A multi-strategy Improved Multi-Objective Particle Swarm Algorithm(IMOPSO)method for microgrid operation optimization is proposed for the coordinated optimization problem of microgrid economy and environmental protect...A multi-strategy Improved Multi-Objective Particle Swarm Algorithm(IMOPSO)method for microgrid operation optimization is proposed for the coordinated optimization problem of microgrid economy and environmental protection.A grid-connected microgrid model containing photovoltaic cells,wind power,micro gas turbine,diesel generator,and storage battery is constructed with the aim of optimizing the multi-objective grid-connected microgrid economic optimization problem with minimum power generation cost and environmental management cost.Based on the optimization of the standard multi-objective particle swarm optimization algorithm,four strategies are introduced to improve the algorithm,namely,Logistic chaotic mapping,adaptive inertia weight adjustment,adaptive meshing using congestion distance mechanism,and fuzzy comprehensive evaluation.The proposed IMOPSO is applied to the microgrid optimization problem and the performance is compared with other unimproved multi-objective gray wolf algorithm(MOGWO),multi-objective ant colony algorithm(MOACO),and MOPSO algorithms,and the total cost of the proposed method is reduced by 3.15%,8.34%,and 10.27%,respectively.The simulation results show that IMOPSO can more effectively reduce the cost and optimize power distribution,and verify the effectiveness of the proposed method.展开更多
In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op...In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.展开更多
Effects of initial pH, temperature, liquid volume, rotation speed, galvanic interaction （pyrite ratio） and pulp density on bioleaching of complex Cu-polymetallic concentrate were investigated. The results indicated ...Effects of initial pH, temperature, liquid volume, rotation speed, galvanic interaction （pyrite ratio） and pulp density on bioleaching of complex Cu-polymetallic concentrate were investigated. The results indicated that the copper extraction at pH 1.5 was 1.5 and 1.4 times that at pH 1.0 and pH 2.0 respectively. The copper extraction obtained at 45 ℃ was 1236.8%higher than that at 50 ℃. With the increase of rotation speed or the decrease of liquid volume, copper extraction was improved obviously. Copper extraction was improved gradually with the increase of pyrite ratio. However, when the ratio was higher than 20.0%, no further increase in copper extraction was observed. And the statistically significant interactive effects on copper extraction were found between temperature and pH, and temperature and pyrite ratio.展开更多
Land use structural optimization is an effective approach to realize land sustainable utilization and allocate limited land resource rationally. Grey multiple objectives programming (GMOP) model based on China terrest...Land use structural optimization is an effective approach to realize land sustainable utilization and allocate limited land resource rationally. Grey multiple objectives programming (GMOP) model based on China terrestrial ecosystem service value was constructed and applied to Lilin town. The result shows that GMOP model has more practical applicability and takes ecologic, social and comprehensive benefit into consideration. There are three programs after optimization. Program Ⅰ is comprehensive improvement and constructing ecological economy type, program Ⅱ is gross cultivated land dynamic balance type and program Ⅲ is compromise type. There are still problems in programs Ⅱ and Ⅲ, such as distribution in disorder, land left unused or abandoned. Based on the benefits above, Program Ⅰ>Program Ⅲ> Program Ⅱ. Program Ⅰ is the optimal case. Its comprehensive benefit is 8.43208×107 RMB yuan/a.展开更多
Resource allocation for an equipment development task is a complex process owing to the inherent characteristics,such as large amounts of input resources,numerous sub-tasks,complex network structures,and high degrees ...Resource allocation for an equipment development task is a complex process owing to the inherent characteristics,such as large amounts of input resources,numerous sub-tasks,complex network structures,and high degrees of uncertainty.This paper presents an investigation into the influence of resource allocation on the duration and cost of sub-tasks.Mathematical models are constructed for the relationships of the resource allocation quantity with the duration and cost of the sub-tasks.By considering the uncertainties,such as fluctuations in the sub-task duration and cost,rework iterations,and random overlaps,the tasks are simulated for various resource allocation schemes.The shortest duration and the minimum cost of the development task are first formulated as the objective function.Based on a multi-objective particle swarm optimization(MOPSO)algorithm,a multi-objective evolutionary algorithm is constructed to optimize the resource allocation scheme for the development task.Finally,an uninhabited aerial vehicle(UAV)is considered as an example of a development task to test the algorithm,and the optimization results of this method are compared with those based on non-dominated sorting genetic algorithm-II(NSGA-II),non-dominated sorting differential evolution(NSDE)and strength pareto evolutionary algorithm-II(SPEA-II).The proposed method is verified for its scientific approach and effectiveness.The case study shows that the optimization of the resource allocation can greatly aid in shortening the duration of the development task and reducing its cost effectively.展开更多
