This research focuses on using BIM modeling optimization to control construction-period risks in the pre-construction stage of industrial factory buildings.It analyzes common risk factors and limitations of traditiona...This research focuses on using BIM modeling optimization to control construction-period risks in the pre-construction stage of industrial factory buildings.It analyzes common risk factors and limitations of traditional approaches.BIM-based methods like collision detection,4D simulation,multi-dimensional data integration,etc.,can effectively mitigate risks.Stakeholder collaboration,digital twin testing,and lean BIM integration is also crucial.Case studies show BIM can reduce risks by 32-41%,with a three phase roadmap provided.展开更多
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ...Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.展开更多
Applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and intelligence.For ethylene thermal cracking,knowledge expression,numerical mo...Applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and intelligence.For ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent manufacturing.This paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking;introduces the frameworks,methods and algorithms that have been proposed over the past10 years and discusses the advantages,limitations and applications in industrial practice.An entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are described.Multilevel control and optimization methods are exhibited,including at the operational,cycle,plant and enterprise level.Relevant software packages are introduced.Finally,an outlook in terms of future directions is presented.展开更多
Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid...Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simultaneous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus allows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization(AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.展开更多
In power plants,flue gases can cause severe corrosion damage in metallic parts such as flue ducts,heat exchangers,and boilers.Coating is an effective technique to prevent this damage.A robust fuzzy model of the surfac...In power plants,flue gases can cause severe corrosion damage in metallic parts such as flue ducts,heat exchangers,and boilers.Coating is an effective technique to prevent this damage.A robust fuzzy model of the surface roughness(Ra and Rz)of flue gas ducts coated by protective composite coating from epoxy and nanoparticles was constructed based on the experimental dataset.The proposed model consists of four nanoparticles(ZnO,ZrO2,SiO2,and NiO)with 2%,4%,6%,and 8%,respectively.Response surface methodology(RSM)was used to optimize the process parameters and identify the optimal conditions for minimum surface roughness of this coated duct.To prove the superiority of the proposed fuzzy model,the model results were compared with those obtained by ANOVA,with the coefficient of determination and the root-mean-square error(RMSE)used as metrics.For Ra,for the first output response,using ANOVA,the coefficient-of-determination values were 0.9137 and 0.4037,respectively,for training and prediction.Similarly,for Rz,the second output response,the coefficient-of-determination results were 0.9695 and 0.4037,respectively,for training and prediction.In the fuzzy modeling of Ra,for the first output response,the RMSE values were 0.0 and 0.1455,respectively,for training and testing.The values for the coefficient of determination were 1.00 and 0.9807,respectively,for training and testing.The results prove the superiority of fuzzy modeling.For modeling the second output response Rz,the RMSE values were 0.0 and 0.0421,respectively,for training and testing,and the coefficient-of-determination values were 1.00 and 0.9959,respectively,for training and testing.展开更多
To enhance system stability,solar collectors have been integrated with air-source heat pumps.This integration facilitates the concurrent utilization of solar and air as energy sources for the system,leading to an impr...To enhance system stability,solar collectors have been integrated with air-source heat pumps.This integration facilitates the concurrent utilization of solar and air as energy sources for the system,leading to an improvement in the system’s heat generation coefficient,overall efficiency,and stability.In this study,we focus on a residential building located in Lhasa as the target for heating purposes.Initially,we simulate and analyze a solar-air source heat pump combined heating system.Subsequently,while ensuring the system meets user requirements,we examine the influence of solar collector installation angles and collector area on the performance of the solar-air source heat pump dual heating system.Through this analysis,we determine the optimal installation angle and collector area to optimize system performance.展开更多
Taking the rubber torsion bushing of a certain type of all-terrain tracked vehicle as the research object,a theoretical model of torsional stiffness was proposed according to the non-linear characteristics of rubber c...Taking the rubber torsion bushing of a certain type of all-terrain tracked vehicle as the research object,a theoretical model of torsional stiffness was proposed according to the non-linear characteristics of rubber components and structural feature of the suspension. Simulations were carried out under different working conditions to obtain root mean square of vertical weighted acceleration as the evaluation index for ride performance of the all-terrain tracked vehicle,with a dynamics model of the whole vehicle based on the theoretical model of the torsional stiffness and standard road roughness as excitation input. Response surface method was used to establish the parametric optimization model of the torsional stiffness. The evaluation index showed that ride performance of the vehicle with optimized torsional stiffness model of suspension was improved compared with previous model fromexperiment. The torsional stiffness model of rubber bushing provided a theoretical basis for the design of the rubber torsion bushing in light tracked vehicles.展开更多
