Pub Date : 2022-12-09DOI: 10.1109/ICESGE56040.2022.10180314
Xue Tan, Yuheng Sha, Yanming Jin, Xiaoqing Yan, Tingting Li
The new power system can promote the green transformation of the energy system and reduce upstream and downstream pollutants and carbon emissions. In order to better evaluate the significant environmental value of investing in new power systems, this study proposed a method combining environmental science, energy planning, technology and economy to more clearly reflect the extensive environmental value of investing in power systems. This study took into consideration the potential of complementary power generation between regions, industry driving force, and the emission reduction effect of adopting new energy downstream and constructed a value accounting method system of upstream and downstream collaborative emission reduction. First, the energy system planning and optimization method was adopted to predict future power consumption and fossil energy conservation. Second, methods of environmental science were used to estimate the emissions of carbon dioxide and different major pollutants. Last, the investment scale and structure of the new power system were considered to calculate the return on new power system investment per unit in reducing upstream and downstream pollutants and carbon emissions. It is estimated that each 10,000 yuan investment in a new power system will encourage the reduction of 6.14 tons of carbon dioxide emissions, 0.74 tons of sulfur dioxide emissions, 0.4 tons of nitrogen oxide emissions, and 1.08 tons of flue gas emissions upstream and downstream during the “14th Five-Year Plan” period.
{"title":"Research on the Evaluation Method of Emission Reduction Value of Power System Investment","authors":"Xue Tan, Yuheng Sha, Yanming Jin, Xiaoqing Yan, Tingting Li","doi":"10.1109/ICESGE56040.2022.10180314","DOIUrl":"https://doi.org/10.1109/ICESGE56040.2022.10180314","url":null,"abstract":"The new power system can promote the green transformation of the energy system and reduce upstream and downstream pollutants and carbon emissions. In order to better evaluate the significant environmental value of investing in new power systems, this study proposed a method combining environmental science, energy planning, technology and economy to more clearly reflect the extensive environmental value of investing in power systems. This study took into consideration the potential of complementary power generation between regions, industry driving force, and the emission reduction effect of adopting new energy downstream and constructed a value accounting method system of upstream and downstream collaborative emission reduction. First, the energy system planning and optimization method was adopted to predict future power consumption and fossil energy conservation. Second, methods of environmental science were used to estimate the emissions of carbon dioxide and different major pollutants. Last, the investment scale and structure of the new power system were considered to calculate the return on new power system investment per unit in reducing upstream and downstream pollutants and carbon emissions. It is estimated that each 10,000 yuan investment in a new power system will encourage the reduction of 6.14 tons of carbon dioxide emissions, 0.74 tons of sulfur dioxide emissions, 0.4 tons of nitrogen oxide emissions, and 1.08 tons of flue gas emissions upstream and downstream during the “14th Five-Year Plan” period.","PeriodicalId":120565,"journal":{"name":"2022 International Conference on Environmental Science and Green Energy (ICESGE)","volume":"448 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128601226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-09DOI: 10.1109/ICESGE56040.2022.10180380
Zhenwei Ji, Jiwen Zhang, Zhiwei Hu, Xingxing Huang, Tianyu Zhao, Zhengwei Wang
Rotor deformation is one of the key problems of large turbine generators. Previous studies mainly calculated the unbalanced magnetic pull (UMP) of turbine generators. Giant hydroelectric generators are huge in size and extremely complex in structure, so there are few relevant UMP analyses about them up to now. This paper uses a two-dimensional transient finite element method to calculate the transient magnetic field and reveal the effect of rotor deformation on the UMP of a 1000 MW hydro-generator in the time and frequency domains. Theoretical calculations are in good agreement with field measurements. The results of this investigation can provide scientifically valuable guidance for the manufacture and installation of structurally complex giant hydraulic turbine units.
