• International Journal of Quality Innovation
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• v.5 no.1
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• pp.43-51
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• 2004

During process design or process optimization, it is quite common for experimenters to find optimum operating conditions for several responses simultaneously. The traditional multiresponse surfaces optimization methods do not consider the uncertain relationship among these responses sufficiently. For this reason, the authors propose an optimization method based on evidential reasoning theory by Dempster and Shafer. By maximizing the basic probability assignment function, which indicates the degree of belief that certain operating condition is the solution of this multiresponse surfaces optimization problem, the desirable operating condition can be found.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2610-2624
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• 2024

Liquid metal heat pipes play a critical role in various high-temperature applications, with their optimization being pivotal to achieving optimal thermal performance. In this study, a deep learning based genetic algorithm is suggested to optimize the operating conditions of liquid metal heat pipes. The optimization performance was investigated in both single and multi-variable optimization schemes, considering the operating conditions of heat load, inclination angle, and filling ratio. The single-variable optimization indicated reasonable performance for various conditions, reinforcing the potential applicability of the optimization method across a broad spectrum of high-temperature industries. The multi-variable optimization revealed an almost congruent performance level to single-variable optimization, suggesting that the robustness of optimization method is not compromised with additional variables. Furthermore, the generalization performance of the optimization method was investigated by conducting an experimental investigation, proving a similar performance. This study underlines the potential of optimizing the operating condition of heat pipes, with significant consequences in sectors such as high temperature field, thereby offering a pathway to more efficient, cost-effective thermal solutions.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.582-588
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• 1993

A scheme of dynamic optimization for batch reactor his been developed and applied to a semi-batch esterification reactor. To obtain optimal operating conditions for the given semi-batch reactor system with complex reaction kinetic and process constraints, a general nonlinear programming solver and finite element techniques have been introduced. The optimization results for the complex reactor system have been compared with those of Kumar et al. [1984] to show better optimization performance. The proposed optimizing scheme has been applied to the free end time problem to obtain the realistic operating condition. The results can supply valuable information for economic operation of the given batch esterification reactor.

• Journal of Korean Society for Quality Management
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• v.7 no.2
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• pp.22-28
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• 1979

The problem considered in this paper is to select the vital factor effect to the product quality through the experimental design and analysis of response surface, so as to control the quality improvement of industrial product. In this time, even through the mathematical model is unknown it could be applicable to control the quality of industrial products and to determine optimum operating condition for many technical fields, particulary, for industrial manufacturing process. When a set of data is available from an experimental design, it is often of interest 1:0 fit polynominal repression model in independent variables (eg, time, temperature, pressure, etc) the optimize the response variable (eg. yield, strength etc). This paper proposes a method known to obtain the optimum operating condition, and how to find the condition by using table of orthogonal array experiments, and optimization methodology of statistical model. A criterion can be applied determining to optimum operating conditions in manufacturing industry and improving the fit of response surface which may be used for prediction of responses and quality control of industrial products.

• Proceedings of the KAIS Fall Conference
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• 2004.06a
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• pp.89-91
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• 2004

This study examines a regeneration process using hot air heated by solar radiation to recover absorption potential by evaporating moisture in liquid desiccant. More specifically, this study is aimed at finding the optimum operating condition of the regenerator by utilizing a well-established statistical tool, so-called design of experiment, and optimization techniques. It is demonstrated that an optimization model to find the optimum operating condition can be obtained using the functional relationship between regeneration rate and affecting factors which is approximated on the basis experimental results.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.2
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• pp.101-107
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• 2005

In this paper, optimized operating parameters were found using multi-dimensional engine simulation software (KIVA-3V) and micro-genetic algorithm for heavy duty diesel engine. The engine operating condition considered was at 1,737 rev/min and 57 % load. Engine simulation model was validated using an engine equipped with a high pressure electronic unit injector (HEUI) system. Three important parameters were used for the optimization - boost pressure, EGR rate and start of injection timing. Numerical optimization identified HCCI-like combustion characteristics showing significant improvements for the soot and $NO_X$ emissions. The optimized soot and $NO_X$ emissions were reduced to 0.005 g/kW-hr and 1.33 g/kW-hr, respectively. Moreover, the optimum results met EPA 2007 mandates at the operating point considered.

• Journal of Electrical Engineering and Technology
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• v.8 no.1
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• pp.80-89
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• 2013

This paper presents an efficient approach for solving economic dispatch (ED) problems with nonconvex cost functions using a 'Mean-Variance Optimization (MVO)' algorithm with Kuhn-Tucker condition and swap process. The aim of the ED problem, one of the most important activities in power system operation and planning, is to determine the optimal combination of power outputs of all generating units so as to meet the required load demand at minimum operating cost while satisfying system equality and inequality constraints. This paper applies Kuhn-Tucker condition and swap process to a MVO algorithm to improve a global minimum searching capability. The proposed MVO is applied to three different nonconvex ED problems with valve-point effects, prohibited operating zones, transmission network losses, and multi-fuels with valve-point effects. Additionally, it is applied to the large-scale power system of Korea. The results are compared with those of the state-of-the-art methods as well.

• Journal of Korean Institute of Industrial Engineers
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• v.7 no.2
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• pp.55-59
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• 1981

An Application of Experimental Designs to Improve the quality of Industrial product : optimization Methodology of statistical model. The primary object of this paper is to aid scientists and Engineers, in applying response surface procedures to obtain operating conditions for many technical fields, particularly for industrial manufacturing processes. The problem considered in this paper is to select technically and scientifically some important factors affecting the quality of products through the experimental design and analysis of response surface. Even though the mathematical model is unknown these statistical analysis can be applicable to control the quality of industrial products and to determine optimum operating conditions for many technical fields, particularly, for industrial manufacturing processes. This paper proposes a method to obtain the optimum operating condition, and how to find the condition by using table of orthogonal array experiments, and optimization methodology of statistical model.

• Clean Technology
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• v.20 no.3
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• pp.298-305
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• 2014

Ash included in coal can cause environmental pollution and it can decrease efficiency of mass and heat transfer by getting scorched and stick in the facilities operated at high temperature. To solve this problem, a feasibility study on pulverized coal fired power plant and integrated gasification combined cycle (IGCC) using the AFC (Ash-Free Coal) as well as the development to remove the ash from the coal was conducted. In this research, optimization of operating condition was proposed by using sensitivity analysis of ASPEN $Plus^{(R)}$ to apply the coal containing under the 200 ppm ash for integrated gasification combined cycle. Particularly, the coal gasification process was classified as three parts : pyrolysis process, volatile matter combustion process and char gasification process. The dimension and operating condition of 1.5 ton/day class non-slagging gasifier are reflected in the coal gasification process model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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• pp.451-456
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• 2007

This paper presents a problem that reconfigure distribution power system using branch exchange method. Optimal reconfiguration problem calculates line loss, voltage condition about system states of all situations that become different according to line On/off status, and search for optimum composition of these. However, result is difficult to be calculated fast. Because radiated operation condition of system is satisfied using many connection and sectionalize switches in the distribution power system. Therefore, in this paper, optimization method for reducing system total loss and satisfying operating condition of radial and constraints condition of voltage is proposed using the fastest branch exchange. And optimal solution at branch exchange algorithm can be wrong estimated to local optimal solution according to initial operating state. Considering this particular, an initial operating point algorithm is added and this paper showed that optimal solution arrives at global optimal solution.