碳排放约束下的水工隧洞施工机群配置优化

Configuration optimization of construction machine group for hydraulic tunnel under carbon emission constraint

考虑碳排放约束条件,基于水工隧洞施工机群配置优化和碳减排双赢的改进路径,构建基于工期-成本-质量-碳排放量的多目标优化函数;选择模拟退火算法改进后的粒子群算法,参考施工机械台班费用定额和《IPCC国家温室气体排放清单指南》计算施工机械的台班碳排放量;将新型机械配置纳入碳排放因素,建立以工期、成本、质量和碳排放为一级指标和14个二级指标的水工隧洞施工机群配置目标偏好权重评价指标体系,并构建考虑碳排放的机群配置优化模型,以滇中引水工程万家隧洞3号支洞为例开展多目标约束下的施工机群配置优化分析。结果表明:建立的水工隧洞施工机群配置目标偏好权重评价指标体系可以较全面地考量施工机群对环境的影响性;计算得出该施工机群主要碳排放源为隧道多功能作业台车和混凝土搅拌站,碳排放量(以CO 2计)达到1000 kg/台班。改进后的粒子群算法可以有效增强全局寻优的搜索能力,识别施工机械中碳排放关键风险源,模型优化结果与现场施工情况相符,验证了模型的可行性。

A multi-objective optimization function based on the schedule-cost-quality-carbon emission is constructed according to the improvement path of optimization of hydraulic tunnel construction machine fleet and carbon emission reduction under the constraint of carbon emission.A particle swarm optimization(PSO)after the improvement of simulated annealing(SA)is selected and the carbon emission per working unit of construction machines is calculated with the reference to the construction machine-working cost quota and IPCC guidelines.By incorporating the new mechanical configuration into carbon emission factors,an evaluation system with duration,cost,quality and carbon emission as the primary indicators and 14 secondary indicators is established and the optimization model of fleet configuration considering carbon emissions is built.The optimization of construction machine fleet configuration under multi-objective constraints was carried out in the No.3 branch of Wanjia tunnel of the Dianzhong water diversion project.The results show that the established target preference weight evaluation system of hydraulic tunnel construction fleet configuration can comprehensively consider the impact of construction fleets on the environment.The main carbon emission sources of the construction machine fleet are tunnel multifunctional work cart and concrete mixing plant,and the carbon emission(CO 2)reaches 1000 kg/machine working unit.The improved particle algorithm can effectively enhance the search capability of global optimization and identify the key risk sources of carbon emission in the construction machinery.Finally,the model optimization results are consistent with the on-site construction situation,which verifies the feasibility of the model.