• Computers and Concrete
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• v.9 no.2
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• pp.119-131
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• 2012

Optimum packing of aggregate is an important aspect of mixture design, since porosity may be reduced and strength improved. It may also cause a reduction in paste content and is thus of economic relevance too. Several mathematic packing models have been developed in the literature for optimization of mixture design. However in this study, numerical simulation will be used as the main tool for this purpose. A basic, simple theoretical model is used for approximate assessment of mixture optimization. Calculation and simulation will start from a bimodal mixture that is based on the mono-sized packing experiences. Tri-modal and multi-sized particle packing will then be discussed to find the optimum mixture. This study will demonstrate that computer simulation is a good alternative for mixture design and optimization when appropriate particle shapes are selected. Although primarily focusing on aggregate, optimization of blends of Portland cement and mineral admixtures could basically be approached in a similar way.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.7
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• pp.28-34
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• 2006

In general engineering problems, the purpose of an optimization is to get optimal design variables. It is the same problem to fix the total amount of the design variables and to judge the optimal mixing proportions of the design variables. That is to say, we can recompose the engineering problems in the concepts of the mixture amount experiments. The goal of mixture amount method is to get the response surfaces of varying both the mixing proportion of component and the total amount of the mixture. The solution of the aircraft fuselage optimization problem is obtained by the mixture amount method and genetic algorithm. In this study, it is shown that the mixture amount method can be utilized for the aircraft structural optimization problem. Also, this method in this study can be applied for the optimization problems over 12 design variables which is impossible for D-optimal design.

• Korean Chemical Engineering Research
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• v.56 no.1
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• pp.29-41
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• 2018

Separating a mixture having an azeotrope or low relative volatility with single distillation column is difficult. Separating water-acetic acid mixture and water-ethanol mixture with a distillation column consumes a lot of energy. Pervaporation membrane can be used to separate the mixture in the concentration region where separation is difficult with distillation. We simulated a distillation-membrane hybrid process where membrane is located on the head of the distillation column for efficient separation of water-acetic acid and water-ethanol mixture. Permeability data were obtained from experiments and literature. We formulated an optimization problem for the process with total annual cost (TAC) as an objective function and major design variables as optimization variables. Major optimization variable affecting TAC of the hybrid process was shown to be distillate concentration. We also suggested a simplified optimization procedure to get a close-to-optimal solution.

• The Journal of the Acoustical Society of Korea
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• v.29 no.1
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• pp.56-61
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• 2010

This paper describes optimization methods of acoustic models in HMM-based continuous speech recognition. Most of the conventional speech recognition systems use the same number of Gaussian mixture components for each HMM state. However, since the number of data samples available for each state is different from each other, it is possible to reduce the overall number of model parameters and the computational cost at the decoding step by optimizing the number of Gaussian mixture components. In this study, we introduced the Gaussian mixture weight term at the merging stage of Gaussian components in the minimum description length (MDL) based acoustic modeling optimization. Experimental results showed that the proposed method can obtain better ASR accuracy than the previous optimization method which does not consider the Gaussian mixture weight term.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.3
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• pp.26-38
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• 2020

Mixture design of self-compacting concrete is a typical multi-criteria decision making problem and conventional mixture designs are based on the low level engineering method like trials and errors through iteration method to satisfy the various requirements. This study concerns with performing the straightforward multiobjective design optimization of economic SCC mixture considering relative importances of the various requirements and cost-effectives of SCC. Total five requirements of 28day compressive strength, filling ability, segregation stability, material cost and mass were taken into consideration to prepare the objective function to be formulated in form of the weighted-multiobjective mixture design optimization problem. Economic SCC mixture computational design can be given in a rational way which considering material costs and the relative importances of the requiremets and from the result of this study it is expected that the development of SCC mixtue computational design and the consequent univeral concrete material design optimization methodology can be advanced.

• Journal of Industrial Technology
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• v.22 no.A
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• pp.83-87
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• 2002

Many industrial products such as paints, ink and adhesives are composed of the ingredients of a mixture. In mixture experiments, the characteristics of quality(response) depends only on the proportions of the ingredients and does not depend on the total amount of the mixture. This article discusses the constrained mixture experimental design, the data analysis, and the optimum formulation of ingredients based on the two quality characteristics - taste and flavor. It IS shown that efficient designs can be constructed from D-optimal criterion. Special cubic models were selected as the final mixture response surfaces for both reponses. The desirability function was used for the optimization of the two responses.

