Pub Date : 2019-10-31DOI: 10.12783/dteees/iceee2019/31792
Qingxi Huang, Luyao Liu, Qie Sun, R. Wennersten
To increase the microgrid’s (MG) profit and decrease the MG’s power loss, the cooperation between MGs has been studied. But there is lack of a specific mechanism for determining how to distribute alliance’s total profit to different MGs and an impact on balancing market’s cost. Hence, this paper firstly confirmed the electricity market mechanism. Then, established a model of direct transaction between MGs and main grid (DTMM) and proposed a model of cooperative transaction between MGs (CTM) based on interbank lending method. Finally, the paper used chronological simulation method to simulate the models proposed on MATLAB. Results showed that all MGs’ profit increased by 359160 € and the balancing market’s cost decreased by 91% in CTM compared with DTMM.
{"title":"A Cooperative Model for Microgrids Based on Interbank Lending Method","authors":"Qingxi Huang, Luyao Liu, Qie Sun, R. Wennersten","doi":"10.12783/dteees/iceee2019/31792","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31792","url":null,"abstract":"To increase the microgrid’s (MG) profit and decrease the MG’s power loss, the cooperation between MGs has been studied. But there is lack of a specific mechanism for determining how to distribute alliance’s total profit to different MGs and an impact on balancing market’s cost. Hence, this paper firstly confirmed the electricity market mechanism. Then, established a model of direct transaction between MGs and main grid (DTMM) and proposed a model of cooperative transaction between MGs (CTM) based on interbank lending method. Finally, the paper used chronological simulation method to simulate the models proposed on MATLAB. Results showed that all MGs’ profit increased by 359160 € and the balancing market’s cost decreased by 91% in CTM compared with DTMM.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85527501","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 : 2019-10-31DOI: 10.12783/dteees/iceee2019/31782
Xiaojia Fan, Sanmang Wu, Shantong Li
How to evaluate the value-added acquisition and carbon emissions of a province, especially for resourcebased provinces, in the GVCs is still a problem worth solving. By embedding the world input-output table of Chinese provinces and using the decomposition method of GVCs, we comprehensively measured the valueadded and carbon emissions of Shanxi Province, a resource-rich province in China. The results show that from 2002 to 2012, the value-added carbon emissions imports and exports of Shanxi have been growing rapidly, which are mostly achieved through direct imports and exports of intermediate products or through other provinces in China. And we combined valueadded with carbon emissions to build an environmental inequity index model, and found that in the GVCs, the profitability of Shanxi actually deteriorated.
{"title":"Has China's Resource-based Regions Improved in the GVCs Specialization?—Take Shanxi as an Example","authors":"Xiaojia Fan, Sanmang Wu, Shantong Li","doi":"10.12783/dteees/iceee2019/31782","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31782","url":null,"abstract":"How to evaluate the value-added acquisition and carbon emissions of a province, especially for resourcebased provinces, in the GVCs is still a problem worth solving. By embedding the world input-output table of Chinese provinces and using the decomposition method of GVCs, we comprehensively measured the valueadded and carbon emissions of Shanxi Province, a resource-rich province in China. The results show that from 2002 to 2012, the value-added carbon emissions imports and exports of Shanxi have been growing rapidly, which are mostly achieved through direct imports and exports of intermediate products or through other provinces in China. And we combined valueadded with carbon emissions to build an environmental inequity index model, and found that in the GVCs, the profitability of Shanxi actually deteriorated.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82635542","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}
Accurate estimation of state of charge (SOC) is greatly crucial for safely and reliably charging and discharging the lithium-ion batteries, especially for those used in electric vehicles (EVs). Currently, a lot of algorithms have been proposed to estimate the battery SOC. In this paper, we compared four filter-based algorithms, including the standard particle filter (PF), the unscented Kalman filter, the unscented Kalman-particle filter (UPF), and the extended Kalman-particle filter (EPF), in terms of the estimate accuracy and convergence rate. The federal urban driving schedule (FUDS) and the urban dynamometer driving schedule (UDDS) were applied to evaluate the performance of these estimation algorithms. Comparison results showed that compared with the UKF, the PF can improve the estimate accuracy, however, it takes much more time to correct the initial SOC error. By introducing the EKF and UKF into the particle filter, the convergence rate can be greatly improved without the decrease in estimate accuracy, and convergence rate is very close to that of the UKF.
