Green Energy and Resources最新文献

Optimal control strategy based on artificial intelligence applied to a continuous dark fermentation reactor for energy recovery from organic wastes

Green Energy and Resources

Pub Date : 2025-02-01 DOI: 10.1016/j.gerr.2024.100112

Kelly Joel Gurubel Tun , Elizabeth León-Becerril , Octavio García-Depraect

Dark fermentation process from low-cost renewable substrates for simultaneous wastewater treatment and hydrogen production (H₂) is suitable due to economic viability and environmental sustainability. This work explores the application of an innovative control strategy in a scale fermentation bioreactor designed for energy recovery from organic wastes. This approach not only promotes low carbon emissions but also offers significant potential for industrial application. Machine learning (ML) and optimization methods are used to model the nonlinear process and then, a neural predictive control (NPC) strategy to drive the system to its optimal operating order under varying influent conditions is developed. Predictive control uses the Newton-Raphson as the optimization algorithm and a multi-layer feedforward neural network for the state prediction. This strategy has demonstrated to be a viable algorithm for real-time control applications. First, experimental data from continuous dark fermentation are modeled using support vector machine (SVM) algorithm for response prediction and then, optimization algorithms are employed to identify the key parameters that enhance H₂ production. These optimal operating parameters are then used to create reference trajectory signals within a NPC scheme to achieve the optimal hydrogen production rate. The control strategy led to an HPR mean of 12.35 ± 1.2 NL H₂/L-d under pseudo-steady state with hydrogen content in the gaseous phase of 63 % v/v, and a maximum COD recovery of 90 ± 2.8 %. The results demonstrate that this innovative control method can significantly improve the performance and efficiency of biogas plants, showing viability for large-scale industrial implementation.

{"title":"Optimal control strategy based on artificial intelligence applied to a continuous dark fermentation reactor for energy recovery from organic wastes","authors":"Kelly Joel Gurubel Tun , Elizabeth León-Becerril , Octavio García-Depraect","doi":"10.1016/j.gerr.2024.100112","DOIUrl":"10.1016/j.gerr.2024.100112","url":null,"abstract":"<div><div>Dark fermentation process from low-cost renewable substrates for simultaneous wastewater treatment and hydrogen production (H<sub>2</sub>) is suitable due to economic viability and environmental sustainability. This work explores the application of an innovative control strategy in a scale fermentation bioreactor designed for energy recovery from organic wastes. This approach not only promotes low carbon emissions but also offers significant potential for industrial application. Machine learning (ML) and optimization methods are used to model the nonlinear process and then, a neural predictive control (NPC) strategy to drive the system to its optimal operating order under varying influent conditions is developed. Predictive control uses the Newton-Raphson as the optimization algorithm and a multi-layer feedforward neural network for the state prediction. This strategy has demonstrated to be a viable algorithm for real-time control applications. First, experimental data from continuous dark fermentation are modeled using support vector machine (SVM) algorithm for response prediction and then, optimization algorithms are employed to identify the key parameters that enhance H<sub>2</sub> production. These optimal operating parameters are then used to create reference trajectory signals within a NPC scheme to achieve the optimal hydrogen production rate. The control strategy led to an HPR mean of 12.35 ± 1.2 NL H<sub>2</sub>/L-d under pseudo-steady state with hydrogen content in the gaseous phase of 63 % <em>v/v</em>, and a maximum COD recovery of 90 ± 2.8 %. The results demonstrate that this innovative control method can significantly improve the performance and efficiency of biogas plants, showing viability for large-scale industrial implementation.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 1","pages":"Article 100112"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parametric study of the decomposition of methane for COx-free H2 and high valued carbon using Ni-based catalyst via machine-learning simulation

Green Energy and Resources

Pub Date : 2025-02-01 DOI: 10.1016/j.gerr.2025.100114

Dinghao Xue , Pingyang Zhang , Yuanyuan Lin , Wenshuo Wang , Jiachang Shi , Qiang Hu , Gartzen Lopez , Cristina Moliner , Jin Sun , Tao Wang , Xinyan Zhang , Yingping Pang , Xiqiang Zhao , Yanpeng Mao , Zhanlong Song , Ziliang Wang , Wenlong Wang

With industrial informatization, abundant data provides solutions for the digital design of methane-based hydrogen production. Catalytic methane decomposition (CMD) is a promising strategy for COx-free hydrogen production, with high-value carbon products generated. However, affected by various factors, the proper process parameters are challenge to be ascertained by the time-consuming experimental method. In this study, five machine learning methods were utilized for the precise prediction of methane conversion using Ni-based catalysts. Combined with SHAP method and univariate analysis method, XGBoost model with the best accuracy (with R² = 0.894, RSME = 7.724) was selected for the exploration of the reaction impact of active phase loading, support loading, and reaction conditions in methane convention, hydrogen production, carbon yield, and carbon quality. The result shows that methane conversion rate is mainly influenced by space velocity, reaction temperature, nickel loading, and methane percentage. Copper doping significantly affects carbon yield and its quality, and there is a strong bond between Ni and Al₂O₃, contributing the most to the reaction. This work would provide a guidance for the efficient catalyst design and effective hydrogen production.

