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.

A Simplified Machine-Tool Power-Consumption Measurement Procedure and Methodology for Estimating Total Energy Consumption

2015 ◽  
Vol 138 (5) ◽  
Author(s):  
Jang-Yeob Lee ◽  
Yong-Jun Shin ◽  
Min-Soo Kim ◽  
Eun-Seob Kim ◽  
Hae-Sung Yoon ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Machine Tool ◽  
Total Energy ◽  
Machine Tools ◽  
Measurement Procedure ◽  
Single Model ◽  
Total Energy Consumption ◽  
Cutting Energy ◽  
Energy Consumption Model ◽  
Consumption Model

Various methods have been developed to describe the energy consumption of machine tools; however, it remains challenging to accommodate the wide variety of machine tools that exist using a single model. In this paper we propose a method to model the energy consumption of machine tools by decoupling the energy of the components of the machine tool from the cutting energy. A procedure is developed to describe the characteristics of the energy consumption of machine tools, which is applied to six different machines. The experimental results show that the cutting energy can be decoupled from the component energy. In this manner, a simplified energy consumption model is developed that can be applied to a wide variety of different machine tools.

A Study on Energy Consumption Optimization for Cooperative Beamforming in Wireless Sensor Networks

2014 ◽  
Vol 620 ◽  
pp. 625-631
Author(s):  
Guang You Yang ◽  
Xiong Gan ◽  
Tuo Zheng ◽  
Zhi Yan Ma
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Total Energy ◽  
Wireless Sensor ◽  
Total Energy Consumption ◽  
Energy Consumption Optimization ◽  
Modulation Parameter ◽  
Cooperative Beamforming

In wireless sensor networks where the volume and energy of nodes are limited by batteries, which are difficult or prohibitively expensive to replace or recharge in the most of its application scenarios, so improving energy efficiency has very important significance.Cooperative beamforming forms virtual antenna arrays by multiple adjacent wireless sensor nodes, which improves the signal strength at the receiver and reduces the energy consumption of the transmitter by multiplexing gain and interference management.In this paper, the problem of energy consumption optimization for cooperative beamforming in wireless sensor networks was studied. First, considering both amplifier energy consumption and circuit energy consumption,energy consumption models for both broadcast phase and cooperative beamforming phase was presented.Then,we propose a two-step optimization to minimize the total energy consumption by optimizing the modulation parameter and the number of cooperative nodes.We simulate the total energy consumption for various transmission distances,modulation parameters , path losses and the number of cooperative nodes.The numerical results show that,for different system parameters, selecting the optimal modulation parameter and the optimal number of cooperative nodes can reduce total energy consumption and improve energy efficiency.

scholarly journals Energy Consumption Analysis and Optimization of the Deep-Sea Self-Sustaining Profile Buoy

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2316 ◽  
Author(s):  
Mingcong Liu ◽  
Shaobo Yang ◽  
Hongyu Li ◽  
Jiayi Xu ◽  
Xingfei Li
Keyword(s):  
Energy Consumption ◽  
Optimization Model ◽  
Deep Sea ◽  
Kinematic Model ◽  
Control Mode ◽  
Total Energy Consumption ◽  
Nsga Ii ◽  
Energy Consumption Model ◽  
Energy Consumption Optimization ◽  
Static Energy

In order to reduce the energy consumption of deep-sea self-sustaining profile buoy (DSPB) and extend its running time, a stage quantitative oil draining control mode has been proposed in this paper. System parameters have been investigated including oil discharge resolution (ODR), judgment threshold of the floating speed and frequency of oil draining on the energy consumption of the system. The single-objective optimization model with the total energy consumption of DSPB’s ascent stage as the objective function has been established by combining the DSPB’s floating kinematic model. At the same time, as the static working current of the DSPB can be further optimized, a multi-objective energy consumption optimization model with the floating time and the energy consumption of the oil pump motor as objective functions has been established. The non-dominated sorted genetic algorithm-II (NSGA-II) has been employed to optimized the energy consumption model in the ascent stage of the DSPB. The results showed that the NSGA-II method has a good performance in the energy consumption optimization of the DSPB, and can reduce the dynamic energy consumption in the floating process by 28.9% within 2 h considering the increase in static energy consumption.

Energy savings potential of new aeration system: Full scale trials

2012 ◽  
Vol 7 (4) ◽  
Author(s):  
A. Lazić ◽  
V. Larsson ◽  
Å. Nordenborg
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Total Energy ◽  
Energy Savings ◽  
Treatment Plant ◽  
Full Scale ◽  
Total Energy Consumption ◽  
Aeration System ◽  
Fine Bubble ◽  
Aeration Control

The objective of this work is to decrease energy consumption of the aeration system at a mid-size conventional wastewater treatment plant in the south of Sweden where aeration consumes 44% of the total energy consumption of the plant. By designing an energy optimised aeration system (with aeration grids, blowers, controlling valves) and then operating it with a new aeration control system (dissolved oxygen cascade control and most open valve logic) one can save energy. The concept has been tested in full scale by comparing two treatment lines: a reference line (consisting of old fine bubble tube diffusers, old lobe blowers, simple DO control) with a test line (consisting of new Sanitaire Silver Series Low Pressure fine bubble diffusers, a new screw blower and the Flygt aeration control system). Energy savings with the new aeration system measured as Aeration Efficiency was 65%. Furthermore, 13% of the total energy consumption of the whole plant, or 21 000 €/year, could be saved when the tested line was operated with the new aeration system.

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.

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