Energy-consumption model for Chinese highway maintenance based on Life Cycle Assessment

The product life cycle energy consumption model was established considering the impact of remanufacturing on the product lifecycle, and the energy consumption quantitative method was given. In order to optimize the life of a product, a method to calculate its life cycle critical point was proposed. The energy consumption model was applied to two types of gearboxes, new and remanufactured, to compare their life cycle energy, and the energy-saving design scheme optimization was achieved.

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Application Calibration Based on Energy Consumption Model in Optimal Design of Green Buildings

Advances in Materials Science and Engineering ◽

10.1155/2021/5360443 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Feiran Xue ◽

Jingyuan Zhao

Keyword(s):

Sustainable Development ◽

Life Cycle ◽

Energy Consumption ◽

Energy Saving ◽

Energy Management ◽

Green Buildings ◽

Load Test ◽

Energy Consumption Model ◽

Consumption Model

Buildings will generate considerable resource and energy consumption, environmental impact, and costs throughout their entire life cycle. Life cycle assessment and life cycle cost methods have become widely used building sustainability evaluation methods internationally due to their long-term comprehensive evaluation perspectives and scientific quantitative calculation methods. In response to energy consumption management issues in green buildings, a questionnaire survey was conducted to investigate the concepts of sustainable development, green environmental protection, and energy consumption management of construction enterprise personnel. Through the survey, it can be seen that corporate executives generally have a sense of sustainable development and pay more attention to sustainable competition and the green development of enterprises. Approximately 90% of executives understand energy consumption models. Only 10% of employees do not understand, but they are acquired by the company’s frontline employees. The result of the feedback is just the opposite. Frontline workers pay little attention to the long-term development of the company. Only about 5% of employees are proficient in the energy management system, but the proportion of completely unknown is as high as 20%, indicating that the overall energy management awareness of Chinese enterprises still needs to be strengthened. Secondly, through design experiments, the energy-saving management effect of the energy consumption model was observed and analyzed. From the power saving and load test, we found that the monitoring of the energy consumption model is helpful for companies to choose more energy-efficient building circuit designs, achieving an average annual energy-saving effect of about 8%, so as to achieve the purpose of energy saving.

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Energy Consumption Model of Diamond Disc Sawing Machine with Variable Material Removal Rate

2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) ◽

10.1109/wcmeim52463.2020.00134 ◽

2020 ◽

Author(s):

Liqiang Luan ◽

Shuowei Bai

Keyword(s):

Energy Consumption ◽

Material Removal Rate ◽

Material Removal ◽

Removal Rate ◽

Energy Consumption Model ◽

Consumption Model ◽

Diamond Disc

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Life-Cycle Assessment of Carbon Footprint of Bike-Share and Bus Systems in Campus Transit

Sustainability ◽

10.3390/su13010158 ◽

2020 ◽

Vol 13 (1) ◽

pp. 158

Author(s):

Sishen Wang ◽

Hao Wang ◽

Pengyu Xie ◽

Xiaodan Chen

Keyword(s):

Life Cycle Assessment ◽

Life Cycle ◽

Energy Consumption ◽

Carbon Footprint ◽

Co2 Emission ◽

Raw Material ◽

Transit System ◽

Two Systems ◽

Bus System ◽

Bike Share

Low-carbon transport system is desired for sustainable cities. The study aims to compare carbon footprint of two transportation modes in campus transit, bus and bike-share systems, using life-cycle assessment (LCA). A case study was conducted for the four-campus (College Ave, Cook/Douglass, Busch, Livingston) transit system at Rutgers University (New Brunswick, NJ). The life-cycle of two systems were disaggregated into four stages, namely, raw material acquisition and manufacture, transportation, operation and maintenance, and end-of-life. Three uncertain factors—fossil fuel type, number of bikes provided, and bus ridership—were set as variables for sensitivity analysis. Normalization method was used in two impact categories to analyze and compare environmental impacts. The results show that the majority of CO2 emission and energy consumption comes from the raw material stage (extraction and upstream production) of the bike-share system and the operation stage of the campus bus system. The CO2 emission and energy consumption of the current campus bus system are 46 and 13 times of that of the proposed bike-share system, respectively. Three uncertain factors can influence the results: (1) biodiesel can significantly reduce CO2 emission and energy consumption of the current campus bus system; (2) the increased number of bikes increases CO2 emission of the bike-share system; (3) the increase of bus ridership may result in similar impact between two systems. Finally, an alternative hybrid transit system is proposed that uses campus buses to connect four campuses and creates a bike-share system to satisfy travel demands within each campus. The hybrid system reaches the most environmentally friendly state when 70% passenger-miles provided by campus bus and 30% by bike-share system. Further research is needed to consider the uncertainty of biking behavior and travel choice in LCA. Applicable recommendations include increasing ridership of campus buses and building a bike-share in campus to support the current campus bus system. Other strategies such as increasing parking fees and improving biking environment can also be implemented to reduce automobile usage and encourage biking behavior.

