An integrated multi-objective optimization model for establishing the low-carbon scenario 2020 to achieve the national carbon emissions reduction target for residential buildings

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

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Framework for the analysis of the low-carbon scenario 2020 to achieve the national carbon Emissions reduction target: Focused on educational facilities

Energy Policy ◽

10.1016/j.enpol.2014.05.009 ◽

2014 ◽

Vol 73 ◽

pp. 356-367 ◽

Cited By ~ 19

Author(s):

Choongwan Koo ◽

Hyunjoong Kim ◽

Taehoon Hong

Keyword(s):

Carbon Emissions ◽

Emissions Reduction ◽

Low Carbon ◽

Educational Facilities ◽

Carbon Emissions Reduction

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Reverse Logistics Location Based on Energy Consumption: Modeling and Multi-Objective Optimization Method

Applied Sciences ◽

10.3390/app11146466 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6466

Author(s):

Lijun Chang ◽

Honghao Zhang ◽

Guoquan Xie ◽

Zhenzhong Yu ◽

Menghao Zhang ◽

...

Keyword(s):

Facility Location ◽

Carbon Emissions ◽

Optimization Algorithm ◽

Reverse Logistics ◽

Low Carbon ◽

Multi Objective Optimization ◽

Low Carbon Economy ◽

Logistics Network ◽

Multi Objective ◽

Carbon Economy

The low-carbon economy, as a major trend of global economic development, has been a widespread concern, which is a rare opportunity to realize the transformation of the economic way in China. The realization of a low-carbon economy requires improved resource utilization efficiency and reduced carbon emissions. The reasonable location of logistics nodes is of great significance in the optimization of a logistics network. This study formulates a double objective function optimization model of reverse logistics facility location considering the balance between the functional objectives of the carbon emissions and the benefits. A hybrid multi-objective optimization algorithm that combines a gravitation algorithm and a particle swarm optimization algorithm is proposed to solve this reverse logistics facility location model. The mobile phone recycling logistics network in Jilin Province is applied as the case study to verify the feasibility of the proposed reverse logistics facility location model and solution method. Analysis and discussion are conducted to monitor the robustness of the results. The results prove that this approach provides an effective tool to solve the multi-objective optimization problem of reverse logistics location.

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Multi-Objective Optimization Model of Multi-Pass Turning Operations to Minimize Energy, Carbon Emissions, and Production Costs

Jurnal Teknik Industri ◽

10.22219/jtiumm.vol21.no2.213-224 ◽

2020 ◽

Vol 21 (2) ◽

pp. 213-224

Author(s):

Aprilia Dityarini ◽

Eko Pujiyanto ◽

I Wayan Suletra

Keyword(s):

Carbon Emissions ◽

Optimization Model ◽

Sustainable Manufacturing ◽

Cutting Parameters ◽

Production Costs ◽

Machining Process ◽

Multi Objective Optimization ◽

Multi Objective ◽

Decision Variables ◽

Optimal Values

Sustainable manufacturing aspects are environmental, economic, and social. These aspects can be applied to an optimization model in the machining process. An optimization model is needed to determine the optimum cutting parameters. This research develops a multi-objective optimization model that can optimize cutting parameters on a multi-pass turning. Decision variables are cutting parameters multi-pass turning. This research has three objective functions for minimizing energy, carbon emissions, and costs. Three functions are searched for optimal values using the GEKKO. A numerical example is given to show the implementation of the model and solved using GEKKO and Interior Point Optimizer (IPOPT). The results of optimization indicate that the model can be used to optimize the cutting parameters.

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Development of a multi-objective optimization model for determining the optimal CO2 emissions reduction strategies for a multi-family housing complex

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2019.04.068 ◽

2019 ◽

Vol 110 ◽

pp. 118-131 ◽

Cited By ~ 3

Author(s):

Kwangbok Jeong ◽

Taehoon Hong ◽

Jimin Kim ◽

Kyuman Cho

Keyword(s):

Co2 Emissions ◽

Optimization Model ◽

Emissions Reduction ◽

Multi Objective Optimization ◽

Multi Objective ◽

Reduction Strategies ◽

Family Housing ◽

Housing Complex

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Multi objective optimization model of CNC turning for minimizing processing time and carbon emission with real machining application

Journal of Industrial Engineering and Management ◽

10.3926/jiem.3269 ◽

2021 ◽

Vol 14 (2) ◽

pp. 376

Author(s):

Cucuk Nur Rosyidi ◽

Wahyu Widhiarso ◽

Eko Pujiyanto

Keyword(s):