In this paper, we conduct research on the multidimensional constraint stability of bridge structure modeling based on the optimization model. The current internal and the external research results to the truss web str...In this paper, we conduct research on the multidimensional constraint stability of bridge structure modeling based on the optimization model. The current internal and the external research results to the truss web structure, the high internode the aspect ratio and the stiffness of the middle truss brace of the truss web, deffection of composite beams of the impact of stress is a very important problem in the design of the bridge. Structural health monitoring is the use of the field of the non-destructive sensing technology, including the structural response, including structural system characteristics analysis, to achieve the purpose of monitoring structural damage or degradation. Under this basis, this paper proposes the new idea on the modelling and simulates the performance.展开更多
With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ...With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.展开更多
With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learnin...With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learning methods.Therefore,in the process of reforming and developing higher education,it is essential to take digital technology empowering the optimization of the education industry as a breakthrough,focusing on five key areas:the construction of smart classrooms,the digital integration of teaching resources,the development of personalized learning support systems,the reform of online-offline hybrid teaching,and the intelligentization of educational management.This paper also examines the experiences,challenges,and shortcomings of typical universities in using digital technology to improve teaching quality,optimize resource allocation,and innovate teaching management models.Finally,corresponding countermeasures and suggestions are proposed to facilitate the smooth implementation of digital transformation in higher education institutions.展开更多
With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reductio...With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.展开更多
Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive ...Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive research,existing approaches often face algorithmic limitations such as slow convergence,premature stagnation in local minima,or suboptimal accuracy in determining optimal DG placement and capacity.This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement.It integrates both quantitative and qualitative analyses of the scholarly landscape,mapping influential research domains,co-authorship structures,the articles’citation networks,keyword clusters,and international collaboration patterns.Moreover,the study classifies and evaluates the most prominent objective functions,key computational models and optimization algorithms,DG technologies,and strategic approaches employed in the field.The findings reveal that advanced algorithmic frameworks substantially enhance network stability,minimize real power losses,and improve voltage profiles under various operational constraints.This review serves as a foundational resource for researchers and practitioners,highlighting emerging algorithmic trends,modelling innovations,and data-driven methodologies that can guide future development of intelligent,optimization-based DG integration strategies in smart distribution systems.展开更多
The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable deve...The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable development,oilfield reconstruction was usually conducted in discrete rather than continuous space.Motivated by economic and sustainability goals,a 3-phase heuristic model for oilfield reconstruction was developed to mine potential locations in continuous space.In phase 1,considering the process characteristics of the oil and gas gathering system,potential locations were mined in continuous space.In phase 2,incorporating comprehensive reconstruction measures,a reconstruction model was established in discrete space.In phase 3,the topology was further adjusted in continuous space.Subsequently,the model was transformed into a single-objective mixed integer linear programming model using the augmented ε-constraint method.Numerical experiments revealed that the small number of potential locations could effectively reduce the reconstruction cost,and the quality of potential locations mined in phase 1 surpassed those generated in random or grid form.Case studies showed that cost and carbon emissions for a new block were reduced by up to 10.45% and 7.21 %,respectively.These reductions were because the potential locations mined in 1P reduced the number of metering stations,and 3P adjusted the locations of metering stations in continuous space to shorten the pipeline length.For an old oilfield,the load and connection ratios of the old metering station increased to 89.7% and 94.9%,respectively,enhancing operation efficiency.Meanwhile,recycling facilitated the diversification of reconstruction measures and yielded a profit of 582,573 ¥,constituting 5.56% of the total cost.This study adopted comprehensive reconstruction measures and tapped into potential reductions in cost and carbon emissions for oilfield reconstruction,offering valuable insights for future oilfield design and construction.展开更多
The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of...The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.展开更多
For high-purity distillation processes,it is difficult to achieve a good direct product quality control using traditional proportional-integral-differential(PID)control or multivariable predictive control technique du...For high-purity distillation processes,it is difficult to achieve a good direct product quality control using traditional proportional-integral-differential(PID)control or multivariable predictive control technique due to some difficulties,such as long response time,many un-measurable disturbances,and the reliability and precision issues of product quality soft-sensors.In this paper,based on the first principle analysis and dynamic simulation of a distillation process,a new predictive control scheme is proposed by using the split ratio of distillate flow rate to that of bottoms as an essential controlled variable.Correspondingly,a new strategy with integrated control and on-line optimization is developed,which consists of model predictive control of the split ratio,surrogate model based on radial basis function neural network for optimization,and modified differential evolution optimization algorithm. With the strategy,the process achieves its steady state quickly,so more profit can be obtained.The proposed strategy has been successfully applied to a gas separation plant for more than three years,which shows that the strategy is feasible and effective.展开更多
Based on the optimization method, a new modified GM (1,1) model is presented, which is characterized by more accuracy prediction for the grey modeling.
