Abstract
Considering serious environmental pollution caused by phosphogypsum and the scarce of phosphate rock resource, the recycling utilisation of phosphogypsum has significant influence on the sustainable development. To promote the recycling utilisation of phosphogypsum, this paper studies the phosphogypsum reduction in phosphate fertilizer plants based on the stack allowance trading mechanism. According to the detail analysis, an optimization model has been established to describe and resolve the complexity of the phosphogypsum reduction problem. In the optimization process, the uncertain environment and stack allowance trading mechanism have been fully considered. A practical case is given to demonstrates the efficiency of the formulated method. The results indicates that the stack allowances trading mechanism all the phosphate fertilizer plants to make more flexible operation decision, which not only realize the environmental pollution mitigation and PG recycling, but also promote each phosphate fertilizer plant sustainable development.
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Acknowledgements
This research was supported by the State Key Development Program for Basic Research in China (973 Program, Grant No. 2011CB201200), the Fund for Creative Research Groups of China (Grant No. 50221402), the National Natural Science Foundation of China (Grant No. 71671118), and the Funds for Creative Research for graduate student of Sichuan University (Grant No. 2012017yjsy104). The authors would like to thank the anonymous referees for their insightful comments and suggestions to improve this paper, as well as the Uncertainty Decision-Making Laboratory and Low Carbon Technology and Economy Research Center of Sichuan University for helpful comments and discussion.
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Fan, L., Chen, G., Li, Z., Lev, B., Zhou, X. (2020). Stack Allowance Trading Mechanism Based Optimization Strategy for Phosphogypsum Reduction in Phosphate Fertilizer Plants. In: Xu, J., Ahmed, S., Cooke, F., Duca, G. (eds) Proceedings of the Thirteenth International Conference on Management Science and Engineering Management. ICMSEM 2019. Advances in Intelligent Systems and Computing, vol 1001. Springer, Cham. https://doi.org/10.1007/978-3-030-21248-3_19
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DOI: https://doi.org/10.1007/978-3-030-21248-3_19
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