{"title":"Parametric study of the decomposition of methane for COx-free H2 and high valued carbon using Ni-based catalyst via machine-learning simulation","authors":"Dinghao Xue , Pingyang Zhang , Yuanyuan Lin , Wenshuo Wang , Jiachang Shi , Qiang Hu , Gartzen Lopez , Cristina Moliner , Jin Sun , Tao Wang , Xinyan Zhang , Yingping Pang , Xiqiang Zhao , Yanpeng Mao , Zhanlong Song , Ziliang Wang , Wenlong Wang","doi":"10.1016/j.gerr.2025.100114","DOIUrl":"10.1016/j.gerr.2025.100114","url":null,"abstract":"<div><div>With industrial informatization, abundant data provides solutions for the digital design of methane-based hydrogen production. Catalytic methane decomposition (CMD) is a promising strategy for COx-free hydrogen production, with high-value carbon products generated. However, affected by various factors, the proper process parameters are challenge to be ascertained by the time-consuming experimental method. In this study, five machine learning methods were utilized for the precise prediction of methane conversion using Ni-based catalysts. Combined with SHAP method and univariate analysis method, XGBoost model with the best accuracy (with R<sup>2</sup> = 0.894, RSME = 7.724) was selected for the exploration of the reaction impact of active phase loading, support loading, and reaction conditions in methane convention, hydrogen production, carbon yield, and carbon quality. The result shows that methane conversion rate is mainly influenced by space velocity, reaction temperature, nickel loading, and methane percentage. Copper doping significantly affects carbon yield and its quality, and there is a strong bond between Ni and Al<sub>2</sub>O<sub>3</sub>, contributing the most to the reaction. This work would provide a guidance for the efficient catalyst design and effective hydrogen production.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 1","pages":"Article 100114"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using machine learning methods for long-term technical and economic evaluation of wind power plants

Green Energy and Resources

Pub Date : 2025-02-01 DOI: 10.1016/j.gerr.2025.100115

Ali Omidkar, Razieh Es'haghian, Hua Song

The depletion of hydrocarbon reserves and the impact of global warming have posed significant challenges to the continued use of fossil fuels. Consequently, renewable energy sources have garnered substantial attention, with some countries now deriving a significant portion of their total energy needs from these alternatives. Among renewable sources, wind energy has been recognized as one of the most accessible and clean. However, it is imperative to evaluate wind power plants both technically and economically. This involves calculating the levelized cost of energy in comparison to fossil-based energy sources and predicting the minimum and maximum energy output over the long term. Achieving this requires long-term forecasts of wind speeds at specific locations, which involve complex mathematical modeling and computations typically performed by supercomputers. In this study, a data-driven machine learning model has been employed to predict wind speeds in Calgary over a 25-year period with minimal CPU time. Throughout the power plant's operational life, the optimal model was also used to calculate the annual energy production. The hybrid CNN-LSTM model demonstrated superior accuracy based on model accuracy metrics. Consequently, the levelized cost of energy produced by the plant was calculated at $0.09 per kWh, which is competitive within the Canadian electricity market. The investment reached a breakeven point in approximately six years, which is deemed acceptable.

{"title":"Using machine learning methods for long-term technical and economic evaluation of wind power plants","authors":"Ali Omidkar, Razieh Es'haghian, Hua Song","doi":"10.1016/j.gerr.2025.100115","DOIUrl":"10.1016/j.gerr.2025.100115","url":null,"abstract":"<div><div>The depletion of hydrocarbon reserves and the impact of global warming have posed significant challenges to the continued use of fossil fuels. Consequently, renewable energy sources have garnered substantial attention, with some countries now deriving a significant portion of their total energy needs from these alternatives. Among renewable sources, wind energy has been recognized as one of the most accessible and clean. However, it is imperative to evaluate wind power plants both technically and economically. This involves calculating the levelized cost of energy in comparison to fossil-based energy sources and predicting the minimum and maximum energy output over the long term. Achieving this requires long-term forecasts of wind speeds at specific locations, which involve complex mathematical modeling and computations typically performed by supercomputers. In this study, a data-driven machine learning model has been employed to predict wind speeds in Calgary over a 25-year period with minimal CPU time. Throughout the power plant's operational life, the optimal model was also used to calculate the annual energy production. The hybrid CNN-LSTM model demonstrated superior accuracy based on model accuracy metrics. Consequently, the levelized cost of energy produced by the plant was calculated at $0.09 per kWh, which is competitive within the Canadian electricity market. The investment reached a breakeven point in approximately six years, which is deemed acceptable.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 1","pages":"Article 100115"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Potentials and effects of electricity cogeneration via ORC integration in small-scale biomass district heating system