An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main f...An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main factors influencing the selection of mining method were taken into account,and the comprehensive evaluation index system of mining method selection was constructed.The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively.New measurement standards were constructed.Then,the unascertained measurement function of each evaluation index was established.The index weights of the factors were calculated by entropy theory,and credible degree recognition criteria were established according to the unascertained measurement theory.The results of mining method evaluation were obtained using the credible degree criteria,thus the best underground mining method was determined.Furthermore,this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China.The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model,so the optimal method can be easily determined.Meanwhile,the proposed method can take into account large amount of uncertain information in mining method selection,which can provide an effective way for selecting the optimal underground mining method.展开更多
The objective and constraint functions related to structural optimization designs are classified into economic and performance indexes in this paper.The influences of their different roles in model construction of str...The objective and constraint functions related to structural optimization designs are classified into economic and performance indexes in this paper.The influences of their different roles in model construction of structural topology optimization are also discussed.Furthermore,two structural topology optimization models,optimizing a performance index under the limitation of an economic index,represented by the minimum compliance with a volume constraint（MCVC）model,and optimizing an economic index under the limitation of a performance index,represented by the minimum weight with a displacement constraint（MWDC）model,are presented.Based on a comparison of numerical example results,the conclusions can be summarized as follows：（1）under the same external loading and displacement performance conditions,the results of the MWDC model are almost equal to those of the MCVC model;（2）the MWDC model overcomes the difficulties and shortcomings of the MCVC model;this makes the MWDC model more feasible in model construction;（3）constructing a model of minimizing an economic index under the limitations of performance indexes is better at meeting the needs of practical engineering problems and completely satisfies safety and economic requirements in mechanical engineering,which have remained unchanged since the early days of mechanical engineering.展开更多
Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system....Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system. A dynamic optimization model of the supply chain is developed. It has achieved optimal system profit under conditions guaranteeing a certain level of customer satisfaction. Applying this model to coal production of the Xuzhou coal mines allows recommendations for a more systematic use of washing and processing,transportation and sale resources for commercial coal production to be made. The results show that this model,which is scientific and effective,has an important value for making reasonable decisions related to complex coal enterprises.展开更多
文摘This research focuses on using BIM modeling optimization to control construction-period risks in the pre-construction stage of industrial factory buildings.It analyzes common risk factors and limitations of traditional approaches.BIM-based methods like collision detection,4D simulation,multi-dimensional data integration,etc.,can effectively mitigate risks.Stakeholder collaboration,digital twin testing,and lean BIM integration is also crucial.Case studies show BIM can reduce risks by 32-41%,with a three phase roadmap provided.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
文摘Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.
基金The authors gratefully acknowledge the National Natural Science Foundation of China for its financial support(U1462206).
文摘Applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and intelligence.For ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent manufacturing.This paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking;introduces the frameworks,methods and algorithms that have been proposed over the past10 years and discusses the advantages,limitations and applications in industrial practice.An entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are described.Multilevel control and optimization methods are exhibited,including at the operational,cycle,plant and enterprise level.Relevant software packages are introduced.Finally,an outlook in terms of future directions is presented.
基金Supported by the Specialized Research Fund for the Doctoral Program of Higher Education(No.20120042120014)
文摘Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simultaneous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus allows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization(AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.
文摘In power plants,flue gases can cause severe corrosion damage in metallic parts such as flue ducts,heat exchangers,and boilers.Coating is an effective technique to prevent this damage.A robust fuzzy model of the surface roughness(Ra and Rz)of flue gas ducts coated by protective composite coating from epoxy and nanoparticles was constructed based on the experimental dataset.The proposed model consists of four nanoparticles(ZnO,ZrO2,SiO2,and NiO)with 2%,4%,6%,and 8%,respectively.Response surface methodology(RSM)was used to optimize the process parameters and identify the optimal conditions for minimum surface roughness of this coated duct.To prove the superiority of the proposed fuzzy model,the model results were compared with those obtained by ANOVA,with the coefficient of determination and the root-mean-square error(RMSE)used as metrics.For Ra,for the first output response,using ANOVA,the coefficient-of-determination values were 0.9137 and 0.4037,respectively,for training and prediction.Similarly,for Rz,the second output response,the coefficient-of-determination results were 0.9695 and 0.4037,respectively,for training and prediction.In the fuzzy modeling of Ra,for the first output response,the RMSE values were 0.0 and 0.1455,respectively,for training and testing.The values for the coefficient of determination were 1.00 and 0.9807,respectively,for training and testing.The results prove the superiority of fuzzy modeling.For modeling the second output response Rz,the RMSE values were 0.0 and 0.0421,respectively,for training and testing,and the coefficient-of-determination values were 1.00 and 0.9959,respectively,for training and testing.
