Optimization of a nuclear contaminated waste recycling system considering radiation risks
Nuclear pollution poses a significant threat to both the ecological environment and public health.In order to guarantee the safety and economic viability of nuclear waste recycling,we propose a multi-objective optimization approach for the nuclear waste recycling system.This approach is based on radiation risk analysis and assessment models,with the aim of addressing both the location and routing challenges associated with nuclear waste recycling.Taking into account the environmental transmission characteristics of radioactive materials and radiation effects,as well as the potential risk to the public,we have designed a radiation risk assessment model based on the Gaussian plume model.This model is utilized to evaluate the extent and severity of harmful radiation effects.To minimize overall risk and cost,we have developed a 0-1 mixed integer linear programming model.Based on the computational complexity of the proposed bi-objective model,we have designed a three-stage solution procedure known as"multi-objective transformation-location-routing."In the first stage,the dual-target model is transformed into a single-target model,taking into account the Not in My Back Yard(NIMBY)effect.The second stage involves employing the minimum envelope clustering method to address the location problem.In the third stage,the genetic algorithm is utilized to solve the routing problem.Finally,we validate the effectiveness of the new radiation risk assessment model and algorithm by comparing results with different risk assessment models and solution methods.Examples across various computational scales are provided to verify the stability of the new algorithmic solutions.The calculation results demonstrate that the new model and algorithm effectively provide optimal location and routing plans.Compared to traditional risk assessment models,the new radiation risk assessment model achieves an optimal plan with lower transportation costs.The algorithm reduces solution time by 56.02％and decreases the average difference rate of the optimal value by 2.87 percentage points,while maintaining stable performance across different problem scales.
public safetynuclear pollutantrecoveryrisksite selectionroutemulti-objective optimization
万方数据
维普