Green Energy and Resources

Pub Date : 2025-02-01 DOI: 10.1016/j.gerr.2024.100113

Truong Nguyen , Leteng Lin

This study explores the potential and impact of electricity cogeneration using Organic Rankine Cycle (ORC) integrated with small-scale biomass boilers within district heating systems. An analysis is conducted on a 3 MW_th biomass-fired district heating plant in southern Sweden. Process monitoring data, collected over a one-year period from the plant, serves as the basis for simulation and analysis. The study examines operational changes and fuel usage at a local level, together with an extension to a regional scale considering both short-term and long-term energy system implications. The results show that integrating a 200 kW_e ORC unit with the existing boiler having a flue gas condenser is cost-optimal and could cogenerate approximately 1.1 GWh electricity annually, with a levelized electricity cost of €64.4 per MWh. This is equivalent to a system power-to-heat ratio of 7.5%. From a broader energy system perspective, this efficient integration could potentially reduce CO₂ emissions by 234∼454 tons per year when the saved energy locally is used to replace fossil fuels in the energy system, depending on how biomass is utilized and what type of fossil fuels are replaced. Increasing installed capacity of ORC unit to maximize electricity co-generation could result in a carbon abatement cost ranging from €204 to €79 per ton CO₂. This cost fluctuates depending on the installed capacity, operation of the ORC units, and prevailing electricity prices. The study highlights the trade-off between financial gains and CO₂ emission reductions, underscoring the complex decision-making involved in energy system optimization.

{"title":"Potentials and effects of electricity cogeneration via ORC integration in small-scale biomass district heating system","authors":"Truong Nguyen , Leteng Lin","doi":"10.1016/j.gerr.2024.100113","DOIUrl":"10.1016/j.gerr.2024.100113","url":null,"abstract":"<div><div>This study explores the potential and impact of electricity cogeneration using Organic Rankine Cycle (ORC) integrated with small-scale biomass boilers within district heating systems. An analysis is conducted on a 3 MW<sub>th</sub> biomass-fired district heating plant in southern Sweden. Process monitoring data, collected over a one-year period from the plant, serves as the basis for simulation and analysis. The study examines operational changes and fuel usage at a local level, together with an extension to a regional scale considering both short-term and long-term energy system implications. The results show that integrating a 200 kW<sub>e</sub> ORC unit with the existing boiler having a flue gas condenser is cost-optimal and could cogenerate approximately 1.1 GWh electricity annually, with a levelized electricity cost of €64.4 per MWh. This is equivalent to a system power-to-heat ratio of 7.5%. From a broader energy system perspective, this efficient integration could potentially reduce CO<sub>2</sub> emissions by 234∼454 tons per year when the saved energy locally is used to replace fossil fuels in the energy system, depending on how biomass is utilized and what type of fossil fuels are replaced. Increasing installed capacity of ORC unit to maximize electricity co-generation could result in a carbon abatement cost ranging from €204 to €79 per ton CO<sub>2</sub>. This cost fluctuates depending on the installed capacity, operation of the ORC units, and prevailing electricity prices. The study highlights the trade-off between financial gains and CO<sub>2</sub> emission reductions, underscoring the complex decision-making involved in energy system optimization.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 1","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigation of highly efficient CO2 hydrogenation at ambient conditions using dielectric barrier discharge plasma 环境条件下介质阻挡放电等离子体高效CO2加氢研究

Green Energy and Resources

Pub Date : 2024-12-01 DOI: 10.1016/j.gerr.2024.100102

Zhihao Zeng , Yujiao Li , Yunfei Ma , Xiaoqing Lin , Xiangbo Zou , Hao Zhang , Xiaodong Li , Qingyang Lin , Ming-Liang Qu , Zengyi Ma , Angjian Wu