文摘To enhance system stability,solar collectors have been integrated with air-source heat pumps.This integration facilitates the concurrent utilization of solar and air as energy sources for the system,leading to an improvement in the system’s heat generation coefficient,overall efficiency,and stability.In this study,we focus on a residential building located in Lhasa as the target for heating purposes.Initially,we simulate and analyze a solar-air source heat pump combined heating system.Subsequently,while ensuring the system meets user requirements,we examine the influence of solar collector installation angles and collector area on the performance of the solar-air source heat pump dual heating system.Through this analysis,we determine the optimal installation angle and collector area to optimize system performance.
文摘Taking the rubber torsion bushing of a certain type of all-terrain tracked vehicle as the research object,a theoretical model of torsional stiffness was proposed according to the non-linear characteristics of rubber components and structural feature of the suspension. Simulations were carried out under different working conditions to obtain root mean square of vertical weighted acceleration as the evaluation index for ride performance of the all-terrain tracked vehicle,with a dynamics model of the whole vehicle based on the theoretical model of the torsional stiffness and standard road roughness as excitation input. Response surface method was used to establish the parametric optimization model of the torsional stiffness. The evaluation index showed that ride performance of the vehicle with optimized torsional stiffness model of suspension was improved compared with previous model fromexperiment. The torsional stiffness model of rubber bushing provided a theoretical basis for the design of the rubber torsion bushing in light tracked vehicles.
文摘In this paper, we conduct research on the multidimensional constraint stability of bridge structure modeling based on the optimization model. The current internal and the external research results to the truss web structure, the high internode the aspect ratio and the stiffness of the middle truss brace of the truss web, deffection of composite beams of the impact of stress is a very important problem in the design of the bridge. Structural health monitoring is the use of the field of the non-destructive sensing technology, including the structural response, including structural system characteristics analysis, to achieve the purpose of monitoring structural damage or degradation. Under this basis, this paper proposes the new idea on the modelling and simulates the performance.
基金funded by Jilin Province Science and Technology Development Plan Project,grant number 20220203163SF.
文摘With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.
文摘With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learning methods.Therefore,in the process of reforming and developing higher education,it is essential to take digital technology empowering the optimization of the education industry as a breakthrough,focusing on five key areas:the construction of smart classrooms,the digital integration of teaching resources,the development of personalized learning support systems,the reform of online-offline hybrid teaching,and the intelligentization of educational management.This paper also examines the experiences,challenges,and shortcomings of typical universities in using digital technology to improve teaching quality,optimize resource allocation,and innovate teaching management models.Finally,corresponding countermeasures and suggestions are proposed to facilitate the smooth implementation of digital transformation in higher education institutions.
文摘With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number:IMSIU-DDRSP2503)。
文摘Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive research,existing approaches often face algorithmic limitations such as slow convergence,premature stagnation in local minima,or suboptimal accuracy in determining optimal DG placement and capacity.This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement.It integrates both quantitative and qualitative analyses of the scholarly landscape,mapping influential research domains,co-authorship structures,the articles’citation networks,keyword clusters,and international collaboration patterns.Moreover,the study classifies and evaluates the most prominent objective functions,key computational models and optimization algorithms,DG technologies,and strategic approaches employed in the field.The findings reveal that advanced algorithmic frameworks substantially enhance network stability,minimize real power losses,and improve voltage profiles under various operational constraints.This review serves as a foundational resource for researchers and practitioners,highlighting emerging algorithmic trends,modelling innovations,and data-driven methodologies that can guide future development of intelligent,optimization-based DG integration strategies in smart distribution systems.