The sulfur-containing odor emitted from sludge composting could be controlled by sulfide oxidizing bacteria, yet mesophilic strains show inactivation during the thermophilic stage of composting. Aimed to investigate a...The sulfur-containing odor emitted from sludge composting could be controlled by sulfide oxidizing bacteria, yet mesophilic strains show inactivation during the thermophilic stage of composting. Aimed to investigate and characterize the thermotolerant bacterium that could oxidize sulfide into sulfate, a heterotrophic strain was isolated from sewage sludge composting and identified as Paenibacillus naphthalenovorans LYH-3. The effects of various environmental factors on sulfide oxidation capacities were studied to optimize the sulfate production, and the highest production rate (27.35%±0.86%) was obtained at pH 7.34, the rotation speed of 161.14 r/min, and the inoculation amount of 5.83%by employing BoxBehnken design. The results of serial sulfide substrates experiments indicated that strain LYH-3 could survive up to 400 mg/L of sulfide with the highest sulfide removal rate (88.79%±0.35%) obtained at 50 mg/L of sulfide. Growth kinetic analysis presented the maximum specific growth rateμm(0.5274 hr-1) after 22 hr cultivation at 50℃. The highest enzyme activities of sulfide quinone oxidoreductase (0.369±0.052 U/mg) and sulfur dioxygenase (0.255±0.014 U/mg) were both obtained at 40℃, and the highest enzyme activity of sulfite acceptor oxidoreductase (1.302±0.035 U/mg) was assessed at 50℃. The results indicated that P. naphthalenovorans possessed a rapid growth rate and efficient sulfide oxidation capacities under thermophilic conditions, promising a potential application in controlling sulfur-containing odors during the thermophilic stage of sludge composting.展开更多
Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is pro...Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.展开更多
Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley a...Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.展开更多
The optimization of nutrient levels for the production of recombinant hyperthermophilie esterase by E. coli was carried out with response surface methodology（RSM） based on the central composite rotatable design（CCR...The optimization of nutrient levels for the production of recombinant hyperthermophilie esterase by E. coli was carried out with response surface methodology（RSM） based on the central composite rotatable design（CCRD）. A 24 central composite rotatable design was used to study the combined effect of the nutritional constituents like yeast extract, peptone, mineral salt and trace metals. The P-value of the coefficient for the linear effect of peptone concentration was 0. 0081 and trace metals solution was less than 0. 0001, suggesting that these were the principal variables with significant effect on the hyperthermophilic esterase production. The predicted optimal hyperthermophilie esterase yield was 269. 17 U/mL, whereas an actual experimental value of 284. 58 U/mL was obtained.展开更多
Multi-project multi-site location problems are multi-objective combinational optimization ones with discrete variables which are hard to solve. To do so, the case of particle swarm optimization is considered due to it...Multi-project multi-site location problems are multi-objective combinational optimization ones with discrete variables which are hard to solve. To do so, the case of particle swarm optimization is considered due to its useful char- acteristics such as easy implantation, simple parameter settings and fast convergence. First these problems are trans- formed into ones with continuous variables by defining an equivalent probability matrix in this paper, then multi-objective particle swarm optimization based on the minimal particle angle is used to solve them. Methods such as continuation of discrete variables, update of particles for matrix variables, normalization of particle position and evalua- tion of particle fitness are presented. Finally the efficiency of the proposed method is validated by comparing it with other methods on an eight-project-ten-site location problem.展开更多
This paper presents the implementation of two multicriteria optimization methods based on different approaches, namely, Rough Set Method (RSM) and Net Flow Method (NFM), to the manufacture by reactive extrusion of lin...This paper presents the implementation of two multicriteria optimization methods based on different approaches, namely, Rough Set Method (RSM) and Net Flow Method (NFM), to the manufacture by reactive extrusion of linear thermoplastic polyurethanes (TPUs), appropriate for medical applications. A preliminary study allowed determining the process operating conditions for which the polymerization time and the average residence time of the reactants in the extruder are of the same order of magnitude. Prior to the optimization, a neural network model able to predict with acceptable accuracy the effect of the operating conditions on the output process variables, was constructed and validated. This model was then used to determine, using Pareto’s concept, a set of non-dominated solutions constituting Pareto’s domain. These solutions were then ranked according to the preferences of a decision maker using NFM and RSM. This allowed providing the 10% highest ranked solutions of Pareto’s domain and proposing a set of optimal operating conditions for the production, with the lowest energy consumption, of TPUs with targeted properties and high purity. Experimental validation runs carried out under similar operating conditions gave rise to criteria values confirming the su- perior performance of NFM, without rejecting, at the same time, the values obtained using RSM.展开更多
Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImpr...Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters.展开更多
Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longe...Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.展开更多
基金supported by the“Science and Technology Innovation Action Plan”project of Shanghai in 2021 program(21DZ1207502).