{"title":"Comparative Study of Four Filter-based Algorithms for State-of-charge Estimation of Lithium-ion Batteries","authors":"Yong Tian, Zhibing Zeng, Lijuan Xiang, Xiaoyu Li, Jindong Tian","doi":"10.12783/dteees/iceee2019/31820","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31820","url":null,"abstract":"Accurate estimation of state of charge (SOC) is greatly crucial for safely and reliably charging and discharging the lithium-ion batteries, especially for those used in electric vehicles (EVs). Currently, a lot of algorithms have been proposed to estimate the battery SOC. In this paper, we compared four filter-based algorithms, including the standard particle filter (PF), the unscented Kalman filter, the unscented Kalman-particle filter (UPF), and the extended Kalman-particle filter (EPF), in terms of the estimate accuracy and convergence rate. The federal urban driving schedule (FUDS) and the urban dynamometer driving schedule (UDDS) were applied to evaluate the performance of these estimation algorithms. Comparison results showed that compared with the UKF, the PF can improve the estimate accuracy, however, it takes much more time to correct the initial SOC error. By introducing the EKF and UKF into the particle filter, the convergence rate can be greatly improved without the decrease in estimate accuracy, and convergence rate is very close to that of the UKF.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76406353","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 : 2019-10-31DOI: 10.12783/dteees/iceee2019/31784
Song Qingchao, Chen Jiawei, M. Tan, He Jian, Cai Kuncheng
In this paper, a decentralized energy management strategy for the fuel cell-ultracapacitor hybrid vehicle. Without communication between the fuel cell unit and ultracapacitor unit, this strategy not only achieves fluctuating load power splitting and recycles braking energy, but also ensures the global stability of system, which is the novelty of this paper. Simulation results based on Matlab/Simulink show the effectiveness and feasibility of the proposed control strategy.
{"title":"A Decentralized Energy Management Strategy of Fuel Cell-Ultracapacitor Hybrid Vehicle","authors":"Song Qingchao, Chen Jiawei, M. Tan, He Jian, Cai Kuncheng","doi":"10.12783/dteees/iceee2019/31784","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31784","url":null,"abstract":"In this paper, a decentralized energy management strategy for the fuel cell-ultracapacitor hybrid vehicle. Without communication between the fuel cell unit and ultracapacitor unit, this strategy not only achieves fluctuating load power splitting and recycles braking energy, but also ensures the global stability of system, which is the novelty of this paper. Simulation results based on Matlab/Simulink show the effectiveness and feasibility of the proposed control strategy.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76942471","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 : 2019-10-31DOI: 10.12783/dteees/iceee2019/31801
Menglin Li, Hongwen He, Jiankun Peng, Yong Chen, Mei Yan
The driver's intention determines the vehicle's Macrodriving state. On the premise of ensuring that the driving state of the vehicle conforms to the driver's intention, it is of practical significance to study the energy-saving of the vehicle. Through the correlation analysis of acceleration / brake pedal signal with vehicle speed and acceleration under real working conditions, the strong correlation between driver input and vehicle speed appears in the range of 4-6 seconds. The mapping relationship between driver’s intention and driver's expected speed is constructed by extreme learning machine. Based on this, the model predictive control for hybrid electric vehicle is carried out. It is compared with the global optimal control strategy solved by dynamic programming and the instantaneous optimal control strategy under the same discrete precision. The results show that compared with the instantaneous optimal control strategy, model predictive control based on driver’s intention can save 9.92% of the energy consumption while meeting the driver’s intention (RMSE 0.9995m/s).
