• Tribology and Lubricants
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• v.38 no.1
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• pp.1-7
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• 2022

Chemical-mechanical polishing (CMP) process is a semiconductor process that planarizes a wafer surface using mechanical friction between a polishing pad and a substrate surface during a specific chemical reaction. During the CMP process, polishing pad conditioning is applied to prevent the rapid degradation of the polishing quality caused by polishing pad glazing through repeated material removal processes. However, during the conditioning process, uneven wear on the polishing pad is inevitable because the disk on which diamond particles are electrodeposited is used. Therefore, the abrasion of the polishing pad should be considered not only for the variables during the conditioning process but also when designing the CMP conditioning system. In this study, three design variables of the conditioning system were analyzed, and the effect on the pad wear profile during conditioning was investigated. The three design variables considered in this study were the length of the conditioner arm, diameter of the conditioner disk, and distance between centers. The Taguchi method was used for the experimental design. The effect of the three design variables on pad wear and uniformity was assessed, and new variables used in conditioning system design were proposed.

• Korean Chemical Engineering Research
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• v.53 no.4
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• pp.440-444
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• 2015

Effects of operating variables on the solid circulation rate were investigated in a three-phase circulating fluidized bed, of which inside diameter was 0.102m and height was 3.5m, respectively. Gas velocity, primary and secondary liquid velocities, particle size and height of solid particles piled up in the solid recycle device were chosen as operating variables. The solid circulation rate increased with increasing primary and secondary liquid velocities and height of solid particles piled up in the solid recycle device, but decreased with increasing particle size. The value of solid circulation rate decreased only slightly with increasing gas velocity in the riser. The values of solid circulation rate were well correlated in terms of dimensionless groups within the experimental conditions.

• Bulletin of the Korean Chemical Society
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• v.20 no.5
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• pp.525-527
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• 1999

Many types of chemical data tend to be characterized by many measured variables on each of a few observations. In this situation, target concentration can be predicted using multivariate statistical modeling. However, it is necessary to use a few variables considering size and cost of instrumentation, for an example, for development of a portable biomedical instrument. This study presents, with a spectral data set of total hemoglobin in whole blood, the possibility that modeling using only a few variables can improve predictability compared to modeling using all of the variables. Predictability from the model using three wavelengths selected from all possible regression method was improved, compared to the model using whole spectra (whole spectra: SEP = 0.4 g/dL, 3-wavelengths: SEP=0.3 g/dL). It appears that the proper selection of variables can be more effective than using whole spectra for determining the hemoglobin concentration in whole blood.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1204-1209
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• 1990

This paper offers the theory and method for regression analysis of the regression model with operational parameter variables based on the fundamentals of mathematical statistics. Regression coefficients are usually constants related to the problem of regression analysis. This paper considers that regression coefficients are not constants but the functions of some operational parameter variables. This is a kind of method of two-step fitting regression model. The second part of this paper considers the experimental step numbers as recursive variables, the recursive identification with unknown operational parameter variables, which includes two recursive variables, is deduced. Then the optimization and the recursive identification are combined to obtain the sequential experiment optimum design with operational parameter variables. This paper also offers a fast recursive algorithm for a large number of sequential experiments.

• Journal of the Korean Chemical Society
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• v.56 no.2
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• pp.257-263
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• 2012

The selection of suitable delignification conditions and optimization of process variables is crucial to the successful operation of chemical pulping processes. Soda pulping of Carpolobia lutea was investigated, as an alternative raw material for pulp and paper production. The process was optimized under the influence of three operational variables, namely, temperature, time and concentration of cooking liquor. Equations derived using a second - order polynomial design predicted the pulp yield and lignin dissolution with errors less than 8% and 11% respectively. The maximum variations in the pulp yield using a second order factorial design was caused by changes in both time and alkali concentration. Optimum pulp yield of 43.87% was obtained at low values of the process variables. The selectivity of lignin dissolution was independent of the working conditions, allowing quantitative estimations to be established between the pulp yield and residual lignin content within the range studied.

• The Korean Journal of Ceramics
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• v.4 no.3
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• pp.235-239
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• 1998

Ultrafine $SnO_2$ powder was prepared by the diffusion mixing gas-phase reaction of $SnCl_4$(g) and water vapor. The effects of reaction variables, such as the chloride partial pressure, the reaction temperature, and the residence time is the reactor, on the powder size were examined systematically. Calculated concentration and distribution of chemical species, using the Burke-Schumann diffusion mixing model, were compared with the experimetal results. The effects of the reaction variables on the powder size were also discussed qualitatively.

• Korean Chemical Engineering Research
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• v.46 no.4
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• pp.714-721
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• 2008

Chemical plants which consume lots of energy are not operating in the best conditions due to their own peculiar nonlinearity, instability, and diverse disturbances. In order to improve this, the plant wide optimization was performed. It is important to select the most appropriate number of decision variables which strongly affect the operating cost because there are too many decision variables which economically have an effect on plant wide. For instance, if all decision variables which can economically affect are applied in optimization and then the result of the optimization is applied to operation, a lot of operating conditions should be going to be changed. As a result of changing a plenty of operating conditions, the cost of the change will absolutely increase. Thus, in this study, the method of selecting the most appropriate decision variables which can influence on saving operation costs was presented in order to optimize plant wide. TPA (Terephthalic-acid) plant is considered as a case study. In other word, after modeling, the most proper decision variables was selected by examining the degree which decision variables influence on operating costs through sensitivity analysis. In TPA process, the three decision variables were selected by the presented method in this study. Then the plant was optimized by selected the decision variables. Consequently, it was seen that the plant are expected to save the 350 million won of energy annually without additional investment for facilities or remodeling of the plant.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• Bulletin of the Korean Chemical Society
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• v.27 no.10
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• pp.1659-1663
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• 2006

A many-body master equation is constructed by incorporating stochastic terms responsible for chemical reactions into the many-body Smoluchowski equation. Two forms of Langevin-type of memory equations describing the time evolution of dynamical variables under the influence of time-independent perturbation with an arbitrary intensity are derived. One form is convenient in obtaining the dynamics approaching the steady-state attained by the perturbation and the other in describing the fluctuation dynamics at the steady-state and consequently in obtaining the linear response of the system at the steady-state to time-dependent perturbation. In both cases, the kinetics of statistical averages of variables is found to be obtained by analyzing the dynamics of time-correlation functions of the variables.

• Journal of the Korean Institute of Gas
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• v.3 no.2 s.7
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• pp.34-42
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• 1999

The discrete variables such as time and sequence must be considered for automating HAZOP analysis of batch processes in contrast with continuous processes. Because these variables can not be explained by the method used in the HAZOP analysis of continuous processes, we have developed the methodology for HAZOP analysis of batch processes on the basis of the relation between discrete variables and continuous ones. In this study, we have discussed the performance of the methodology on a Latex batch process to evaluate its effectiveness.