The increasing utilization of CO₂ for synthesizing high-value fuels or essential chemicals is a potentially effective approach to mitigating global warming and climate change. Compared to thermal catalytic CO₂ conversion under harsh operating conditions (400∼500°C, 10 MPa), non-thermal plasma can overcome kinetic barriers and trigger reactions beyond thermal equilibrium at ambient temperature and pressure. In this study, the effects of operating conditions (discharge frequency, input power, and gas flow rate) and geometrical parameters (discharge length, discharge gap, and dielectric materials) have been extensively analyzed using typical cylindrical dielectric barrier discharge (DBD) plasma. The discharge characteristics changed by operating conditions (including waveforms of applied voltage and current) are compared, indicating higher applied voltage and lower gas flow rate can strengthen the filamentary discharges. The results demonstrate CO₂ conversion rate increases with the increase of applied voltage and the decrease of CO₂/H₂ ratio, achieving its maximum value of 43.0% at 20 mL/min. The highest energy efficiency of 3771.9 μg/kJ for CO generation is obtained at the applied voltage of 5.5 kV and gas flow rate of 40 mL/min, respectively. Besides, the structure of plasma reactor also impacts the performance of CO₂ conversion. On the one hand, the discharge gap has a significant role in the variation of CO₂ conversion and product selectivity, which is attributed to the electric field density and corresponding electron-induced reaction. On the other hand, the circulating water-cooling jacket was used to find out the influence of reaction temperature, which switched the product from CO to CH₄. This work will pave the way for a sustainable alternative towards future CO₂ conversion and utilization.

增加利用二氧化碳合成高价值燃料或基本化学品是减缓全球变暖和气候变化的潜在有效方法。与恶劣操作条件下（400 ~ 500°C, 10 MPa）的热催化CO2转化相比，非热等离子体可以克服动力学障碍，并在环境温度和压力下触发超过热平衡的反应。在本研究中，使用典型的圆柱形介质阻挡放电（DBD）等离子体，对工作条件（放电频率、输入功率和气体流速）和几何参数（放电长度、放电间隙和介质材料）的影响进行了广泛的分析。比较了不同工况下的放电特性变化（包括外加电压和电流波形），表明较高的外加电压和较低的气体流量可以增强细丝放电。结果表明：CO2转化率随施加电压的增大和CO2/H2比的减小而增大，在20 mL/min时达到最大值43.0%；当施加电压为5.5 kV、气体流速为40 mL/min时，CO的能量效率最高，为3771.9 μg/kJ。此外，等离子体反应器的结构也会影响CO2转化的性能。一方面，放电间隙对CO2转化率和产物选择性的变化有显著影响，这与电场密度和相应的电子诱导反应有关。另一方面，利用循环水冷却夹套考察反应温度的影响，使产物由CO转化为CH4。这项工作将为未来二氧化碳转化和利用的可持续替代方案铺平道路。

{"title":"Investigation of highly efficient CO2 hydrogenation at ambient conditions using dielectric barrier discharge plasma","authors":"Zhihao Zeng , Yujiao Li , Yunfei Ma , Xiaoqing Lin , Xiangbo Zou , Hao Zhang , Xiaodong Li , Qingyang Lin , Ming-Liang Qu , Zengyi Ma , Angjian Wu","doi":"10.1016/j.gerr.2024.100102","DOIUrl":"10.1016/j.gerr.2024.100102","url":null,"abstract":"<div><div>The increasing utilization of CO<sub>2</sub> for synthesizing high-value fuels or essential chemicals is a potentially effective approach to mitigating global warming and climate change. Compared to thermal catalytic CO<sub>2</sub> conversion under harsh operating conditions (400∼500°C, 10 MPa), non-thermal plasma can overcome kinetic barriers and trigger reactions beyond thermal equilibrium at ambient temperature and pressure. In this study, the effects of operating conditions (discharge frequency, input power, and gas flow rate) and geometrical parameters (discharge length, discharge gap, and dielectric materials) have been extensively analyzed using typical cylindrical dielectric barrier discharge (DBD) plasma. The discharge characteristics changed by operating conditions (including waveforms of applied voltage and current) are compared, indicating higher applied voltage and lower gas flow rate can strengthen the filamentary discharges. The results demonstrate CO<sub>2</sub> conversion rate increases with the increase of applied voltage and the decrease of CO<sub>2</sub>/H<sub>2</sub> ratio, achieving its maximum value of 43.0% at 20 mL/min. The highest energy efficiency of 3771.9 μg/kJ for CO generation is obtained at the applied voltage of 5.5 kV and gas flow rate of 40 mL/min, respectively. Besides, the structure of plasma reactor also impacts the performance of CO<sub>2</sub> conversion. On the one hand, the discharge gap has a significant role in the variation of CO<sub>2</sub> conversion and product selectivity, which is attributed to the electric field density and corresponding electron-induced reaction. On the other hand, the circulating water-cooling jacket was used to find out the influence of reaction temperature, which switched the product from CO to CH<sub>4</sub>. This work will pave the way for a sustainable alternative towards future CO<sub>2</sub> conversion and utilization.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"2 4","pages":"Article 100102"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application and extension of diesel spray theory in analysis of methanol spray characteristics under high-pressure injection conditions