{"title":"Predictive Energy Management for Hybrid Electric Vehicle Considering Driver’s Intention","authors":"Menglin Li, Hongwen He, Jiankun Peng, Yong Chen, Mei Yan","doi":"10.12783/dteees/iceee2019/31801","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31801","url":null,"abstract":"The driver's intention determines the vehicle's Macrodriving state. On the premise of ensuring that the driving state of the vehicle conforms to the driver's intention, it is of practical significance to study the energy-saving of the vehicle. Through the correlation analysis of acceleration / brake pedal signal with vehicle speed and acceleration under real working conditions, the strong correlation between driver input and vehicle speed appears in the range of 4-6 seconds. The mapping relationship between driver’s intention and driver's expected speed is constructed by extreme learning machine. Based on this, the model predictive control for hybrid electric vehicle is carried out. It is compared with the global optimal control strategy solved by dynamic programming and the instantaneous optimal control strategy under the same discrete precision. The results show that compared with the instantaneous optimal control strategy, model predictive control based on driver’s intention can save 9.92% of the energy consumption while meeting the driver’s intention (RMSE 0.9995m/s).","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85240394","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 : 2019-10-31DOI: 10.12783/dteees/iceee2019/31761
O. Alrebei, Ali Al-Doboon, P. Bowen, A. Valera-Medina
Due to strict emission control regulations on gas turbines, power generation industries are liable for maintaining CO2 and NOx emission levels within allowable tolerance margins. CARSOXY gas turbines eliminate NOx emissions by replacing Air/fuel combustion with CO2-Argon-Steam/oxyfuel combustion. In addition, CARSOXY gas turbines control carbon emissions by Carbon Capture and Storage (CCS) techniques. CARSOXY gas turbines have demonstrated an increase in efficiency by 13.93%. This paper performs comparable techno-economic analyses between CARSOXY and Air-driven gas turbine cycles using the same amount of CH4 fuel. Both cycles have been modeled and economically analyzed using ASPEN PLUS. The CARSOXY cycle has demonstrated to be more economically sustainable than the Air-driven gas turbine. The Modified Internal Rate of Return (MIRR) of the CARSOXY cycle is approximately 2.2% higher than that for the Air-driven cycle. Moreover, the profitability index (PI) of the CARSOXY cycle is 1.72, while it is only 1.28 for the Air-driven cycle.
{"title":"Techno-economics of CO2-Argon-Steam Oxy-Fuel (CARSOXY) Gas Turbines","authors":"O. Alrebei, Ali Al-Doboon, P. Bowen, A. Valera-Medina","doi":"10.12783/dteees/iceee2019/31761","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31761","url":null,"abstract":"Due to strict emission control regulations on gas turbines, power generation industries are liable for maintaining CO2 and NOx emission levels within allowable tolerance margins. CARSOXY gas turbines eliminate NOx emissions by replacing Air/fuel combustion with CO2-Argon-Steam/oxyfuel combustion. In addition, CARSOXY gas turbines control carbon emissions by Carbon Capture and Storage (CCS) techniques. CARSOXY gas turbines have demonstrated an increase in efficiency by 13.93%. This paper performs comparable techno-economic analyses between CARSOXY and Air-driven gas turbine cycles using the same amount of CH4 fuel. Both cycles have been modeled and economically analyzed using ASPEN PLUS. The CARSOXY cycle has demonstrated to be more economically sustainable than the Air-driven gas turbine. The Modified Internal Rate of Return (MIRR) of the CARSOXY cycle is approximately 2.2% higher than that for the Air-driven cycle. Moreover, the profitability index (PI) of the CARSOXY cycle is 1.72, while it is only 1.28 for the Air-driven cycle.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72813929","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 : 2019-10-31DOI: 10.12783/dteees/iceee2019/31783
Qiuping Li, Sanmang Wu
Controlling carbon dioxide (CO ) emissions has been an important measure to mitigate global climate change, and the study of household consumption and related environmental impacts has become a hot topic in the field of sustainable development research in recent years. Taking 2000–2014 as the research period, this paper studies the indirect CO emissions of household consumption in China by using the MRIO model and uses SDA to analyze the driving factors of indirect CO emissions of household consumption in China. The results show that: (1) During the study period, the indirect CO emissions caused by household consumption in China showed an increasing trend, with the total household amount increasing by 1.9 times, and the per capita CO emissions increasing by 1.76 times. (2) The CO emissions from household consumption in China are concentrated mainly in Commercial and Public Services (CPS), Electricity, Gas, Steam and Air Conditioning Supply (EGSA), and Manufacture of Food and Tobacco (MFT), which accounted for 26.63%, 17.69% and 13.52%, respectively, of the total emissions in 2014. (3) China has been in the position of net outflow of indirect CO emissions from household consumption. (4) The growth of per capita household consumption is the main factor promoting the growth of indirect CO emissions from household consumption in China, and the reduction of carbon intensity in various countries is the main factor in restraining the indirect CO emissions caused by household consumption in China. This study shows that indirect CO emissions from household consumption in China are likely to rise, and the government should not only improve the level of household consumption continuously but also actively adjust the industrial structure and optimize the consumption structure to alleviate CO2 emissions effectively.
