This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have ...This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.展开更多
The rising demand for high-density power storage systems such as hydrogen,combined with renewable power production systems,has led to the design of optimal power production and storage systems.In this study,a wind and...The rising demand for high-density power storage systems such as hydrogen,combined with renewable power production systems,has led to the design of optimal power production and storage systems.In this study,a wind and photovoltaic(PV)hybrid electrolyzer system,which maximizes the hydrogen production for a diurnal operation of the system,is designed and simulated.The operation of the system is optimized using imperialist competitive algorithm(ICA).The objective of this optimization is to combine the PV array and wind turbine(WT)in a way that,for minimized average excess power generation,maximum hydrogen would be produced.Actual meteorological data of Miami is used for simulations.A framework of the advanced alkaline electrolyzer with the detailed electrochemical model is used.This optimal system comprises a PV module with a power of 7.9 kW and a WT module with a power of 11 kW.The rate of hydrogen production is 0.0192 mol/s;an average Faraday efficiency of 86.9 percent.The electrolyzer works with 53.7 percent of its nominal power.The availability of the wind for longer periods of time reflects the greater contribution of WT in comparison with PV towards the overall throughput of the system.展开更多
Despite the success of the imperialist competitive algorithm(ICA)in solving optimization problems,it still suffers from frequently falling into local minima and low convergence speed.In this paper,a fuzzy version of t...Despite the success of the imperialist competitive algorithm(ICA)in solving optimization problems,it still suffers from frequently falling into local minima and low convergence speed.In this paper,a fuzzy version of this algorithm is proposed to address these issues.In contrast to the standard version of ICA,in the proposed algorithm,powerful countries are chosen as imperialists in each step;according to a fuzzy membership function,other countries become colonies of all the empires.In absorption policy,based on the fuzzy membership function,colonies move toward the resulting vector of all imperialists.In this algorithm,no empire will be eliminated;instead,during the execution of the algorithm,empires move toward one point.Other steps of the algorithm are similar to the standard ICA.In experiments,the proposed algorithm has been used to solve the real world optimization problems presented for IEEE-CEC 2011 evolutionary algorithm competition.Results of experiments confirm the performance of the algorithm.展开更多
文摘This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.
基金supported by the National Science Foundation (No.1553494)
文摘The rising demand for high-density power storage systems such as hydrogen,combined with renewable power production systems,has led to the design of optimal power production and storage systems.In this study,a wind and photovoltaic(PV)hybrid electrolyzer system,which maximizes the hydrogen production for a diurnal operation of the system,is designed and simulated.The operation of the system is optimized using imperialist competitive algorithm(ICA).The objective of this optimization is to combine the PV array and wind turbine(WT)in a way that,for minimized average excess power generation,maximum hydrogen would be produced.Actual meteorological data of Miami is used for simulations.A framework of the advanced alkaline electrolyzer with the detailed electrochemical model is used.This optimal system comprises a PV module with a power of 7.9 kW and a WT module with a power of 11 kW.The rate of hydrogen production is 0.0192 mol/s;an average Faraday efficiency of 86.9 percent.The electrolyzer works with 53.7 percent of its nominal power.The availability of the wind for longer periods of time reflects the greater contribution of WT in comparison with PV towards the overall throughput of the system.
文摘Despite the success of the imperialist competitive algorithm(ICA)in solving optimization problems,it still suffers from frequently falling into local minima and low convergence speed.In this paper,a fuzzy version of this algorithm is proposed to address these issues.In contrast to the standard version of ICA,in the proposed algorithm,powerful countries are chosen as imperialists in each step;according to a fuzzy membership function,other countries become colonies of all the empires.In absorption policy,based on the fuzzy membership function,colonies move toward the resulting vector of all imperialists.In this algorithm,no empire will be eliminated;instead,during the execution of the algorithm,empires move toward one point.Other steps of the algorithm are similar to the standard ICA.In experiments,the proposed algorithm has been used to solve the real world optimization problems presented for IEEE-CEC 2011 evolutionary algorithm competition.Results of experiments confirm the performance of the algorithm.
