scholarly journals A Multi-Objective Chance-Constrained Programming Approach for Uncertainty-Based Optimal Nutrients Load Reduction at the Watershed Scale

Water ◽  
2017 ◽  
Vol 9 (5) ◽  
pp. 322
Author(s):  
Xujun Liu ◽  
Mengjiao Zhang ◽  
Han Su ◽  
Feifei Dong ◽  
Yao Ji ◽  
...  
Keyword(s):  
Programming Approach ◽  
Chance Constrained Programming ◽  
Load Reduction ◽  
Watershed Scale ◽  
Multi Objective ◽  
Objective Chance ◽  
Constrained Programming

scholarly journals Multi-objective chance-constrained blending optimization of zinc smelter under stochastic uncertainty

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Yu Chen ◽  
Yonggang Li ◽  
Bei Sun ◽  
Chunhua Yang ◽  
Hongqiu Zhu
Keyword(s):  
Chance Constrained Programming ◽  
Analytic Hierarchy ◽  
Pareto Solution ◽  
Hybrid Intelligent Algorithm ◽  
Multi Objective ◽  
Roasting Process ◽  
Objective Chance ◽  
Constrained Programming ◽  
Hierarchy Process ◽  
Production Probability

<p style='text-indent:20px;'>Considering the uncertainty of zinc concentrates and the shortage of high-quality ore inventory, a multi-objective chance-constrained programming (MOCCP) is established for blending optimization. Firstly, the distribution characteristics of zinc concentrates are obtained by statistical methods and the normal distribution is truncated according to the actual industrial situation. Secondly, by minimizing the pessimistic value and maximizing the optimistic value of object function, a MOCCP is decomposed into a MiniMin and MaxiMax chance-constrained programming, which is easy to handle. Thirdly, a hybrid intelligent algorithm is presented to obtain the Pareto front. Then, the furnace condition of roasting process is established based on analytic hierarchy process, and a satisfactory solution is selected from Pareto solution according to expert rules. Finally, taking the production data as an example, the effectiveness and feasibility of this method are verified. Compared to traditional blending optimization, recommended model both can ensure that each component meets the needs of production probability, and adjust the confident level of each component. Compared with the distribution without truncation, the optimization results of this method are more in line with the actual situation.</p>

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