{"title":"Influence of Magnetic Pole Ovality on the Unbalanced Magnetic Pull of a 1000 MW Hydro-generator Unit Installed with High Precision","authors":"Zhenwei Ji, Jiwen Zhang, Zhiwei Hu, Xingxing Huang, Tianyu Zhao, Zhengwei Wang","doi":"10.1109/ICESGE56040.2022.10180380","DOIUrl":"https://doi.org/10.1109/ICESGE56040.2022.10180380","url":null,"abstract":"Rotor deformation is one of the key problems of large turbine generators. Previous studies mainly calculated the unbalanced magnetic pull (UMP) of turbine generators. Giant hydroelectric generators are huge in size and extremely complex in structure, so there are few relevant UMP analyses about them up to now. This paper uses a two-dimensional transient finite element method to calculate the transient magnetic field and reveal the effect of rotor deformation on the UMP of a 1000 MW hydro-generator in the time and frequency domains. Theoretical calculations are in good agreement with field measurements. The results of this investigation can provide scientifically valuable guidance for the manufacture and installation of structurally complex giant hydraulic turbine units.","PeriodicalId":120565,"journal":{"name":"2022 International Conference on Environmental Science and Green Energy (ICESGE)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122895200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To grasp the future trend of ambient air quality more accurately and provide more reliable data support for formulating and implementing environmental protection departments' policies. This paper establishes the Winters model and mean GM (1,1) model to predict the ambient air quality in the next five years, based on Harbin's atmospheric environmental quality monitoring data from 2015 to 2021. The results show that the seasonal characteristics of ambient air quality in Harbin are still not eliminated, but the overall trend is improving year by year. Using the combined model can make long-term predictions for smaller time units, enhancing work precision and short-term predictions for larger time units. Furthermore, it makes the comprehensive study and judgment of the future change trend of environmental factors more reasonable and the prediction results more meaningful.
{"title":"Prediction and Analysis of Ambient Air Quality in Harbin Based on Time Series Analysis Model","authors":"Zhihao Zhang, Yanan Li, Jiazhuo Qi, Jun-jian Ma, Xiaoyan Wang, Miao Zhou","doi":"10.1109/ICESGE56040.2022.10180313","DOIUrl":"https://doi.org/10.1109/ICESGE56040.2022.10180313","url":null,"abstract":"To grasp the future trend of ambient air quality more accurately and provide more reliable data support for formulating and implementing environmental protection departments' policies. This paper establishes the Winters model and mean GM (1,1) model to predict the ambient air quality in the next five years, based on Harbin's atmospheric environmental quality monitoring data from 2015 to 2021. The results show that the seasonal characteristics of ambient air quality in Harbin are still not eliminated, but the overall trend is improving year by year. Using the combined model can make long-term predictions for smaller time units, enhancing work precision and short-term predictions for larger time units. Furthermore, it makes the comprehensive study and judgment of the future change trend of environmental factors more reasonable and the prediction results more meaningful.","PeriodicalId":120565,"journal":{"name":"2022 International Conference on Environmental Science and Green Energy (ICESGE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132420211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-09DOI: 10.1109/ICESGE56040.2022.10180322
Osama A. Marzouk
This work aims to organize selected global targets set by the two international organizations: IEA (International Energy Agency) and IRENA (International Renewable Energy Agency), to form pathways for stopping the accumulation of carbon dioxide (CO2) in the atmosphere, and for reaching a condition of net-zero CO2 emissions through accelerating the transition to clean-energy consumption, thereby decelerating the rate of climate change. In achieving this aim, three energy-emissions-climate (EEC) outlook scenarios are discussed and selected targets from them are listed here after being grouped chronologically, from 2021 to 2050. The majority of these targets (54 total) belong to the years 2030 (23 targets) and 2050 (16 targets). However, the years 2021, 2025, 2035, 2040, and 2045 have one or more targets as well. The outlook scenarios considered here are: (1) Net-Zero Emissions by 2050 Scenario (NZE) of IEA, (2) Sustainable Development Scenario (SDS) of IEA, and (3) 1.5°C Scenario by IRENA. The IEA-NZE and IRENA-1.5°C scenarios both imply limiting the global temperature increase to 1.5°C, and reaching global net-zero CO2 emissions by 2050. The IEA-SDS scenario is less strict, allowing a slightly-higher global temperature increase of 1.65°C and having a later deadline of 2070 for reaching global net-zero CO2 emissions.
