Energy-Saving Scheduling in a Flexible Flow Shop Using a Hybrid Genetic Algorithm

Many researches discussing reduced energy consumption for environmental protection focus on machine efficiency or process redesign. To optimize the machine operation time can also save the energy, and these researches have received great interests in recent years. This study considers three different states of machines, among processing there are two different speeds, to solve the problem of minimizing energy costs under time-of-use tariff with no tardy jobs in flexible flow shop. This problem is basically NP-hard, we proposed a hybrid genetic algorithm (GA) to solve problems in reasonable timeliness. The result shows that to optimize different states of machines under time-of use tariff can reduce energy costs significantly in on-time delivery.

Research on the Trusted Energy-Saving Transmission of Data Center Network

According to the high operating costs and a large number of energy waste in the current data center network architectures, we propose a kind of trusted flow preemption scheduling combining the energy-saving routing mechanism based on typical data center network architecture. The mechanism can make the network flow in its exclusive network link bandwidth and transmission path, which can improve the link utilization and the use of the network energy efficiency. Meanwhile, we apply trusted computing to guarantee the high security, high performance and high fault-tolerant routing forwarding service, which helps improving the average completion time of network flow.

SUSTAINABLE DESIGN AND ENERGY CONSUMPTION ANALYSIS FOR STRUCTURAL COMPONENTS

The engineering community has been striving to design more sustainable buildings in an attempt to reduce both environmental impact and energy use during all phases of design,construction and operation.Design professionals currently have very limited guidance or tools to incorporate life-cycle and sustainability concepts into their designs.After reviewing the capabilities and limitations of four current life cycle analysis(LCA)computer programs,this research has selected the Athena Impact Estimator v4.0 to perform parametric studies of structural members made up of different construction materials.The energy consumption values are calculated and compared for columns,beams,concrete suspended slabs,precast double-tee sections and various other floor types.While Athena did offer some insights based on its LCA results,this research has concluded that existing LCA and sustainability analysis programs have too few options to meet the current needs of design professionals.A more accurate,sophisticated whole-building LCA tool needs to be developed to assess sustainable properties of design alternatives and to produce the most sustainable structural systems.

Specific Energy Consumption Analysis Model and Its Application in Typical Steel Manufacturing Process

Theoretical minimum and actual specific energy consumptions （SEC） of typical manufacturing process （SMP） were studied. Firstly, a process division of a typical SMP in question was conducted with the theory of SEC analysis. Secondly, an exergy analysis model of a subsystem consisting of several parallel processes and a SEC analysis model of SMP were developed. And finally, based on the analysis models, the SEC of SMP was analyzed by means of the statistical significance. The results show that the SEC of typical SMP comprises the theoretical minimum SEC and the additional SEC derived from the irreversibility~ and the SMP has a theoretical minimum SEC of 6.74 GJ/t and an additional SEC of 19.32 GJ/t, which account for 25.88% and 74.12% of the actual SEC, respectively.

Path Analysis of Energy-Saving Technology in Yangtze River Basin Based on Multi-Objective and Multi-Parameter Optimisation

The Yangtze River Basin in China is characterised by hot-and cold-humid climates in summer and winter, respectively. Thus, increased demand for heating and cooling energy according to the season, as well as poor indoor thermal comfort, are inevitable. To overcome this problem, this study focused on the influence of passive design and heating, ventilation, and air conditioning equipment performance on the energy performance of residential buildings, and explored potential energy-saving technology paths involving passive design and improved coefficient of performance through a multi-objective and multi-parameter optimisation technique. A large-scale questionnaire survey covering a typical city was first conducted to identify family lifestyle patterns regarding time spent at home, family type, air conditioner use habits, indoor thermal comfort, etc. Then, the actual heating and cooling energy consumption and information of model building were determined for this region. Subsequently, the design parameters of an individual building were simulated using Energyplus to investigate the cooling and heating energy consumption for a typical residential building with an air conditioner. The results indicated an improvement of approximately 30% in energy efficiency through optimisation of the external-wall insulation thickness and the external-window and shading performance, and through use of appropriate ventilation technology. Thus, a multi-objective and multi-parameter optimisation model was developed to achieve comprehensive optimisation of several design parameters. Experimental results showed that comprehensive optimisation could not only reduce cooling and heating energy consumption, but also improve the thermal comfort level achieved with a non-artificial cooling and heating source. Finally, three energy-saving technology paths were formulated to achieve a balance between indoor thermal comfort improvement and the target energy efficiency(20 kWh/(m2?a)). The findings of this study have implications for the future design of buildings in the Yangtze River Basin, and for modification of existing buildings for improved energy efficiency.