Green Energy and Resources

Pub Date : 2024-12-01 DOI: 10.1016/j.gerr.2024.100103

Pengbo Dong , Yifan Zhang , Yang Wang , Wuqiang Long , Jiangping Tian , Hua Tian , Keiya Nishida

Methanol has received widespread attention as a kind of alternative fuel for internal combustion engines because of its wide range of sources, low price, low combustion emission pollution, and carbon neutrality. Meanwhile, the relatively developed diesel spray theories have a great reference value to theoretical analysis of high-pressure methanol injection. Based on the optical experiment of the methanol sprays under high-pressure injection conditions, the empirical models for predicting spray tip penetration, spray angle, spray area, and spray volume of diesel were used to calculate the parameters of the methanol sprays. These calculation values were then compared with the experimental values to establish empirical models of high-pressure methanol spray characteristics. On this basis, an assessment of the adaptability of the diesel spray similarity theory applied to the high-pressure methanol sprays was conducted under similarity conditions. The results show that Wakuri's model has the best predictive performance on the methanol spray tip penetration (the average relative error is 4.31%), and Inagaki's model provides the most precise predictions on the methanol spray angle (the average relative error is 2.63%). After correcting the constants, empirical models that can describe the methanol spray characteristics in this experiment were proposed. In terms of the similarity theory, the diesel spray similarity theory shows good adaptability to the spray tip penetration and spray angle of the high-pressure methanol sprays with nozzle diameters of 0.12 mm and 0.15 mm under similarity conditions. The above results can serve as a basis for extending diesel spray theory to methanol and for the upsizing or downsizing design of direct injection methanol engines with different bore sizes of the same series.

{"title":"Application and extension of diesel spray theory in analysis of methanol spray characteristics under high-pressure injection conditions","authors":"Pengbo Dong , Yifan Zhang , Yang Wang , Wuqiang Long , Jiangping Tian , Hua Tian , Keiya Nishida","doi":"10.1016/j.gerr.2024.100103","DOIUrl":"10.1016/j.gerr.2024.100103","url":null,"abstract":"<div><div>Methanol has received widespread attention as a kind of alternative fuel for internal combustion engines because of its wide range of sources, low price, low combustion emission pollution, and carbon neutrality. Meanwhile, the relatively developed diesel spray theories have a great reference value to theoretical analysis of high-pressure methanol injection. Based on the optical experiment of the methanol sprays under high-pressure injection conditions, the empirical models for predicting spray tip penetration, spray angle, spray area, and spray volume of diesel were used to calculate the parameters of the methanol sprays. These calculation values were then compared with the experimental values to establish empirical models of high-pressure methanol spray characteristics. On this basis, an assessment of the adaptability of the diesel spray similarity theory applied to the high-pressure methanol sprays was conducted under similarity conditions. The results show that Wakuri's model has the best predictive performance on the methanol spray tip penetration (the average relative error is 4.31%), and Inagaki's model provides the most precise predictions on the methanol spray angle (the average relative error is 2.63%). After correcting the constants, empirical models that can describe the methanol spray characteristics in this experiment were proposed. In terms of the similarity theory, the diesel spray similarity theory shows good adaptability to the spray tip penetration and spray angle of the high-pressure methanol sprays with nozzle diameters of 0.12 mm and 0.15 mm under similarity conditions. The above results can serve as a basis for extending diesel spray theory to methanol and for the upsizing or downsizing design of direct injection methanol engines with different bore sizes of the same series.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"2 4","pages":"Article 100103"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modification approach of Northern Wall to improve the performance of solar greenhouse dryers: A review

Green Energy and Resources

Pub Date : 2024-12-01 DOI: 10.1016/j.gerr.2024.100104

M.C. Ndukwu , Leonard Akuwueke , Godwin Akpan , M.F. Umunna , Godwin Usoh , Inemesit Ekop , Promise Etim , I. Okosa , Francis Orji , E.C. Ikechukwu-Edeh , Ifiok Ekop , Merlin Simo-Tagne , Lyes Bennamoun , Hongwei Wu , Fidelis Abam