{"title":"Chronologically-Ordered Quantitative Global Targets for the Energy-Emissions-Climate Nexus, from 2021 to 2050","authors":"Osama A. Marzouk","doi":"10.1109/ICESGE56040.2022.10180322","DOIUrl":"https://doi.org/10.1109/ICESGE56040.2022.10180322","url":null,"abstract":"This work aims to organize selected global targets set by the two international organizations: IEA (International Energy Agency) and IRENA (International Renewable Energy Agency), to form pathways for stopping the accumulation of carbon dioxide (CO2) in the atmosphere, and for reaching a condition of net-zero CO2 emissions through accelerating the transition to clean-energy consumption, thereby decelerating the rate of climate change. In achieving this aim, three energy-emissions-climate (EEC) outlook scenarios are discussed and selected targets from them are listed here after being grouped chronologically, from 2021 to 2050. The majority of these targets (54 total) belong to the years 2030 (23 targets) and 2050 (16 targets). However, the years 2021, 2025, 2035, 2040, and 2045 have one or more targets as well. The outlook scenarios considered here are: (1) Net-Zero Emissions by 2050 Scenario (NZE) of IEA, (2) Sustainable Development Scenario (SDS) of IEA, and (3) 1.5°C Scenario by IRENA. The IEA-NZE and IRENA-1.5°C scenarios both imply limiting the global temperature increase to 1.5°C, and reaching global net-zero CO2 emissions by 2050. The IEA-SDS scenario is less strict, allowing a slightly-higher global temperature increase of 1.65°C and having a later deadline of 2070 for reaching global net-zero CO2 emissions.","PeriodicalId":120565,"journal":{"name":"2022 International Conference on Environmental Science and Green Energy (ICESGE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131465955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-09DOI: 10.1109/ICESGE56040.2022.10180367
Yichen Wei, Zixian Zhang, Xiaohui Zhu, Yong Yue
The harmful algal blooms in fresh waters have led to severe environmental problems such as mass mortalities of wild and cultured fish and shellfish, and human illnesses, which hamper the sustainability of fisheries and aquaculture. Blue-green algae (BGA) are commonly the dominant species in harmful algal blooms. Thus, studying the correlation between BGA and water quality indicators can contribute to establishing data-driven models when predicting the outbreaks of BGA. Previous studies typically used data from fixed-point sampling for correlation analysis. For specific waters, fixed-point sampling has the defects of small coverage, low sampling frequency, and poor flexibility, which is one of the reasons affecting the reliability of the analysis results. This paper uses an unmanned surface vehicle (USV) for water quality data collection. Spearman's correlation coefficient and statistical methods are used to conduct correlation analysis between BGA biomass (measured by phycocyanin) and water quality indicators. The results show a significant positive correlation between BGA biomass and chlorophyll-a, pH, water temperature, and dissolved oxygen. The results are consistent with most correlation studies and demonstrate the feasibility of using the massive sampling data collected by unmanned surface vehicle to analyze the correlation between BGA and water quality indicators.
{"title":"Correlation Research between Blue-green Algae and Water Quality Indicators Using Unmanned Surface Vehicle","authors":"Yichen Wei, Zixian Zhang, Xiaohui Zhu, Yong Yue","doi":"10.1109/ICESGE56040.2022.10180367","DOIUrl":"https://doi.org/10.1109/ICESGE56040.2022.10180367","url":null,"abstract":"The harmful algal blooms in fresh waters have led to severe environmental problems such as mass mortalities of wild and cultured fish and shellfish, and human illnesses, which hamper the sustainability of fisheries and aquaculture. Blue-green algae (BGA) are commonly the dominant species in harmful algal blooms. Thus, studying the correlation between BGA and water quality indicators can contribute to establishing data-driven models when predicting the outbreaks of BGA. Previous studies typically used data from fixed-point sampling for correlation analysis. For specific waters, fixed-point sampling has the defects of small coverage, low sampling frequency, and poor flexibility, which is one of the reasons affecting the reliability of the analysis results. This paper uses an unmanned surface vehicle (USV) for water quality data collection. Spearman's correlation coefficient and statistical methods are used to conduct correlation analysis between BGA biomass (measured by phycocyanin) and water quality indicators. The results show a significant positive correlation between BGA biomass and chlorophyll-a, pH, water temperature, and dissolved oxygen. The results are consistent with most correlation studies and demonstrate the feasibility of using the massive sampling data collected by unmanned surface vehicle to analyze the correlation between BGA and water quality indicators.","PeriodicalId":120565,"journal":{"name":"2022 International Conference on Environmental Science and Green Energy (ICESGE)","volume":"113 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131879793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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