Analysis of Technical Energy Conservation Potential of China’s Energy Consumption Sectors

It is necessary for China to refocus its energy conservation effort from the industrial sector (field) to all three sectors simultaneously, i.e. industry, construction and transport. In addition, it should also make significant effort for conserving energy on general technical equipment that are used in large quantities and for a variety of applications. Therefore, there is a need to integrate industrial, construction and transport sectors, i.e. the integration between key technologies and widely used technologies, between hard and soft management, between energy-saving technologies and comprehensive resource utilization technologies. According to estimates, if China’s energy consuming sectors adopted appropriate energy-saving technologies, total energy-savings (using 2010 as the baseline) would be 200 million, 450 million, 650 million and 800 million tons of standard coal in 2015, 2020, 2025 and 2030, respectively.

Review of energy consumption research for papermaking industry based on life cycle analysis

Papermaking industry is a high-energy-consuming industry with long supply chain.The growth of paper product demand further intensifies the need of energy consumption.Energy saving through the full supply chain has become a focal point for long-term sustainable development of the papermaking industry.This paper reviews the advances in life cycle analysis for the papermaking industry in recent years.All the stages from the full supply chain are involved to give a panoramic overview of the papermaking industry.The object of this paper is to provide scientific basis to industry and decision-makers with profound understanding of the energy consumption and energy saving potential in a life cycle perspective.

A process-level hierarchical structural decomposition analysis (SDA) of energy consumption in an integrated steel plant

A hierarchical structural decomposition analysis(SDA) model has been developed based on process-level input-output(I-O) tables to analyze the drivers of energy consumption changes in an integrated steel plant during 2011-2013. By combining the principle of hierarchical decomposition into D&L method, a hierarchical decomposition model for multilevel SDA is obtained. The developed hierarchical IO-SDA model would provide consistent results and need less computation effort compared with the traditional SDA model. The decomposition results of the steel plant suggest that the technology improvement and reduced steel final demand are two major reasons for declined total energy consumption. The technical improvements of blast furnaces, basic oxygen furnaces, the power plant and the by-products utilization level have contributed mostly in reducing energy consumption. A major retrofit of ancillary process units and solving fuel substitution problem in the sinter plant and blast furnace are important for further energy saving. Besides the empirical results, this work also discussed that why and how hierarchical SDA can be applied in a process-level decomposition analysis of aggregated indicators.

Analysis and forecast of residential building energy consumption in Chongqing on carbon emissions

Carbon emissions mainly result from energy consumption. Carbon emissions inevitably will increase to some extent with economic expansion and rising energy consumption. We introduce a gray theory of quantitative analysis of the energy consumption of residential buildings in Chongqing,China,on the impact of carbon emission factors. Three impacts are analyzed,namely per capita residential housing area,domestic water consumption and the rate of air conditioner ownership per 100 urban households. The gray prediction model established using the Chongqing carbon emission-residential building energy consumption forecast model is sufficiently accurate to achieve a measure of feasibility and applicability.

Sensitivity Analysis of Energy Consumption for Cylinder Efficiency of A 1 000 MW Steam Turbine Unit

以东汽1000MW汽轮机为研究对象，采用汽轮机组定功率变工况计算方法，对汽轮机各汽缸效率在THA、70％THA、50％THA、40％THA滑压工况进行敏度分析，得到相应工况下各汽缸效率变化对汽轮机热耗率和机组发、供电煤耗率的影响规律。分析表明，缸效率的能耗敏度随缸效率变化基本呈线性关系；低压缸效率的能耗敏度最大，高压缸效率次之，中压缸效率较小；随机组负荷降低，高压缸效率的能耗敏度增加，中、低压缸效率的能耗敏度变化较小。在THA工况下低压缸效率下降1％，供电煤耗敏度绝对值增加1．246g／（kW·h），相对值升高0．433％；高压缸效率下降1％，供电煤耗敏度的绝对值增加0．44g／（kW．h），相对值升高0．154％；中压缸效率下降1％，供电煤耗敏度的绝对值增加0．352g／（kW．h），相对值升高0．122％。分析结果可对同类型机组进行节能诊断，进而指导机组的优化运行。