Globally, interest is shifting toward green energy due to its environmental appeal. Therefore, to promote energy and environmental conservation in drying, several solar dryers have been developed which offers limitless, clean, and free energy to dry agricultural product. Among these solar dryers, solar greenhouse dryers offer a very simple low-temperature, energy-efficient structure capable of drying large beds of crops by harnessing thermal radiation energy from the sun. To improve the thermal performance in the passive mode especially, several modification approaches have been adopted. This article, therefore, reviewed various possible modification methods that have been adopted to improve the thermal performance of the greenhouse, with a focus on the modification of the northern wall. The various strategies involved in the modification of the north wall structure include creating an opaque north wall with black painted materials, installing a reflective north wall using a mirror, integrating heat storage materials like pebbles or brick, integrating phase change materials into the north wall, digging the soil depth to form a north wall and creating a variable southern roof with a modified north wall. Modifying the northern wall showed higher drying chamber temperature compared to completely transparent convectional greenhouse dryers in all the studies. These modifications can increase the temperature of the modified greenhouse by 13.38∼21.10% for a natural convection solar greenhouse dryer compared to the conventional type. With this approach, the radiation losses from the northern wall can be minimized and the energy management system of the greenhouse can be optimized for higher performance, making it more sustainable and eliminating the use of fossil fuel in agricultural product drying.

{"title":"Modification approach of Northern Wall to improve the performance of solar greenhouse dryers: A review","authors":"M.C. Ndukwu , Leonard Akuwueke , Godwin Akpan , M.F. Umunna , Godwin Usoh , Inemesit Ekop , Promise Etim , I. Okosa , Francis Orji , E.C. Ikechukwu-Edeh , Ifiok Ekop , Merlin Simo-Tagne , Lyes Bennamoun , Hongwei Wu , Fidelis Abam","doi":"10.1016/j.gerr.2024.100104","DOIUrl":"10.1016/j.gerr.2024.100104","url":null,"abstract":"<div><div>Globally, interest is shifting toward green energy due to its environmental appeal. Therefore, to promote energy and environmental conservation in drying, several solar dryers have been developed which offers limitless, clean, and free energy to dry agricultural product. Among these solar dryers, solar greenhouse dryers offer a very simple low-temperature, energy-efficient structure capable of drying large beds of crops by harnessing thermal radiation energy from the sun. To improve the thermal performance in the passive mode especially, several modification approaches have been adopted. This article, therefore, reviewed various possible modification methods that have been adopted to improve the thermal performance of the greenhouse, with a focus on the modification of the northern wall. The various strategies involved in the modification of the north wall structure include creating an opaque north wall with black painted materials, installing a reflective north wall using a mirror, integrating heat storage materials like pebbles or brick, integrating phase change materials into the north wall, digging the soil depth to form a north wall and creating a variable southern roof with a modified north wall. Modifying the northern wall showed higher drying chamber temperature compared to completely transparent convectional greenhouse dryers in all the studies. These modifications can increase the temperature of the modified greenhouse by 13.38∼21.10% for a natural convection solar greenhouse dryer compared to the conventional type. With this approach, the radiation losses from the northern wall can be minimized and the energy management system of the greenhouse can be optimized for higher performance, making it more sustainable and eliminating the use of fossil fuel in agricultural product drying.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"2 4","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Drivers of green energy transition: A review

Green Energy and Resources

Pub Date : 2024-12-01 DOI: 10.1016/j.gerr.2024.100105

Francis Muhire , Dickson Turyareeba , Muyiwa S. Adaramola , Mary Nantongo , Ronnette Atukunda , Anthony M. Olyanga

The pressing need for substantial actions to address climate change is globally recognised, notably through initiatives like the Green Energy Transition (GET) to foster a sustainable future. Despite this global acknowledgement, traditional energy sources maintain their dominance in the worldwide energy sector, with fossil fuels and solid biomass accounting for about 75% of total global Greenhouse Gas (GHG) emissions. The escalating GHG emissions levels directly threaten the climate, leading to global warming and adverse environmental consequences. A systematic literature review was employed to comprehensively examine the conceptualisation and drivers of the GET. The study identified Economic, Social, Political/Legal, Technological, and Environmental factors as drivers of GET. The study revealed diverse perspectives among researchers in conceptualising the GET, with a prevailing consensus that it is a global shift from carbon-intensive to sustainable and low-carbon emission energy alternatives and associated technologies. Predominantly, sustainability transition theories emerged as the most frequently applied conceptual frameworks. Commonly utilised tools for data analysis included Autoregressive Distributed Lag and Generalized Methods of Moments. Recognising the critical role of GET in mitigating GHG emissions and addressing climate change, the results underscore the importance of addressing the identified factors propelling the transition.