文摘A multi-strategy Improved Multi-Objective Particle Swarm Algorithm(IMOPSO)method for microgrid operation optimization is proposed for the coordinated optimization problem of microgrid economy and environmental protection.A grid-connected microgrid model containing photovoltaic cells,wind power,micro gas turbine,diesel generator,and storage battery is constructed with the aim of optimizing the multi-objective grid-connected microgrid economic optimization problem with minimum power generation cost and environmental management cost.Based on the optimization of the standard multi-objective particle swarm optimization algorithm,four strategies are introduced to improve the algorithm,namely,Logistic chaotic mapping,adaptive inertia weight adjustment,adaptive meshing using congestion distance mechanism,and fuzzy comprehensive evaluation.The proposed IMOPSO is applied to the microgrid optimization problem and the performance is compared with other unimproved multi-objective gray wolf algorithm(MOGWO),multi-objective ant colony algorithm(MOACO),and MOPSO algorithms,and the total cost of the proposed method is reduced by 3.15%,8.34%,and 10.27%,respectively.The simulation results show that IMOPSO can more effectively reduce the cost and optimize power distribution,and verify the effectiveness of the proposed method.
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
基金Project (2012zzts026) supported by the Fundamental Research Funds for the Central Universities,ChinaProject (201205020) supported by Scientific Research Program of Marine Public Welfare Industry of China+2 种基金Project (51074195) supported by the National Natural Science Foundation of ChinaProject (CX2012B123) supported by Research Innovation for Graduate Student of Hunan Province,ChinaProject (12C517) supported by Education Department of Hunan Province,China
文摘Effects of initial pH, temperature, liquid volume, rotation speed, galvanic interaction （pyrite ratio） and pulp density on bioleaching of complex Cu-polymetallic concentrate were investigated. The results indicated that the copper extraction at pH 1.5 was 1.5 and 1.4 times that at pH 1.0 and pH 2.0 respectively. The copper extraction obtained at 45 ℃ was 1236.8%higher than that at 50 ℃. With the increase of rotation speed or the decrease of liquid volume, copper extraction was improved obviously. Copper extraction was improved gradually with the increase of pyrite ratio. However, when the ratio was higher than 20.0%, no further increase in copper extraction was observed. And the statistically significant interactive effects on copper extraction were found between temperature and pH, and temperature and pyrite ratio.
基金Project(2006BAJ05A14)supported by the National Science and Technology Pillar Program During the 11th Five-year Plan Period,ChinaProject(112400430057)supported by the Soft Science Research Program of Henan Province,ChinaProject(B2010-87)supported by the Foundation for Doctorate Research of Henan Polytechnic University,China
文摘Land use structural optimization is an effective approach to realize land sustainable utilization and allocate limited land resource rationally. Grey multiple objectives programming (GMOP) model based on China terrestrial ecosystem service value was constructed and applied to Lilin town. The result shows that GMOP model has more practical applicability and takes ecologic, social and comprehensive benefit into consideration. There are three programs after optimization. Program Ⅰ is comprehensive improvement and constructing ecological economy type, program Ⅱ is gross cultivated land dynamic balance type and program Ⅲ is compromise type. There are still problems in programs Ⅱ and Ⅲ, such as distribution in disorder, land left unused or abandoned. Based on the benefits above, Program Ⅰ>Program Ⅲ> Program Ⅱ. Program Ⅰ is the optimal case. Its comprehensive benefit is 8.43208×107 RMB yuan/a.
基金supported by the National Natural Science Foundation of China(71690233)
文摘Resource allocation for an equipment development task is a complex process owing to the inherent characteristics,such as large amounts of input resources,numerous sub-tasks,complex network structures,and high degrees of uncertainty.This paper presents an investigation into the influence of resource allocation on the duration and cost of sub-tasks.Mathematical models are constructed for the relationships of the resource allocation quantity with the duration and cost of the sub-tasks.By considering the uncertainties,such as fluctuations in the sub-task duration and cost,rework iterations,and random overlaps,the tasks are simulated for various resource allocation schemes.The shortest duration and the minimum cost of the development task are first formulated as the objective function.Based on a multi-objective particle swarm optimization(MOPSO)algorithm,a multi-objective evolutionary algorithm is constructed to optimize the resource allocation scheme for the development task.Finally,an uninhabited aerial vehicle(UAV)is considered as an example of a development task to test the algorithm,and the optimization results of this method are compared with those based on non-dominated sorting genetic algorithm-II(NSGA-II),non-dominated sorting differential evolution(NSDE)and strength pareto evolutionary algorithm-II(SPEA-II).The proposed method is verified for its scientific approach and effectiveness.The case study shows that the optimization of the resource allocation can greatly aid in shortening the duration of the development task and reducing its cost effectively.
