A New Approach to Blending and Loading Problem of Molten Aluminum
DOI:
https://doi.org/10.5614/j.eng.technol.sci.2014.46.4.8Abstract
The problems of blending electrolyzer and multi-constraint optimization of electrolytic aluminum scheduling in the electrolytic aluminum production process were addressed. Based on a mathematical model analysis, a novel hybrid optimization algorithm is proposed for optimization of blending together the molten aluminum in different electrolytic cells. An affinity degree function was designed to represent the path of aluminum scheduling. The mutation operators were designed to implement the transformation of electrolyzer combination and change the route of loading. A typical optimization example from an aluminum plant in northwest China is given in this paper, the results of which demonstrate the effectiveness of the proposed method.Downloads
References
Kumar, M. & Rajotia, S., Integration of Scheduling with Computer Aided Process Planning, Journal of Materials Processing Technology, 138(1),pp. 297-300, 2003.
Moon, C. & Seo, Y., Evolutionary Algorithm for Advanced Process Planning and Scheduling in a Multi-plant, Computers & Industrial Engineering, 48(2), pp. 311-325, 2005.
Menca, R., Sierra, M.R. & Mencia, C., A Genetic Algorithm for JobShop Scheduling with Operators Enhanced by Weak Lamarckian Evolution and Search Space Narrowing, Natural Computing, 13(2), pp.179-192, 2014.
Shabtay, D., Gaspar, N. & Kaspi, M., A Survey on Offline Scheduling with Rejection, Journal of Scheduling, 16(1), pp. 3-28, 2013.
Xia, Y.M., Calculation Analysis and Software Development of Primary Aluminum Casting Optimized Proportioning Aluminum Method, Light Metals, 25(7), pp. 38-41, 2005.
Denis, B-G., Yano, C.A. & Leung, J.M.Y., Discrete Deterministic and Stochastic Blending Problems with Two Quality Characteristics: Aluminum Blending, IIE Transactions, 31(10), pp. 1001-1009, 1999.
Zhang, Y.L. Research and Development of Arranging Package of Aluminum Electrolysis Cells Intelligently, PhD Dissertation, College of Electronical and Information Engineering, North China University of Technology, Beijing, China, 2009.
Ronghua, S., A Novel Immune Clonal Algorithm for MO Problems, IEEE Transactions on Evolutionary Computation, 16(1), pp. 35-50, 2012.
Golmakani, H.R. & Namazi, A., An Artificial Immune Algorithm for Multiple-Route Job Shop Scheduling Problem, The International Journal of Advanced Manufacturing Technology, 63(1), pp. 77-86, 2012.
Chryssolouris, G. & Subramaniam, V., Dynamic Scheduling of Manufacturing Job Shops Using Genetic Algorithms, Journal of Intelligent Manufacturing, 12(3), pp. 281-293, 2001
