A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing

2009 ◽  
Vol 2 (2) ◽  
pp. 123-133 ◽  
Author(s):  
Anton Dietmair ◽  
Alexander Verl
Keyword(s):  
Decision Making ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Consumption Model ◽  
Consumption Model

scholarly journals Energy Consumption Model for Drilling Processes Based on Cutting Force

2019 ◽  
Vol 9 (22) ◽  
pp. 4801
Author(s):  
Qi Wang ◽  
Dinghua Zhang ◽  
Bing Chen ◽  
Ying Zhang ◽  
Baohai Wu
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Cutting Force ◽  
Drilling Process ◽  
Cutting Power ◽  
Energy Models ◽  
Auxiliary Power ◽  
Proposed Model ◽  
Energy Consumption Model ◽  
Consumption Model

Accurate energy consumption modelling is critical for the improvement of energy efficiency in machining. Existing energy models of machining processes mainly focus on turning or milling, and there are few energy models for drilling. However, since drilling is often applied to roughing and semi-finishing, and the cutting parameters are large, the energy consumption is huge, and it is urgent to study the consumption of energy during the drilling process. In this paper, an energy consumption model for drilling processes was proposed. Idle power, cutting power, and auxiliary power were included in the proposed energy consumption model, using the cutting force to obtain the cutting power during drilling. Further, the relationship between cutting power and auxiliary power was analyzed. Cutting experiments were then carried out which confirmed the correctness of the proposed model. In addition, compared with several existing energy consumption models, the proposed model had better accuracy and applicability. It is expected that the proposed energy consumption model will have applications for the minimization of energy consumption and improvement of energy efficiency but not limited to only drilling energy consumption prediction.

Urban Building Energy Consumption Forecast Based on the IPAT Theory

2013 ◽  
Vol 689 ◽  
pp. 482-486
Author(s):  
Cui Zou ◽  
Xiu Li Liu
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Building Energy ◽  
Building Energy Consumption ◽  
The Public ◽  
Urban Buildings ◽  
Efficiency Measures ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
Energy Consumption Forecast

This paper established an urban building energy consumption model based on IPAT theory combined with ARMA method. Applying the model, the paper calculated the urban building energy consumption in different scenarios, and then predicted the amount of urban building energy consumption in China during 2011-2015. The amount of Chinese urban building energy consumption would grow rapidly during this period, and would reach about 895 million tons of standard coal in 2015. Results analysis showed that it would be necessary to promote energy- efficiency measures in urban buildings, especially in the public buildings.

scholarly journals A Systematic Method of Optimization of Machining Parameters Considering Energy Consumption, Machining time and Surface Roughness with Experimental Analysis

2021 ◽  
Author(s):  
Chunhua Feng ◽  
Haohao Guo ◽  
Jingyang Zhang ◽  
Yugui Huang ◽  
Shi Huang
Keyword(s):  
Experimental Data ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Systematic Method ◽  
Machining Time ◽  
Cutting Energy ◽  
Energy Consumption Model ◽  
Consumption Model

Abstract For improving energy efficiency of machining process, extensive studies have focused on how to establish energy consumption model and optimize cutting parameters. However, the existing methods lack a systematic method to promote the widespread use of energy efficiency methods in the industry. This paper proposes a systematic method integrating energy model, experiment design, and multi-objective optimization model. Firstly, the energy model is established considering cutting energy and non-cutting energy. Then, the orthogonal experiment is designed with the three levels of four factors of spindle speed, feed speed, cutting depth, and cutting width in the X and Y cutting directions. The data of energy consumption, surface quality and machining time are obtained to study the effects of different cutting elements and cutting directions. Meanwhile, the standby, spindle idling, feed, SEC, material cutting and idling feed models of the CNC machine tools are established based on the experimental data. In addition, for verifying the accuracy of the established energy consumption model, five sets of experimental data are tested that show the prediction accuracy can reach 99.4%. Finally, a multi-objective optimization model for high efficiency and energy saving of processing process is establishes to optimize the cutting parameters from the three perspectives of energy consumption, processing time and surface quality. Combining the case of milling with constraints including machine tool performance, tool life, processing procedures, and processing requirements, the Pareto solution set is used to solve the Pareto of the target model. Through drawing a three-dimensional needle graph and two-dimensional histogram, the optimal cutting parameter combination for rough machining and semi-finish machining are provided, assisting in promoting the application of the sustainable techniques in the industry.

Qos Constraints-Based Energy-Efficient Model for IP Networks

2013 ◽  
Vol 765-767 ◽  
pp. 1747-1751
Author(s):  
Ding De Jiang ◽  
Wen Juan Wang ◽  
Wei Han Zhang ◽  
Peng Zhang ◽  
Ya Li
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Large Scale ◽  
Ip Networks ◽  
Speed Scaling ◽  
Model Based ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
Simulation Results

This paper proposes an energy-efficient model to overcome the energy-efficient problem in large-scale IP networks, based on QoS constraints. To characterize network energy consumption, we present a link energy consumption model based on the sleep and speed scaling mechanisms. If there is no traffic on a link, let it sleep, or activate it and divide its energy consumption into base energy consumption and traffic energy consumption. And then according to the link energy consumption model, we can build our energy-efficient model to improve the network energy efficiency. Finally, simulation results show that our model can significantly improve the network energy efficiency.

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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