{"title":"Changes and Driving Forces of Indirect CO2 Emissions from Household Consumption in China—Based on an International Comparative Perspective","authors":"Qiuping Li, Sanmang Wu","doi":"10.12783/dteees/iceee2019/31783","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31783","url":null,"abstract":"Controlling carbon dioxide (CO ) emissions has been an important measure to mitigate global climate change, and the study of household consumption and related environmental impacts has become a hot topic in the field of sustainable development research in recent years. Taking 2000–2014 as the research period, this paper studies the indirect CO emissions of household consumption in China by using the MRIO model and uses SDA to analyze the driving factors of indirect CO emissions of household consumption in China. The results show that: (1) During the study period, the indirect CO emissions caused by household consumption in China showed an increasing trend, with the total household amount increasing by 1.9 times, and the per capita CO emissions increasing by 1.76 times. (2) The CO emissions from household consumption in China are concentrated mainly in Commercial and Public Services (CPS), Electricity, Gas, Steam and Air Conditioning Supply (EGSA), and Manufacture of Food and Tobacco (MFT), which accounted for 26.63%, 17.69% and 13.52%, respectively, of the total emissions in 2014. (3) China has been in the position of net outflow of indirect CO emissions from household consumption. (4) The growth of per capita household consumption is the main factor promoting the growth of indirect CO emissions from household consumption in China, and the reduction of carbon intensity in various countries is the main factor in restraining the indirect CO emissions caused by household consumption in China. This study shows that indirect CO emissions from household consumption in China are likely to rise, and the government should not only improve the level of household consumption continuously but also actively adjust the industrial structure and optimize the consumption structure to alleviate CO2 emissions effectively.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88675188","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 : 2019-10-31DOI: 10.12783/dteees/iceee2019/31803
Shuangqi Li, Hongwen He, Jianwei Li
A precise mathematical model is crucial for the battery management system to ensure the secure and stable battery operation. This paper presents a big data-driven battery management method utilizing the Support Vector Regression (SVR) algorithm, with the ability to work stably under dynamic conditions and whole battery life cycle. The rain-flow cycle counting algorithm is used to reflect the battery degradation phenomenon in this paper, and The SVR algorithm is used to establish the battery model. The idea is to reduce the impact of data quality on the model, so as to utilize the battery big data effectively and improve the battery modeling accuracy. Finally, a conjunction working mode between the Cloud-based BMS (C-BMS) and BMS in vehicles (V-BMS) is also proposed, provided as an applied case of the model. Using the battery data to verify the model effectiveness and accuracy, the error of the battery SoC estimation is within 3%.
{"title":"A Cloud-based Aging Considered Vehicle-mounted Lithium-ion Battery Management Method: A Big Data Perspective","authors":"Shuangqi Li, Hongwen He, Jianwei Li","doi":"10.12783/dteees/iceee2019/31803","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31803","url":null,"abstract":"A precise mathematical model is crucial for the battery management system to ensure the secure and stable battery operation. This paper presents a big data-driven battery management method utilizing the Support Vector Regression (SVR) algorithm, with the ability to work stably under dynamic conditions and whole battery life cycle. The rain-flow cycle counting algorithm is used to reflect the battery degradation phenomenon in this paper, and The SVR algorithm is used to establish the battery model. The idea is to reduce the impact of data quality on the model, so as to utilize the battery big data effectively and improve the battery modeling accuracy. Finally, a conjunction working mode between the Cloud-based BMS (C-BMS) and BMS in vehicles (V-BMS) is also proposed, provided as an applied case of the model. Using the battery data to verify the model effectiveness and accuracy, the error of the battery SoC estimation is within 3%.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86806052","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 : 2019-10-31DOI: 10.12783/dteees/iceee2019/31798
Yonggang Liu, Junjun Liu, D. Qin, Zheng Chen
In this paper, a rule learning-based energy management strategy is proposed to achieve optimal energy consumption economy and prolong the batteries lifetime for a fuel cell hybrid electric vehicle (FCHEV). First, the Pontryagin’s minimum principle (PMP) is used to obtain the optimal global solution. Then, K-means algorithm is adopted to simplify the optimal database composed of the optimized data based on PMP and the corresponding driving cycle features. According to the simplification data set, the improved repeated incremental pruning to produce error reduction (RIPPER) algorithm based on rule learning theory is used to learn the rules. Finally, the multiple linear regression algorithms are utilized to fit the data in the rule set. Simulation results validate that the proposed strategy can achieve more than 94% of the PMP strategy in the fuel consumption and also can prolong the batteries lifetime.