基金supported by the National Natural Science Foundation of China(No. 51878216)。
文摘The sulfur-containing odor emitted from sludge composting could be controlled by sulfide oxidizing bacteria, yet mesophilic strains show inactivation during the thermophilic stage of composting. Aimed to investigate and characterize the thermotolerant bacterium that could oxidize sulfide into sulfate, a heterotrophic strain was isolated from sewage sludge composting and identified as Paenibacillus naphthalenovorans LYH-3. The effects of various environmental factors on sulfide oxidation capacities were studied to optimize the sulfate production, and the highest production rate (27.35%±0.86%) was obtained at pH 7.34, the rotation speed of 161.14 r/min, and the inoculation amount of 5.83%by employing BoxBehnken design. The results of serial sulfide substrates experiments indicated that strain LYH-3 could survive up to 400 mg/L of sulfide with the highest sulfide removal rate (88.79%±0.35%) obtained at 50 mg/L of sulfide. Growth kinetic analysis presented the maximum specific growth rateμm(0.5274 hr-1) after 22 hr cultivation at 50℃. The highest enzyme activities of sulfide quinone oxidoreductase (0.369±0.052 U/mg) and sulfur dioxygenase (0.255±0.014 U/mg) were both obtained at 40℃, and the highest enzyme activity of sulfite acceptor oxidoreductase (1.302±0.035 U/mg) was assessed at 50℃. The results indicated that P. naphthalenovorans possessed a rapid growth rate and efficient sulfide oxidation capacities under thermophilic conditions, promising a potential application in controlling sulfur-containing odors during the thermophilic stage of sludge composting.
基金National Natural Science Foundations of China(Nos.61222303,21276078)National High-Tech Research and Development Program of China(No.2012AA040307)+1 种基金New Century Excellent Researcher Award Program from Ministry of Education of China(No.NCET10-0885)the Fundamental Research Funds for the Central Universities and Shanghai Leading Academic Discipline Project,China(No.B504)
文摘Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.
文摘The optimization of nutrient levels for the production of recombinant hyperthermophilie esterase by E. coli was carried out with response surface methodology（RSM） based on the central composite rotatable design（CCRD）. A 24 central composite rotatable design was used to study the combined effect of the nutritional constituents like yeast extract, peptone, mineral salt and trace metals. The P-value of the coefficient for the linear effect of peptone concentration was 0. 0081 and trace metals solution was less than 0. 0001, suggesting that these were the principal variables with significant effect on the hyperthermophilic esterase production. The predicted optimal hyperthermophilie esterase yield was 269. 17 U/mL, whereas an actual experimental value of 284. 58 U/mL was obtained.
基金Project 60304016 supported by the Nationa Natural Science Foundation of China
文摘Multi-project multi-site location problems are multi-objective combinational optimization ones with discrete variables which are hard to solve. To do so, the case of particle swarm optimization is considered due to its useful char- acteristics such as easy implantation, simple parameter settings and fast convergence. First these problems are trans- formed into ones with continuous variables by defining an equivalent probability matrix in this paper, then multi-objective particle swarm optimization based on the minimal particle angle is used to solve them. Methods such as continuation of discrete variables, update of particles for matrix variables, normalization of particle position and evalua- tion of particle fitness are presented. Finally the efficiency of the proposed method is validated by comparing it with other methods on an eight-project-ten-site location problem.
文摘This paper presents the implementation of two multicriteria optimization methods based on different approaches, namely, Rough Set Method (RSM) and Net Flow Method (NFM), to the manufacture by reactive extrusion of linear thermoplastic polyurethanes (TPUs), appropriate for medical applications. A preliminary study allowed determining the process operating conditions for which the polymerization time and the average residence time of the reactants in the extruder are of the same order of magnitude. Prior to the optimization, a neural network model able to predict with acceptable accuracy the effect of the operating conditions on the output process variables, was constructed and validated. This model was then used to determine, using Pareto’s concept, a set of non-dominated solutions constituting Pareto’s domain. These solutions were then ranked according to the preferences of a decision maker using NFM and RSM. This allowed providing the 10% highest ranked solutions of Pareto’s domain and proposing a set of optimal operating conditions for the production, with the lowest energy consumption, of TPUs with targeted properties and high purity. Experimental validation runs carried out under similar operating conditions gave rise to criteria values confirming the su- perior performance of NFM, without rejecting, at the same time, the values obtained using RSM.
文摘Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters.
基金supported by a Horizontal Project on the Development of a Hybrid Energy Storage Simulation Model for Wind Power Based on an RT-LAB Simulation System(PH2023000190)the Inner Mongolia Natural Science Foundation Project and the Optimization of Exergy Efficiency of a Hybrid Energy Storage System with Crossover Control for Wind Power(2023JQ04).
文摘Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.
