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response surface method for modeling the removal of carbon dioxide from a simulated gas using water absorption enhanced with a liquid-film-forming device

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Journal of Environmental Sciences 2018年第3期30卷 116-126页

作者： Diem-Mai Kim Nguyen Tsuyoshi Imai Thanh-Loc Thi Dang Ariyo Kanno Takaya Higuchi Koichi Yamamoto Masahiko Sekine Graduate School of Environmental Sciences and Engineering Yamaguchi University Graduate School of Sciences and Technology for Innovation Yamaguchi University Department of Environmental Science College of Sciences Hue University

This paper presents the results from using a physical absorption process to absorb gaseous CO2mixed with N2using water by producing tiny bubbles via a liquid-film-forming device（LFFD）that improves the solubility of CO2in water.The influence of various parameters—pressure,initial CO2concentration,gas-to-liquid ratios,and temperature—on the CO2removal efficiency and its absorption rate in water were investigated and estimated thoroughly by statistical polynomial models obtained by the utilization of the response surface method（rsm）with a central composite design（CCD）.Based on the analysis,a high efficiency of CO2capture can be reached in conditions such as low pressure,high CO2concentration at the inlet,low gas/liquid ratio,and low temperature.For instance,the highest removal efficiency in the rsm–CCD experimental matrix of nearly 80%occurred for run number 20,which was conducted at 0.30 MPa,CO2concentration of 35%,gas/liquid ratio of 0.71,and temperature of 15℃.Furthermore,the coefficients of determination,R^2,were 0.996 for the removal rate and 0.982 for the absorption rate,implying that the predicted values computed by the constructed models correlate strongly and fit well with the experimental values.The results obtained provide essential information for implementing this method properly and effectively and contribute a promising approach to the problem of CO2capture in air pollution treatment.

关键词： Carbon dioxide Water dissolution Liquid-film response surface method （rsm）

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response surface method using grey relational analysis for decision making in weapon system selection

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Journal of Systems Engineering and Electronics 2014年第2期25卷 265-272页

作者： Peng Wang Peng Meng Baowei Song School of Marine Science and Technology Northwestern Polytechnical University

A proper weapon system is very important for a na- tional defense system. Generally, it means selecting the optimal weapon system among many alternatives, which is a multiple- attribute decision making （MADM） problem. This paper proposes a new mathematical model based on the response surface method （rsm） and the grey relational analysis （GRA）. rsm is used to obtain the experimental points and analyze the factors that have a significant impact on the selection results. GRA is used to an- alyze the trend relationship between alternatives and reference series. And then an rsm model is obtained, which can be used to calculate all alternatives and obtain ranking results. A real world application is introduced to illustrate the utilization of the model for the weapon selection problem. The results show that this model can be used to help decision-makers to make a quick comparison of alternatives and select a proper weapon system from multiple alternatives, which is an effective and adaptable method for solving the weapon system selection problem.

关键词： weapon system multiple-attribute decision making（MADM） response surface method （rsm） grey relational analysis（GRA）.

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维普期刊数据库

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Computer modeling of high-pressure leaching process of nickel laterite by design of experiments and neural networks

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International Journal of Minerals,Metallurgy and Materials 2012年第7期19卷 584-594页

作者： Milovan Milivojevic Srecko Stopic Bernd Friedrich Boban Stojanovic Dragoljub Drndarevic Technical and Business College IME Process Metallurgy and Metal Recycling RWTH Aachen University Department of Mathematics and Informatics Faculty of Science University of Kragujevac

Due to the complex chemical composition of nickel ores, the requests for the decrease of production costs, and the increase of nickel extraction in the existing depletion of high-grade sulfide ores around the world, computer modeling of nickel ore leaching process be- came a need and a challenge. In this paper, the design of experiments （DOE） theory was used to determine the optimal experimental design plan matrix based on the D optimality criterion. In the high-pressure sulfuric acid leaching （HPSAL） process for nickel laterite in ＂Rudjinci＂ ore in Serbia, the temperature, the sulfuric acid to ore ratio, the stirring speed, and the leaching time as the predictor variables, and the degree of nickel extraction as the response have been considered. To model the process, the multiple linear regression （MLR） and response surface method （rsm）, together with the two-level and four-factor full factorial central composite design （CCD） plan, were used. The proposed re- gression models have not been proven adequate. Therefore, the artificial neural network （ANN） approach with the same experimental plan was used in order to reduce operational costs, give a better modeling accuracy, and provide a more successful process optimization. The model is based on the multi-layer neural networks with the back-propagation （BP） learning algorithm and the bipolar sigmoid activation function.

关键词： nickel laterite leaching computer simulation design of experiments （DOE） response surface method （rsm） neural networks

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