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Recycling of cotton clothing into activated carbon fibers
Kim Jiyun,Kwon Woong,Bai Byong Chol,정의경 한국탄소학회 2022 Carbon Letters Vol.32 No.5
This study investigates the preparation of activated carbon fiber derived from waste cotton fabric for economical and eco-friendly recycling as well as its application to water purification. The activated carbon fiber was prepared by physical activation using steam and the adsorption property was then evaluated using methylene blue. When the activation temperature increased, the specific surface area and mesopore volume of the activated carbon fiber increased up to 2562 m2/g and 0.214 cm3/g, resulting in the increased adsorption of methylene blue. The results of the adsorption experiment for the activated carbon fiber were analyzed using the Langmuir and Freundlich equations. The Langmuir equation was more suitable than the Freundlich equation to explain the adsorption equilibrium. The maximum adsorption amount of methylene blue was 161.1–731.5 mg/g for fiber samples activated at temperatures ranging from 750 to 950 °C with sample labeled 750SA to 900SA according to the Langmuir equation. The kinetics of methylene blue adsorption by the activated carbon fiber were analyzed using non-linear pseudo-first-order and pseudo-second-order. Sample 750SA was suitable for the pseudo-first-order and 800SA, 850SA, and 900SA sample were suitable for the pseudo-second-order. Therefore, waste cotton fabric has the potential to be the precursor for activated carbon fiber with excellent adsorption properties.
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Peng Wang,Jiyun Bai,Jun Meng 한국정보처리학회 2020 Journal of information processing systems Vol.16 No.5
The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However,the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. Thegenetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and theoptimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods anddiscussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved betterperformance than ACOR, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reachthe global optimal solution.
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The Combination of Extension of Ant Colony Algorithm and Other Intelligent Algorithms
Ma Li,Li Qianting,Ma Meiqiong,Meng Jun,Bai Jiyun 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.12
The extension of ant colony algorithm was proposed by Dorigo, the founder of ant colony algorithm, which is the latest ant colony algorithm for solving a continuous space optimization problem. Considering the blindness of man-made choice of initial solution and initial parameters of the algorithm, and according to the algorithm converging slowly and easily falling into local optimum, this paper has provided improvement strategy for this optimization. It has introduced quantum computing and genetic algorithm, chaos optimization to carry out combination and comparison, and it has carried out improvement on the weight internally solved by memory in the algorithm. The effectiveness of various combined algorithms was determined through the optimization of numerous multi-dimensional continuous functions.
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