• Computers and Concrete
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• v.17 no.5
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• pp.629-638
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• 2016

Optimization of the concrete mixture design is a process of search for a mixture for which the sum of the cost of the ingredients is the lowest, yet satisfying the required performance of concrete. In this study, a statistical model was carried out to model a cost effective optimal mix proportioning of high strength self-compacting concrete (HSSCC) using the Response Surface Methodology (RSM). The effect of five key mixture parameters such as water-binder ratio, cement content, fine aggregate percentage, fly ash content and superplasticizer content on the properties and performance of HSSCC like compressive strength, passing ability, segregation resistance and manufacturing cost were investigated. To demonstrate the responses of model in quadratic manner Central Composite Design (CCD) was chosen. The statistical model showed the adjusted correlation coefficient R2adj values were 92.55%, 93.49%, 92.33%, and 100% for each performance which establish the adequacy of the model. The optimum combination was determined to be $439.4kg/m^3$ cement content, 35.5% W/B ratio, 50.0% fine aggregate, $49.85kg/m^3$ fly ash, and $7.76kg/m^3$ superplasticizer within the interest region using desirability function. Finally, it is concluded that multiobjective optimization method based on desirability function of the proposed response model offers an efficient approach regarding the HSSCC mixture optimization.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.177-182
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• 2021

Generally, the linear programming (LP) with O(n⁴) time complexity is applied to mixture optimization problem that can be produce the given ingredients grade product with minimum cost from mixture of various raw materials. This paper suggests heuristic algorithm with O(n log n) time complexity to obtain the solution of this problem. The proposed algorithm meets the content range of the components required by the alloy steel plate while obtaining the minimum raw material cost, decides the quantity of raw material that is satisfied with ingredients grade for ascending order of unit cost. Although the proposed algorithm applies simple decision technique with O(n log n) time complexity, it can be obtains same solution as or more than optimization technique of linear programing.

• Food Engineering Progress
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• v.15 no.4
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• pp.297-304
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• 2011

Ramen flour formulation was optimized by applying a mixture experimental design. In the optimization, the overall palatability (OP) of cooked ramen and the rheological properties of selected dough were maximized or minimized. Blended ratios of the ingredients such as Dark Northern Spring (DNS), Hard Red Winter (HRW), and Soft White (SW) were designed on a simplex-lattice. Dough rheological properties were measured by Rapid Visco Analyser (RVA), Farinograph, and Extensograph, and the overall palatability by sensory evaluation. Several principal dough rheological properties such as RVA peak viscosity (PV), Farinograph development time (DT), and Extensograph resistance/extensibility after 45 min (R/E 45 min) were selected to influence the overall palatability by canonical correlation analysis (CCA). Goals of the optimization were given as OP maximized, PV maximized, DT minimized, and R/E at 45 min maximized. The optimization results were found to be DNS 33.3%, HRW 33.3%, and SW 33.3% with OP, 5.825; PV, 587.9 cP; DT, 3.1 min; R/E at 45 min, 2.339 BU/mm.

• Advances in concrete construction
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• v.11 no.2
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• pp.99-109
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• 2021

Slag blended concrete is widely used as a mineral admixture in the modern concrete industry. This study shows an optimization process that determines the optimal mixture of air-entrained slag blended concrete considering carbonation durability, frost durability, CO2 emission, and materials cost. First, the aim of optimization is set as total cost, which equals material cost plus CO2 emission cost. The constraints of optimization consist of strength, workability, carbonation durability with climate change, frost durability, range of components and component ratio, and absolute volume. A genetic algorithm is used to determine optimal mixtures considering aim function and various constraints. Second, mixture design examples are shown considering four different cases, namely, mixtures without considering carbonation (Case 1), mixtures considering carbonation (Case 2), mixtures considering carbonation coupled with climate change (Case 3), and mixtures of high strength concrete (Case 4). The results show that the carbonization is the controlling factor of the mixture design of the concrete with ordinary strength (the designed strength is 30MPa). To meet the challenge of climate change, stronger concrete must be used. For high-strength slag blended concrete (design strength is 55MPa), strength is the control factor of mixture design.