基金supported by the National Natural Science Foundation of China (Grant No.52174065)the National Natural Science Foundation of China (Grant No.52304071)+1 种基金China University of Petroleum,Beijing (Grant No.ZX20220040)MOE Key Laboratory of Petroleum Engineering (China University of Petroleum,No.2462024PTJS002)。
文摘The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable development,oilfield reconstruction was usually conducted in discrete rather than continuous space.Motivated by economic and sustainability goals,a 3-phase heuristic model for oilfield reconstruction was developed to mine potential locations in continuous space.In phase 1,considering the process characteristics of the oil and gas gathering system,potential locations were mined in continuous space.In phase 2,incorporating comprehensive reconstruction measures,a reconstruction model was established in discrete space.In phase 3,the topology was further adjusted in continuous space.Subsequently,the model was transformed into a single-objective mixed integer linear programming model using the augmented ε-constraint method.Numerical experiments revealed that the small number of potential locations could effectively reduce the reconstruction cost,and the quality of potential locations mined in phase 1 surpassed those generated in random or grid form.Case studies showed that cost and carbon emissions for a new block were reduced by up to 10.45% and 7.21 %,respectively.These reductions were because the potential locations mined in 1P reduced the number of metering stations,and 3P adjusted the locations of metering stations in continuous space to shorten the pipeline length.For an old oilfield,the load and connection ratios of the old metering station increased to 89.7% and 94.9%,respectively,enhancing operation efficiency.Meanwhile,recycling facilitated the diversification of reconstruction measures and yielded a profit of 582,573 ¥,constituting 5.56% of the total cost.This study adopted comprehensive reconstruction measures and tapped into potential reductions in cost and carbon emissions for oilfield reconstruction,offering valuable insights for future oilfield design and construction.
基金funded by the National Key Research and Development Program of China(2024YFE0106800)Natural Science Foundation of Shandong Province(ZR2021ME199).
文摘The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.
基金Supported by the National High Technology Research and Development Program of China（2007AA04Z193） the National Natural Science Foundation of China（60974008 60704032）
文摘For high-purity distillation processes,it is difficult to achieve a good direct product quality control using traditional proportional-integral-differential(PID)control or multivariable predictive control technique due to some difficulties,such as long response time,many un-measurable disturbances,and the reliability and precision issues of product quality soft-sensors.In this paper,based on the first principle analysis and dynamic simulation of a distillation process,a new predictive control scheme is proposed by using the split ratio of distillate flow rate to that of bottoms as an essential controlled variable.Correspondingly,a new strategy with integrated control and on-line optimization is developed,which consists of model predictive control of the split ratio,surrogate model based on radial basis function neural network for optimization,and modified differential evolution optimization algorithm. With the strategy,the process achieves its steady state quickly,so more profit can be obtained.The proposed strategy has been successfully applied to a gas separation plant for more than three years,which shows that the strategy is feasible and effective.
文摘Based on the optimization method, a new modified GM (1,1) model is presented, which is characterized by more accuracy prediction for the grey modeling.
基金Project(2007CB209402) supported by the National Basic Research Program of China Project(SKLGDUEK0906) supported by the Research Fund of State Key Laboratory for Geomechanics and Deep Underground Engineering of China
文摘An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main factors influencing the selection of mining method were taken into account,and the comprehensive evaluation index system of mining method selection was constructed.The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively.New measurement standards were constructed.Then,the unascertained measurement function of each evaluation index was established.The index weights of the factors were calculated by entropy theory,and credible degree recognition criteria were established according to the unascertained measurement theory.The results of mining method evaluation were obtained using the credible degree criteria,thus the best underground mining method was determined.Furthermore,this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China.The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model,so the optimal method can be easily determined.Meanwhile,the proposed method can take into account large amount of uncertain information in mining method selection,which can provide an effective way for selecting the optimal underground mining method.
基金supported by the National Natural Science Foundation of China(Grant 11172013)
文摘The objective and constraint functions related to structural optimization designs are classified into economic and performance indexes in this paper.The influences of their different roles in model construction of structural topology optimization are also discussed.Furthermore,two structural topology optimization models,optimizing a performance index under the limitation of an economic index,represented by the minimum compliance with a volume constraint（MCVC）model,and optimizing an economic index under the limitation of a performance index,represented by the minimum weight with a displacement constraint（MWDC）model,are presented.Based on a comparison of numerical example results,the conclusions can be summarized as follows：（1）under the same external loading and displacement performance conditions,the results of the MWDC model are almost equal to those of the MCVC model;（2）the MWDC model overcomes the difficulties and shortcomings of the MCVC model;this makes the MWDC model more feasible in model construction;（3）constructing a model of minimizing an economic index under the limitations of performance indexes is better at meeting the needs of practical engineering problems and completely satisfies safety and economic requirements in mechanical engineering,which have remained unchanged since the early days of mechanical engineering.
文摘Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system. A dynamic optimization model of the supply chain is developed. It has achieved optimal system profit under conditions guaranteeing a certain level of customer satisfaction. Applying this model to coal production of the Xuzhou coal mines allows recommendations for a more systematic use of washing and processing,transportation and sale resources for commercial coal production to be made. The results show that this model,which is scientific and effective,has an important value for making reasonable decisions related to complex coal enterprises.