{"title":"Multi-Objective Optimization Energy Management Strategy for Fuel Cell Hybrid Vehicles Based on Rule Learning","authors":"Yonggang Liu, Junjun Liu, D. Qin, Zheng Chen","doi":"10.12783/dteees/iceee2019/31798","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31798","url":null,"abstract":"In this paper, a rule learning-based energy management strategy is proposed to achieve optimal energy consumption economy and prolong the batteries lifetime for a fuel cell hybrid electric vehicle (FCHEV). First, the Pontryagin’s minimum principle (PMP) is used to obtain the optimal global solution. Then, K-means algorithm is adopted to simplify the optimal database composed of the optimized data based on PMP and the corresponding driving cycle features. According to the simplification data set, the improved repeated incremental pruning to produce error reduction (RIPPER) algorithm based on rule learning theory is used to learn the rules. Finally, the multiple linear regression algorithms are utilized to fit the data in the rule set. Simulation results validate that the proposed strategy can achieve more than 94% of the PMP strategy in the fuel consumption and also can prolong the batteries lifetime.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84443443","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 : 2019-10-31DOI: 10.12783/dteees/iceee2019/31826
Bin Wang, Jinshan Wang, Hailong Li, E. Thorin, Sebastian Schwede
Using sewage sludge as fuel has been considered an effective way to handle the sludge. However, due to the high water content, sludge is required to be dried before it can be burnt. In this paper, a paddle sewage sludge dryer was proposed to add after bag house gas filter (BHF) and before flue gas quench (FGQ), which can use the waste heat of the flue gas (FG) and therefore, improve the overall efficiency of the biomass-based plant and sludge handling. A mathematical model was developed to simulate the heat transfer in the paddle dryer, which was validated through the comparison with measured data. Based on this model, the impacts of flow rate of sludge and stirrer speed on the moisture content of exit sludge, temperature of exit sludge were studied. The results show that the increase of stirrer speed or the decrease of sludge mass flow rate all can reduce the moisture content of exit sludge.
{"title":"Feasibility Analysis of Drying Sludge Using Flue Gas Waste Heat","authors":"Bin Wang, Jinshan Wang, Hailong Li, E. Thorin, Sebastian Schwede","doi":"10.12783/dteees/iceee2019/31826","DOIUrl":"https://doi.org/10.12783/dteees/iceee2019/31826","url":null,"abstract":"Using sewage sludge as fuel has been considered an effective way to handle the sludge. However, due to the high water content, sludge is required to be dried before it can be burnt. In this paper, a paddle sewage sludge dryer was proposed to add after bag house gas filter (BHF) and before flue gas quench (FGQ), which can use the waste heat of the flue gas (FG) and therefore, improve the overall efficiency of the biomass-based plant and sludge handling. A mathematical model was developed to simulate the heat transfer in the paddle dryer, which was validated through the comparison with measured data. Based on this model, the impacts of flow rate of sludge and stirrer speed on the moisture content of exit sludge, temperature of exit sludge were studied. The results show that the increase of stirrer speed or the decrease of sludge mass flow rate all can reduce the moisture content of exit sludge.","PeriodicalId":11324,"journal":{"name":"DEStech Transactions on Environment, Energy and Earth Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88334757","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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