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Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽

10.3390/pr9040655 ◽

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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Impact of Cycle Time and Payload of an Industrial Robot on Resource Efficiency

Robotics ◽

10.3390/robotics10010033 ◽

2021 ◽

Vol 10 (1) ◽

pp. 33

Author(s):

Florian Stuhlenmiller ◽

Steffi Weyand ◽

Jens Jungblut ◽

Liselotte Schebek ◽

Debora Clever ◽

...

Keyword(s):

Life Cycle Assessment ◽

Life Cycle ◽

Energy Consumption ◽

Greenhouse Gas Emissions ◽

Greenhouse Gas ◽

Resource Efficiency ◽

Life Cycle Costs ◽

Gas Emissions ◽

Cycle Times ◽

The Individual

Modern industry benefits from the automation capabilities and flexibility of robots. Consequently, the performance depends on the individual task, robot and trajectory, while application periods of several years lead to a significant impact of the use phase on the resource efficiency. In this work, simulation models predicting a robot’s energy consumption are extended by an estimation of the reliability, enabling the consideration of maintenance to enhance the assessment of the application’s life cycle costs. Furthermore, a life cycle assessment yields the greenhouse gas emissions for the individual application. Potential benefits of the combination of motion simulation and cost analysis are highlighted by the application to an exemplary system. For the selected application, the consumed energy has a distinct impact on greenhouse gas emissions, while acquisition costs govern life cycle costs. Low cycle times result in reduced costs per workpiece, however, for short cycle times and higher payloads, the probability of required spare parts distinctly increases for two critical robotic joints. Hence, the analysis of energy consumption and reliability, in combination with maintenance, life cycle costing and life cycle assessment, can provide additional information to improve the resource efficiency.

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A Life Cycle Assessment of Biofuel Produced From Waste Cooking Oil

Volume 6A: Energy ◽

10.1115/imece2018-86301 ◽

2018 ◽

Author(s):

Sierra Spencer ◽

Malia Scott ◽

Nelson Macken

Keyword(s):

Global Warming ◽

Life Cycle Assessment ◽

Life Cycle ◽

Energy Consumption ◽

Waste Cooking Oil ◽

Oil Refining ◽

Soy Oil ◽

Waste Oil ◽

Cumulative Energy ◽

Cooking Oil

Biofuels have received considerable attention as a more sustainable solution for heating applications. Used vegetable oil, normally considered a waste product, has been suggested as a possible candidate. Herein we perform a life cycle assessment to determine the environmental impact of using waste vegetable oil as a fuel. We present a cradle to fuel model that includes the following unit processes: soybean farming, soy oil refining, the cooking process, cleaning/drying waste oil, preheating the oil in a centralized heating facility and transportation when required. For soybean farming, national historical data for yields, energy required for machinery, fertilizers (nitrogen, phosphorous and potassium), herbicides, pesticides and nitrous oxide production are considered. In soy oil refining, steam production using natural gas and electricity for machinery are considered inputs. Preprocessing, extraction using hexane and post processing are considered. In order to determine a mass balance for the cooking operation, oil carryout and waste oil removal are estimated. During waste oil processing, oil is filtered and water removed. Data from GREET is used to compute global warming potential (GWP) and energy consumption in terms of cumulative energy demand (CED). Mass allocation is applied to the soy meal produced in refining and oil utilized for cooking. Results are discussed with emphasis on improving sustainability. A comparison is made to traditional fuels, e.g., commercial fuel oil and natural gas. The production of WVO as fuel has significantly less global warming potential but higher cumulative energy consumption than traditional fuels. The study should provide useful information on the sustainability of using waste cooking oil as a fuel for heating.

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