The samples obtained by Finite Element Method (FEM) simulation for section extrusion process have been trained on BP Neural Networks. The mapping relationsbetween die's geometrical parameters and energetic paramet...The samples obtained by Finite Element Method (FEM) simulation for section extrusion process have been trained on BP Neural Networks. The mapping relationsbetween die's geometrical parameters and energetic parameters, such as stress and strain generated in the die are established. The extrusion process model and its expert system are also determined. The excellent expansibility this system possesses provides a new prospect for the future development of expert system for section extrusion dies.展开更多
Adaptive reuse in urban centers aims to achieve net-zero energy goals by lowering energy consumption and improving thermal comfort in existing buildings.The combined effects of building expansions on energy performanc...Adaptive reuse in urban centers aims to achieve net-zero energy goals by lowering energy consumption and improving thermal comfort in existing buildings.The combined effects of building expansions on energy performance,and daylighting availability remain unexplored.This paper developed a novel simulation model by applying multi-building data and neural-networks framework to examine the impact of adaptive reuse through variables including number of floors,energy generation,façade glazing,and building expansions in various directions.The developed model was validated by comparing simulated and actual energy use of several buildings,yielding an average error of 7.88%.This error represents the deviation between the simulated and actual energy use intensity values.Energy demand reduced by expansion along the East-West axis was 41%greater than that from expansion in the South direction.This was confirmed by sensitivity analysis,with R values of approximately 0.68 for East and West expansions,and 0.16 for the South.Overall,this study demonstrates that expanding buildings in the East-West direction tends to be the most energy-efficient approach for increasing occupied spaces,with its effectiveness potentially influenced by factors such as site location,building orientation,and climatic conditions.展开更多
The mining industry consumes an enormous amount of energy globally,the main part of which is conservable.Diesel is a key source of energy in mining operations,and mine locomotives have significant diesel consumption.T...The mining industry consumes an enormous amount of energy globally,the main part of which is conservable.Diesel is a key source of energy in mining operations,and mine locomotives have significant diesel consumption.Train speed has been recognized as the primary parameter affecting locomotive fuel consumption.In this study,an artificial intelligence(AI)look-forward control is developed as an online method for energy-efficiency improvement in mine-railway operation.An AI controller will modify the desired train-speed profile by accounting for the grade resistance and speed limits of the route ahead.Travel-time increment is applied as an improvement constraint.Recent models for mine-train-movement simulation have estimated locomotive fuel burn using an indirect index.An AI-developed algorithm for mine-train-movement simulation can correctly predict locomotive diesel consumption based on the considered values of the transfer parameters in this paper.This algorithm finds the mine-locomotive subsystems,and satisfies the practical diesel-consumption data specified in the locomotive’s manufacturer catalog.The model developed in this study has two main sections designed to estimate locomotive fuel consumption in different situations by using an artificial neural network(ANN),and an optimization section that applies a genetic algorithm(GA)to optimize train speed for the purpose of minimizing locomotive diesel consumption.The AI model proposed in this paper is learned and validated using real datasets collected from a mine-railway route in Western Australia.The simulation of a mine train with a commonly used locomotive in Australia GeneralMotors SD40-2(GM SD40-2)on a local railway track illustrates a significant reduction in diesel consumption along with a satisfactory travel-time increment.The simulation results also demonstrate that the AI look-forward controller has faster calculations than control systems based that use dynamic programming.展开更多
文摘The samples obtained by Finite Element Method (FEM) simulation for section extrusion process have been trained on BP Neural Networks. The mapping relationsbetween die's geometrical parameters and energetic parameters, such as stress and strain generated in the die are established. The extrusion process model and its expert system are also determined. The excellent expansibility this system possesses provides a new prospect for the future development of expert system for section extrusion dies.
基金supported by the Government of Canada’s New Frontiers in Research Fund,through the three federal research funding agencies(CIHR,NSERC,and SSHRC).
文摘Adaptive reuse in urban centers aims to achieve net-zero energy goals by lowering energy consumption and improving thermal comfort in existing buildings.The combined effects of building expansions on energy performance,and daylighting availability remain unexplored.This paper developed a novel simulation model by applying multi-building data and neural-networks framework to examine the impact of adaptive reuse through variables including number of floors,energy generation,façade glazing,and building expansions in various directions.The developed model was validated by comparing simulated and actual energy use of several buildings,yielding an average error of 7.88%.This error represents the deviation between the simulated and actual energy use intensity values.Energy demand reduced by expansion along the East-West axis was 41%greater than that from expansion in the South direction.This was confirmed by sensitivity analysis,with R values of approximately 0.68 for East and West expansions,and 0.16 for the South.Overall,this study demonstrates that expanding buildings in the East-West direction tends to be the most energy-efficient approach for increasing occupied spaces,with its effectiveness potentially influenced by factors such as site location,building orientation,and climatic conditions.
文摘The mining industry consumes an enormous amount of energy globally,the main part of which is conservable.Diesel is a key source of energy in mining operations,and mine locomotives have significant diesel consumption.Train speed has been recognized as the primary parameter affecting locomotive fuel consumption.In this study,an artificial intelligence(AI)look-forward control is developed as an online method for energy-efficiency improvement in mine-railway operation.An AI controller will modify the desired train-speed profile by accounting for the grade resistance and speed limits of the route ahead.Travel-time increment is applied as an improvement constraint.Recent models for mine-train-movement simulation have estimated locomotive fuel burn using an indirect index.An AI-developed algorithm for mine-train-movement simulation can correctly predict locomotive diesel consumption based on the considered values of the transfer parameters in this paper.This algorithm finds the mine-locomotive subsystems,and satisfies the practical diesel-consumption data specified in the locomotive’s manufacturer catalog.The model developed in this study has two main sections designed to estimate locomotive fuel consumption in different situations by using an artificial neural network(ANN),and an optimization section that applies a genetic algorithm(GA)to optimize train speed for the purpose of minimizing locomotive diesel consumption.The AI model proposed in this paper is learned and validated using real datasets collected from a mine-railway route in Western Australia.The simulation of a mine train with a commonly used locomotive in Australia GeneralMotors SD40-2(GM SD40-2)on a local railway track illustrates a significant reduction in diesel consumption along with a satisfactory travel-time increment.The simulation results also demonstrate that the AI look-forward controller has faster calculations than control systems based that use dynamic programming.