{"title":"Drivers of green energy transition: A review","authors":"Francis Muhire , Dickson Turyareeba , Muyiwa S. Adaramola , Mary Nantongo , Ronnette Atukunda , Anthony M. Olyanga","doi":"10.1016/j.gerr.2024.100105","DOIUrl":"10.1016/j.gerr.2024.100105","url":null,"abstract":"<div><div>The pressing need for substantial actions to address climate change is globally recognised, notably through initiatives like the Green Energy Transition (GET) to foster a sustainable future. Despite this global acknowledgement, traditional energy sources maintain their dominance in the worldwide energy sector, with fossil fuels and solid biomass accounting for about 75% of total global Greenhouse Gas (GHG) emissions. The escalating GHG emissions levels directly threaten the climate, leading to global warming and adverse environmental consequences. A systematic literature review was employed to comprehensively examine the conceptualisation and drivers of the GET. The study identified Economic, Social, Political/Legal, Technological, and Environmental factors as drivers of GET. The study revealed diverse perspectives among researchers in conceptualising the GET, with a prevailing consensus that it is a global shift from carbon-intensive to sustainable and low-carbon emission energy alternatives and associated technologies. Predominantly, sustainability transition theories emerged as the most frequently applied conceptual frameworks. Commonly utilised tools for data analysis included Autoregressive Distributed Lag and Generalized Methods of Moments. Recognising the critical role of GET in mitigating GHG emissions and addressing climate change, the results underscore the importance of addressing the identified factors propelling the transition.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"2 4","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Theoretical analysis of organic Rankine cycle for maximum power generation in optimization operation conditions

Green Energy and Resources

Pub Date : 2024-12-01 DOI: 10.1016/j.gerr.2024.100101

Baoju Jia , Yu Lei , Faming Sun , Weisheng Zhou

The global critical issue in energy scarcity should be appropriately solved to realize a sustainable society. Effective use of Rankine cycle is one possible way since it provides most of worldwide electricity production. In this paper, theoretical analysis model of organic working fluids R717, R134a, R1234yf, R290, R245fa and R1233zd in Rankine cycle for maximum power generation in optimization operation using low-temperature heat sources are proposed and studied for development next generation green and zero-carbon energy generation system to promote the race to zero. Results show that temperatures of warm and cold water at inlet, mass flow rate of the warm water and performance of the evaporator play a key role to obtain the theoretical optimization operation conditions for maximum power generation. In the case of same initial conditions of temperatures of warm water (85°C) and cold water (15°C) at inlet, mass flow rate of the warm water (10 kg/s) and performance of the evaporator (100 kW/K), R717 has the best performance in terms of the maximum power output 56.0 kW with thermal efficiency of 8.6%, and the next is the R1233zd (54.4 kW, 8.3%), R245fa (54.0 kW, 8.2%), R134a (52.8 kW, 7.9%), R290 (52.7 kW, 7.9%), and R1234yf (51.7 kW, 7.7%). Here, it should be noticed that other optimization conditions are almost the same (mass flow rate of the cold water 9.1–9.2 kg/s; performance of the condenser 91∼92 kW/K) to get their maximum power output of ORC. In addition, it also known that low-GWP R1233zd (GWP: 1) can deserve the best option to replace R245fa (GWP: 950) and R1234yf (GWP: 4) also can replace r134a (GWP: 1430) since their optimization operation conditions are almost same.

{"title":"Theoretical analysis of organic Rankine cycle for maximum power generation in optimization operation conditions","authors":"Baoju Jia , Yu Lei , Faming Sun , Weisheng Zhou","doi":"10.1016/j.gerr.2024.100101","DOIUrl":"10.1016/j.gerr.2024.100101","url":null,"abstract":"<div><div>The global critical issue in energy scarcity should be appropriately solved to realize a sustainable society. Effective use of Rankine cycle is one possible way since it provides most of worldwide electricity production. In this paper, theoretical analysis model of organic working fluids R717, R134a, R1234yf, R290, R245fa and R1233zd in Rankine cycle for maximum power generation in optimization operation using low-temperature heat sources are proposed and studied for development next generation green and zero-carbon energy generation system to promote the race to zero. Results show that temperatures of warm and cold water at inlet, mass flow rate of the warm water and performance of the evaporator play a key role to obtain the theoretical optimization operation conditions for maximum power generation. In the case of same initial conditions of temperatures of warm water (85°C) and cold water (15°C) at inlet, mass flow rate of the warm water (10 kg/s) and performance of the evaporator (100 kW/K), R717 has the best performance in terms of the maximum power output 56.0 kW with thermal efficiency of 8.6%, and the next is the R1233zd (54.4 kW, 8.3%), R245fa (54.0 kW, 8.2%), R134a (52.8 kW, 7.9%), R290 (52.7 kW, 7.9%), and R1234yf (51.7 kW, 7.7%). Here, it should be noticed that other optimization conditions are almost the same (mass flow rate of the cold water 9.1–9.2 kg/s; performance of the condenser 91∼92 kW/K) to get their maximum power output of ORC. In addition, it also known that low-GWP R1233zd (GWP: 1) can deserve the best option to replace R245fa (GWP: 950) and R1234yf (GWP: 4) also can replace r134a (GWP: 1430) since their optimization operation conditions are almost same.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"2 4","pages":"Article 100101"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigation of bioethanol low-carbon fuel for diesel engines under idling conditions: Combustion, engine performance and emissions 怠速工况下柴油发动机使用生物乙醇低碳燃料的研究：燃烧、发动机性能和排放