Correlation Analysis of Energy Consumption of Agricultural Rotorcraft

With the rapid development of unmanned aerial vehicle technology,unmanned aerial vehicles(UAVs)have been widely used in the field of agricultural plant protection.Compared with fuel-driven UAVs,electrically driven rotorcrafts have many advantages such as lower cost,simpler operation,good maneuverability and cleaner power,which them popular in the plant protection.However,electrical rotorcrafts still face battery problems in actual operation,which limits its working time and application.Aiming at this issue,this paper studied the influence of rotorcraft flight parameters on energy consumption through series of carefully designed flight experiments.First of all,the linear motion experiments have been designed that the rotorcraft was made to perform speed tests and acceleration test with the speed varied from 2∼9 m/s.Secondly,the turning maneuver experiments are carried out under the different circular routes,a rotorcraft was made to conduct successive steering maneuvers at a certain speed of 2 m/s.With the collected tests data,the relation of the energy consumption and the flight dynamic parameter are analyzed through correlation analysis,and the test results of different pairs of experiments have been compared.The research results of this paper would encourage the agricultural rotorcraft to make less maneuvers during operation,which can also provide practical experience and data support for subsequent optimization of flight parameters and reduction of energy consumption.

Analysis of the Energy Efficiency of Diesel Oil Consumption in the Brazilian Iron Ore Mining Company

This paper aims to evaluate the diesel oil consumption between 2008 and 2015 in the production of iron ore in Brazil, creating correlations between energy intensity (production), economy and checking the impact of fuel prices on the commodity. During the analyzed period, the years 2008-2009 indicated economic crises, which interfered in the price and the commercialization of iron ore products. The physical intensity was 0.2% higher than the economic intensity. In the period from 2010 to 2014, economic activity remained more stable, with a decreasing trend, mainly due to the increase of iron ore prices. The physical intensity is much higher than the economic intensity influenced by the expansion of the Chinese economy. The year of 2014 indicated the end of the high iron ore price cycle and the beginning of supply and demand stabilization with consequent reduction in prices. In 2015, the market entered the stabilization phase, with a continuous reduction in unit production costs and transportation logistics. There was an abrupt change due to the strong increase of the economic intensity due to the fall of the international prices of iron ore. The diesel oil consumption plays a vital role in the scenario of cost reduction in iron ore production and a deeper analysis must be done in order to discover some options to change the energy matrix.

Operation strategy and energy-saving of the solar lighting/heating system through spectral splitting

The performance of a solar lighting and heating system(SLHS)based on the spectral splitting effect of nanofluids is presented in this paper.SLHS through nanofluids would split the sunlight spectrum into different wavelength,and then introduce the visible light into the offices for lighting and absorb infrared energy to generate hot water.The Energy Plus software was used to analyze the energy consumption of typical office building located in the city of Harbin in China coupled with SLHS.Based on the simulation results two lighting zones were identified in the offices and the optimal lighting control strategy was developed for a full year.The performance models of SLHS with different light-receiving areas of 10 m^(2)and 40 m^(2)were simulated and validated using the existing experimental data.The overall energy-saving of the offices over a full year were analyzed using the validated model.Results demonstrated that for SLHS with the area of 40 m^(2),the rate of the energy saving in the offices due to lighting and hot water systems were 58.9%,and 19.3%,respectively.The system also had the additional benefit of reducing the cooling load of the air conditioning system during summer period together with improving the quality of the indoor environment resulting in better health and productivity of the occupants.

Influencing factors and contribution analysis of CO_(2) emissions originating from final energy consumption in Sichuan Province,China

