed by selecting specific regions within the design space and studying them using complex computational models. The results obtained from these computational models are combined with statistical tools to build a response surface that approximately represents the actual design objective function and the associated constraints of the design within the specified design space. In this research, an adaptive approach called adaptive response surface optimization is implemented. In this approach, the regions studied and the response surfaces are dynamically adjusted based on the progression of the optimization process. Such adaptability significantly accelerates the structural optimization process and yields successful results. To illustrate this method, a benchmark problem was solved using the finite element solver Abaqus, the statistical software JMP, and the optimization toolbox from the Scipy library.

The success of any racing car hinges on three key factors: its speed, handling, and reliability. In a highly competitive environment where lap times are extremely tight, even slight variations in components can significantly affect performance and, consequently, lap times. At the heart of a race car's performance lies the upright—a critical component of its suspension system. The upright serves to link the suspension arms to the wheels, effectively transmitting steering and braking forces to the suspension setup. Achieving optimal performance requires finding the right balance between lightweight design and ample stiffness, crucial for maintaining precise steering geometry and overall vehicle dynamics, especially under intense loads. Furthermore, there is a need to explore the system of structural optimization and seamlessly integrate Finite Element (FE) Models into the mathematical optimization process. This thesis explores a technique for parametric structural optimization utilizing finite element analysis and response surfaces to minimize the weight of the upright. Various constraints such as frequency, stress, displacement, and fatigue are taken into consideration during this optimization process. A parametric finite element model of the upright was designed, along with the mathematical formulation of the optimization problem as a nonlinear programming problem, based on the design objectives and suspension geometry. By conducting parameter sensitivity analysis, three design variables were chosen from a pool of five, and response surfaces were constructed to represent the constraints and objective function to be used to solve the optimization problem using Sequential Quadratic Programming (SQP). To streamline the process of parameter sensitivity analysis and response surface development, a Python scripting procedure was employed to automate the finite element job analysis and results extraction. The optimized upright design resulted in overall weight reduction of 25.3% from the maximum weight design of the parameterized upright. / Master of Science / The success of any racing car depends on three key factors: its speed, handling and reliability. In a highly competitive environment where lap times are extremely tight, even slight variations in components can significantly affect performance and consequently, lap times. At the heart of a race car's performance lies the upright—a critical component of its suspension system. The upright serves to link the suspension arms to the wheels, effectively transmitting steering and braking forces to the suspension setup. To achieve the best performance, upright must be as light as possible but it needs to be strong enough to ensure that the car is predictable when turning in a corner or while braking. Additionally, there is a need to explore methods of structural optimization and integrate finite element analysis seamlessly into the optimization process. Finite element analysis (FEA) is the use of part models, simulations, and calculations to predict and understand how an object might behave under certain physical conditions. This thesis examines a technique for optimizing the upright by designing it with numerous adjustable features for testing and then utilizing response surfaces to minimize its weight. Throughout this process, factors such as vibration, stress, deformation, and fatigue are carefully considered. A detailed parametric finite element model of the upright was developed, alongside the formulation of the optimization problem as a nonlinear programming problem, based on the objectives of the design and the geometry of the suspension. Through rigorous testing of parameters for optimization potential, design variables are selected for optimization. Response surfaces were then constructed to represent the constraints and objective function necessary to solve the optimization problem using Sequential Quadratic Programming (SQP). To enhance the efficiency of this process, a Python script was created to handle specific tasks within the finite element solver. This automation streamlined the analysis of the finite element model and the extraction of results. Ultimately, the optimized design of the upright yielded a 25.3% reduction in weight compared to its maximum weight configuration.

Many industrial statisticians employ the techniques of Response Surface Methodology (RSM) to study and optimize products and processes. A second-order Taylor series approximation is commonly utilized to model the data; however, parametric models are not always adequate. In these situations, any degree of model misspecification may result in serious bias of the estimated response. Nonparametric methods have been suggested as an alternative as they can capture structure in the data that a misspecified parametric model cannot. Yet nonparametric fits may be highly variable especially in small sample settings which are common in RSM. Therefore, semiparametric regression techniques are proposed for use in the RSM setting. These methods will be applied to an elementary RSM problem as well as the robust parameter design problem. / Ph. D.

This thesis focuses on practical and quantitative methods for measuring effectiveness in naval ship design. An Overall Measure of Effectiveness (OMOE) model or function is an essential prerequisite for optimization and design trade-off. This effectiveness can be limited to individual ship missions or extend to missions within a task group or larger context. A method is presented that uses the Analytic Hierarchy Process combined with Multi-Attribute Value Theory to build an Overall Measure of Effectiveness and Overall Measure of Risk function to properly rank and approximately measure the relative mission effectiveness and risk of design alternatives, using trained expert opinion to replace complex analysis tools. A validation of this method is achieved through experimentation comparing ships ranked by the method with direct ranking of the ships through war gaming scenarios. The second part of this thesis presents a mathematical ship synthesis model to be used in early concept development stages of the ship design process. Tools to simplify and introduce greater accuracy are described and developed. Response Surface Models and Design of Experiments simplify and speed up the process. Finite element codes such as MAESTRO improve the accuracy of the ship synthesis models which in turn lower costs later in the design process. A case study of an Advanced Logistics Delivery Ship (ALDV) is performed to asses the use of RSM and DOE methods to minimize computation time when using high-fidelity codes early in the naval ship design process. / Master of Science