Green Energy and Resources

Pub Date : 2024-11-02 DOI: 10.1016/j.gerr.2024.100100

Jun Cong Ge , Lifeng Wang , Hongliang Luo , Nag Jung Choi

In this study, the low idle operation is defined as the engine running at the lowest engine speed with a few slight loads. Idling is necessary for most vehicles, especially for buses and trucks that frequently travel long distances, as drivers often rest inside the vehicle. However, under idling conditions, weak air flow and low air-fuel ratio result in poor air to fuel mixture, ultimately causing incomplete combustion and the production of more harmful exhaust emissions. Bioethanol, as a low-carbon fuel, has great potential for application in diesel engines due to its unique properties. In this research, the influences of different diesel-bioethanol blends (BE0, BE5, BE10, BE15) on combustion and emissions of a diesel engine were investigated under idle conditions. The main results show that there was no phase separation phenomenon even up to 15% bioethanol was directly blended with diesel by volume. And adding bioethanol to diesel had no significant impact on combustion pressure peak, but it postponed the start of combustion (SOC). Surprisingly, the nitrogen oxide (NO_x) and smoke were simultaneously decreased by over 52% and 78% with the intervention of bioethanol, respectively.

在本研究中，低怠速运行是指发动机在最低转速下运行，仅有少量负载。怠速对于大多数车辆都是必要的，尤其是对于经常长途旅行的公共汽车和卡车，因为驾驶员经常在车内休息。然而，在怠速工况下，微弱的气流和较低的空燃比会导致空气与燃料混合不良，最终导致燃烧不完全，产生更多有害废气。生物乙醇作为一种低碳燃料，因其独特的性能而在柴油发动机中具有巨大的应用潜力。本研究调查了不同柴油-生物乙醇混合物（BE0、BE5、BE10、BE15）在怠速条件下对柴油发动机燃烧和排放的影响。主要结果表明，即使生物乙醇与柴油的直接混合比例达到 15%，也不会出现相分离现象。在柴油中添加生物乙醇对燃烧压力峰值没有明显影响，但会推迟燃烧开始时间（SOC）。令人惊讶的是，在生物乙醇的作用下，氮氧化物（NOx）和烟雾同时分别减少了 52% 和 78%。

{"title":"Investigation of bioethanol low-carbon fuel for diesel engines under idling conditions: Combustion, engine performance and emissions","authors":"Jun Cong Ge , Lifeng Wang , Hongliang Luo , Nag Jung Choi","doi":"10.1016/j.gerr.2024.100100","DOIUrl":"10.1016/j.gerr.2024.100100","url":null,"abstract":"<div><div>In this study, the low idle operation is defined as the engine running at the lowest engine speed with a few slight loads. Idling is necessary for most vehicles, especially for buses and trucks that frequently travel long distances, as drivers often rest inside the vehicle. However, under idling conditions, weak air flow and low air-fuel ratio result in poor air to fuel mixture, ultimately causing incomplete combustion and the production of more harmful exhaust emissions. Bioethanol, as a low-carbon fuel, has great potential for application in diesel engines due to its unique properties. In this research, the influences of different diesel-bioethanol blends (BE0, BE5, BE10, BE15) on combustion and emissions of a diesel engine were investigated under idle conditions. The main results show that there was no phase separation phenomenon even up to 15% bioethanol was directly blended with diesel by volume. And adding bioethanol to diesel had no significant impact on combustion pressure peak, but it postponed the start of combustion (SOC). Surprisingly, the nitrogen oxide (NO<sub>x</sub>) and smoke were simultaneously decreased by over 52% and 78% with the intervention of bioethanol, respectively.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"2 4","pages":"Article 100100"},"PeriodicalIF":0.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0