Energy Consumption Model of Hoisting Mechanism of Bridge and Gantry Crane Based on Bond Graph Theory

2020 ◽  
Vol 09 (04) ◽  
pp. 542-552
Author(s):  
华 赵
Keyword(s):  
Graph Theory ◽  
Energy Consumption ◽  
Bond Graph ◽  
Gantry Crane ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
Bond Graph Theory

Dynamic characteristics and energy consumption modelling of machine tools based on bond graph theory

Energy ◽  
2020 ◽  
Vol 212 ◽  
pp. 118767
Author(s):  
Wei Liu ◽  
Li Li ◽  
Wei Cai ◽  
Congbo Li ◽  
Lingling Li ◽  
...  
Keyword(s):  
Graph Theory ◽  
Energy Consumption ◽  
Dynamic Characteristics ◽  
Machine Tools ◽  
Bond Graph ◽  
Bond Graph Theory

scholarly journals Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 655
Author(s):  
Huanhuan Zhang ◽  
Jigeng Li ◽  
Mengna Hong
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Gradient Boosting ◽  
Paper Machine ◽  
Steam Consumption ◽  
Tissue Paper ◽  
Paper Machines ◽  
Energy Consumption Model ◽  
Extreme Gradient Boosting ◽  
Consumption Model

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

scholarly journals Comprehensive Energy Consumption Model for Unmanned Aerial Vehicles, Based on Empirical Studies of Battery Performance

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 58383-58394 ◽  
Author(s):  
Hasini Viranga Abeywickrama ◽  
Beeshanga Abewardana Jayawickrama ◽  
Ying He ◽  
Eryk Dutkiewicz
Keyword(s):  
Energy Consumption ◽  
Unmanned Aerial Vehicles ◽  
Empirical Studies ◽  
Aerial Vehicles ◽  
Energy Consumption Model ◽  
Consumption Model

scholarly journals Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot Joints

2018 ◽  
Vol 2018 ◽  
pp. 1-26
Author(s):  
Ying He ◽  
Jiangping Mei ◽  
Zhiwei Fang ◽  
Fan Zhang ◽  
Yanqin Zhao
Keyword(s):  
Fourier Series ◽  
Energy Consumption ◽  
Total Energy ◽  
Dynamic Model ◽  
Trajectory Optimization ◽  
Joint Torque ◽  
Total Energy Consumption ◽  
Energy Consumption Model ◽  
Energy Consumption Optimization ◽  
Consumption Model

Palletizing robot is widely used in logistics operation. At present, people pay attention to not only the loading capacity and working efficiency of palletizing robots, but also the energy consumption in their working process. This paper takes MD1200-YJ palletizing robot as the research object and studies the problem of low energy consumption optimization of joint driving system from the perspective of trajectory optimization. Firstly, a multifactor dynamic model of palletizing robot is established based on the conventional inverse rigid body dynamic model of the robot, the Stribeck friction model and the spring balance torque model. And then based on joint torque, friction torque, motion parameter, and joule’s law, the useful work model, thermal loss model of joint motor, friction energy consumption model of joint system, and total energy consumption model of palletizing robot are established, and through simulation and experiment, the correctness of the multifactor dynamic model and energy consumption model is verified. Secondly, based on the Fourier series approximation method to construct the joint trajectory expression, the minimum total energy consumption as the optimization objective, with coefficients of Fourier series as optimization variables, the motion parameters of initial and final position, and running time constant as constraint conditions, the genetic algorithm is used to solve the optimization problem. Finally, through the simulation analysis the optimized Fourier series motion law and the 3-4-5 polynomial motion law are comprehensively evaluated to verify the effectiveness of the optimization method. Moreover, it provides the theoretical basis for the follow-up research and points out the direction of improvement.

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