Within the context of CO_(2)emission peaking and carbon neutrality,the study of CO_(2)emissions at the provincial level is few.Sichuan Province in China has not only superior clean energy resources endowment but also great potential for the reduction of CO_(2)emissions.Therefore,using logarithmic mean Divisia index(LMDI)model to analysis the influence degree of different influencing factors on CO_(2)emissions from final energy consumption in Sichuan Province,so as to formulate corresponding emission reduction countermeasures from different paths according to the influencing factors.Based on the data of final energy consumption in Sichuan Province from 2010 to 2019,we calculated CO_(2)emission by the indirect emission calculation method.The influencing factors of CO_(2)emissions originating from final energy consumption in Sichuan Province were decomposed into population size,economic development,industrial structure,energy consumption intensity,and energy consumption structure by the Kaya-logarithmic mean Divisia index(LMDI)decomposition model.At the same time,grey correlation analysis was used to identify the correlation between CO_(2)emissions originating from final energy consumption and the influencing factors in Sichuan Province.The results showed that population size,economic development and energy consumption structure have positive contributions to CO_(2)emissions from final energy consumption in Sichuan Province,and economic development has a significant contribution to CO_(2)emissions from final energy consumption,with a contribution rate of 519.11%.The industrial structure and energy consumption intensity have negative contributions to CO_(2)emissions in Sichuan Province,and both of them have significant contributions,among which the contribution rate of energy consumption structure was 325.96%.From the perspective of industrial structure,secondary industry makes significant contributions and will maintain a restraining effect;from the perspective of energy consumption structure,industry sector has a significant contribution.The results of this paper are conducive to the implementation of carbon emission reduction policies in Sichuan Province.

Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.

Analysis on Industry Structure Adjustment and Energy Consumption Based on Grey Theory——A Case Study of Qinghai Province

In view of the complexity and non-linearity of energy consumption system and the status quo of the development of energy in Qinghai Province,the relations between energy consumption and industrial structure is analyzed by using the quantitative analysis of grey relation degree by using the grey system theory.The relevancy degree among the primary industry,the secondary industry and the tertiary industry and living energy consumption are obtained,and then the trend of energy consumption in the following several years can be predicted.The results show that the secondary industry has the largest relevancy degree to the total energy consumption.In the end,according to the results of the research,several suggestions on how to saving energy are put forward.Firstly,the government should improve the high-tech industry and restrict the development of high-consumption and high-pollution industries.Secondly,the government should promote the low-carbon way of life;promote energy saving and control the energy consumption of the department of life.Thirdly,clean production should be actively promoted in the tertiary industry and the circular economy should be vigorously expanded.

Simplified prediction model for lighting energy consumption in office building scheme design

At the scheme design stage,the potential of daylighting is significant due to the saving for electric lighting use. There are few simple tools for architects to optimize the daylighting design. Therefore,it is useful to develop a design guideline related to the evaluation of lighting energy saving potential and sunlight design strategies. This paper analyzes the impacts of different artificial lighting control methods and design parameters on daylighting. A direct correlation between lighting energy consumption and parameters such as orientations,window to wall ratio (WWR) and perimeter depth is established. A simplified prediction model is proposed to estimate lighting energy consumption with the given perimeter depth,WWR,and window transparency. Validation of the model is carried out compared with detailed lighting simulation software for an office building. After the variation analysis for these parameters,design advises for the daylighting design at scheme design phase are summarized.

Economic Analysis of Energy-efficient Buildings in China

Applying energy-saving measures in residential buildings is usually constrained by the increase of initial investment. ＂However, if it is analyzed from the view of energy cost and life-cycle cost, the energy-saving benefit can offset ~he increase of initial investment. An analysis method based on life-cycle concept was developed to calcu- late the energy cost of residential building flats. Several uncertain factors were included into the model, making it more accurate to reflect practical situation. The model was solved using the software DeST and applied to one resi- dential building project in Shanghai. The case study shows that the initial investment （cost） is paid back during the operational phase through less consumption of energy. It further indicates that the investment recovery period is between 10 and 19 years which are acceptable to households and developers in China.

Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation(BP)neural network to solve nonlinear problems and have the ability of global approximation and generalization.By analyzing the influence of different uses,different building surfaces and different energysaving schemes on the change of building energy consumption,the grey correlation method is used to determine the main influencing factors affecting each building energy consumption,including uses,building surfaces and energy-saving schemes,which are used as the input of the model and the building energy consumption as the output of the model,so as to establish the building energy consumption analysis model based on BP neural network.However,in practical application,BP neural network has the defects of slow convergence and easy to fall into local minima.In view of this,this paper uses genetic algorithm to optimize the weight and threshold of BP neural network,completes the improvement of various building energy consumption analysis models,and realizes the qualitative analysis of building energy consumption.The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm(GABP)in this paper is relatively high,which is more accurate than the results of the traditional BP neural network model,and the relative error of the analysis model is reduced from 11.56%to 8.13%,which proves that the GABP can be better suitable for the study of school building energy consumption analysis model,It is applied to the prediction of building energy consumption,which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.