Carbon Emissions ◽

Optimization Model ◽

Carbon Emission ◽

Processing Time ◽

Cutting Speed ◽

Cutting Fluid ◽

Multi Objective Optimization ◽

Cnc Turning ◽

Multi Objective ◽

Cnc Turning Process

Purpose: The purpose of this research is to develop an optimization model of CNC turning process. The objective function of the model is to minimize processing time and carbon emission. We implemented the results of optimization with real machining application using a certain workpiece.Design/methodology/approach: The model in this research used multi objective optimization involving two objective functions, namely processing time which includes cutting time and auxiliary time and carbon emissions resulted from the electricity energy consumptions, cutting tool, cutting fluid or coolant, raw materials production, and chip removal.Findings: The results of multi objective optimization indicate that the model can be used to minimize the processing time and carbon emissions with the optimal cutting speed and feed rate are 193.7 m/minute and 0.405 mm/rev. The results of sensitivity analysis showed that the higher weights of processing time will decrease the cutting speed, while the higher carbon emissions weight will result in faster cutting speed. The weight has no effects on feed rate.Originality/value: This paper gives a real machining application to show the applicability of the optimization model

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Influencing Factors and Scenario Forecasts of Carbon Emissions of the Chinese Power Industry: Based on a Generalized Divisia Index Model and Monte Carlo Simulation

Energies ◽

10.3390/en11092398 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2398 ◽

Cited By ~ 10

Author(s):

Lin Zhu ◽

Lichun He ◽

Peipei Shang ◽

Yingchun Zhang ◽

Xiaojun Ma

Keyword(s):

Energy Consumption ◽

Carbon Emissions ◽

Energy Intensity ◽

Power Industry ◽

Emissions Reduction ◽

Low Carbon ◽

Maximum Probability ◽

Index Model ◽

Carbon Emissions Reduction ◽

Probability Range

The power industry is the industry with the most direct uses of fossil fuels in China and is one of China’s main carbon industries. A comprehensive and accurate analysis of the impacts of carbon emissions by the power industry can reveal the potential for carbon emissions reductions in the power industry to achieve China’s emissions reduction targets. The main contribution of this paper is the use of a Generalized Divisia Index Model for the first time to factorize the change of carbon emissions in China’s power industry from 2000 to 2015, and gives full consideration to the influence of the economy, population, and energy consumption on the carbon emissions. At the same time, the Monte Carlo method is first used to predict the carbon emissions of the power industry from 2017 to 2030 under three different scenarios. The results show that the output scale is the most important factor leading to an increase in carbon emissions in China’s power industry from 2000 to 2015, followed by the energy consumption scale and population size. Energy intensity levels have always promoted carbon emissions reduction in the power industry, where energy intensity and carbon intensity effects of energy consumption have great potential to mitigate carbon levels. By setting the main factors affecting carbon emissions in the future three scenarios, this paper predicts the carbon emissions of China’s power industry from 2017 to 2030. Under the baseline scenario, the maximum probability range of the potential annual growth rate of carbon emissions by the power industry in China from 2017 to 2030 is 1.9–2.2%. Under the low carbon scenario and technological breakthrough scenario, carbon emissions in China’s power industry continue to decline from 2017 to 2030. The maximum probability range of the potential annual drop rate are measured at 1.6–2.1% and 1.9–2.4%, respectively. The results of this study show that China’s power industry still has great potential to reduce carbon emissions. In the future, the development of carbon emissions reduction in the power industry should focus on the innovation and development of energy saving and emissions reduction technology on the premise of further optimizing the energy structure and adhering to the low-carbon road.

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Prediction on Carbon Emissions Reduction and Analysis on Economic Development Based on Energy Consumption Policy

The Open Fuels & Energy Science Journal ◽

10.2174/1876973x01508010229 ◽

2015 ◽

Vol 8 (1) ◽

pp. 229-232

Author(s):

Hailong Chen

Keyword(s):

Global Warming ◽

Computer Technology ◽

Carbon Emissions ◽

National Economy ◽

Fossil Fuels ◽

Emissions Reduction ◽

Low Carbon ◽

Leading Role ◽

Carbon Emissions Reduction ◽

Industrial Level

As to the global warming, China has confidence in the development of the economy by bearing responsibility and obligation toward curbing global warming, which at this time can be achieved by reducing carbon emissions. Industry is an important material production department in the national economy, and plays a leading role in the national economy. Chinese industrial production is mainly based on the consumption of fossil fuels, resulting in a large amount of CO2 emission. Therefore, how to find a way to predict the discharge of CO2 by computer technology and make people realize the importance of low carbon development at industrial level is the focus of this study.

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