- Ghayur, Adeel, Verheyen, Vincent
- Authors: Ghayur, Adeel , Verheyen, Vincent
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2019
- Full Text: false
- Reviewed:
- Description: Chemical processes offer untapped potential to increase overall system efficiencies by synergizing renewable hydrogen storage with dispatchable renewable energy facilities. In this study an Energy Storage Facility model is developed and simulation conducted to examine this potential. The model incorporates a Solid Oxide Fuel Cell (SOFC) integrated with a Magnesium Hydride (MgH2) Tank and an alkaline electrolyzer linked to the power grid. Surplus grid power is converted to hydrogen and stored as magnesium hydride. This storage process generates waste heat which is used to partially offset the water heating requirement of the electrolyzer. Simulation results demonstrate 20% reduction in parasitic heat energy consumption using this waste heat. Stored hydrogen provides power on demand via the SOFC. Waste heat from SOFC fulfils the desorption heat demand of the MgH2 Tank. Simulation results reveal waste heat from the SOFC is just enough to preheat oxygen and hydrogen and desorb hydrogen from the MgH2 tank. These results are encouraging, warranting further investigation into metal hydride storage to help Australia's transition towards renewable energy resources. © 2019 IEEE.
Carbon negative platform chemicals from waste using enhanced geothermal systems
- Ghayur, Adeel, Verheyen, Vincent
- Authors: Ghayur, Adeel , Verheyen, Vincent
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 14th Greenhouse Gas Control Technologies Conference, GHGT-14; Melbourne, Australian; 21st-26st October 2018 p. 1-4
- Full Text:
- Reviewed:
- Description: Australia has ample geothermal resource, however, it is of low-grade heat and requires Enhanced Geothermal Systems (EGS). Integrating heat recovered via EGS into a lignocellulosic biorefinery opens the avenue for countless opportunities in biomass to products industries. In this study, a biorefinery is modelled that uses heat from a supercritical CO
- Authors: Ghayur, Adeel , Verheyen, Vincent
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 14th Greenhouse Gas Control Technologies Conference, GHGT-14; Melbourne, Australian; 21st-26st October 2018 p. 1-4
- Full Text:
- Reviewed:
- Description: Australia has ample geothermal resource, however, it is of low-grade heat and requires Enhanced Geothermal Systems (EGS). Integrating heat recovered via EGS into a lignocellulosic biorefinery opens the avenue for countless opportunities in biomass to products industries. In this study, a biorefinery is modelled that uses heat from a supercritical CO
Experimental evaluation of methods for reclaiming sulfur loaded amine absorbents
- Garg, Bharti, Pearson, Pauline, Cousins, Ashleigh, Verheyen, Vincent, Puxty, Graeme, Feron, Paul
- Authors: Garg, Bharti , Pearson, Pauline , Cousins, Ashleigh , Verheyen, Vincent , Puxty, Graeme , Feron, Paul
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 14th Greenhouse Gas Control Technologies Conference (GHGT-14); Melbourne, Australia; 21st-26th October 2018 p. 1-8
- Full Text:
- Reviewed:
- Description: Sulfur dioxide (SO2) is a major flue gas contaminant that has a direct effect on the performance of amine-based carbon dioxide capture units operating on power plant flue gases. In many countries, flue gas desulfurisation (FGD) is an essential upstream requirement to CO2 capture systems, thereby increasing the overall operational and capital cost of the capture system. In Australia, the efficacy of CO2 capture may be compromised by the accumulation of SO2 in the absorption solvent. CSIRO’s CS-Cap process is designed to capture of both these acidic gases in one absorption column, thereby eliminating the need for a separate FGD unit which could potentially save millions of dollars. Previous research at CSIRO’s post-combustion capture pilot plant at Loy Yang power station has shown that mono-ethanolamine (MEA) solvent absorbs both CO2 and SO2, resulting in a spent amine absorbent rich in sulfates. Further development of the CS-Cap concept requires a deeper understanding of the properties of the sulfate-rich absorbent and the conditions under which it can be effectively regenerated. In the present study, thermal reclamation and reactive crystallisation processes were investigated, allowing the parameters affecting the regeneration of sulfate-loaded amine to be identified. It was found that amine losses were considerably higher in thermal reclamation than in reactive precipitation. During thermal reclamation, vacuum conditions were more effective than atmospheric, and pH of the initial solution played a significant role in recovery of MEA from the sulfate-rich absorbent. Reactive crystallisation could be effectively accomplished with the addition of KOH. An advantage of this process was that high purity K2SO4 crystals (~99%) were formed, despite the presence of degradation products in the solvent.
- Authors: Garg, Bharti , Pearson, Pauline , Cousins, Ashleigh , Verheyen, Vincent , Puxty, Graeme , Feron, Paul
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 14th Greenhouse Gas Control Technologies Conference (GHGT-14); Melbourne, Australia; 21st-26th October 2018 p. 1-8
- Full Text:
- Reviewed:
- Description: Sulfur dioxide (SO2) is a major flue gas contaminant that has a direct effect on the performance of amine-based carbon dioxide capture units operating on power plant flue gases. In many countries, flue gas desulfurisation (FGD) is an essential upstream requirement to CO2 capture systems, thereby increasing the overall operational and capital cost of the capture system. In Australia, the efficacy of CO2 capture may be compromised by the accumulation of SO2 in the absorption solvent. CSIRO’s CS-Cap process is designed to capture of both these acidic gases in one absorption column, thereby eliminating the need for a separate FGD unit which could potentially save millions of dollars. Previous research at CSIRO’s post-combustion capture pilot plant at Loy Yang power station has shown that mono-ethanolamine (MEA) solvent absorbs both CO2 and SO2, resulting in a spent amine absorbent rich in sulfates. Further development of the CS-Cap concept requires a deeper understanding of the properties of the sulfate-rich absorbent and the conditions under which it can be effectively regenerated. In the present study, thermal reclamation and reactive crystallisation processes were investigated, allowing the parameters affecting the regeneration of sulfate-loaded amine to be identified. It was found that amine losses were considerably higher in thermal reclamation than in reactive precipitation. During thermal reclamation, vacuum conditions were more effective than atmospheric, and pH of the initial solution played a significant role in recovery of MEA from the sulfate-rich absorbent. Reactive crystallisation could be effectively accomplished with the addition of KOH. An advantage of this process was that high purity K2SO4 crystals (~99%) were formed, despite the presence of degradation products in the solvent.
Renewable methane storage in Gippsland for peak and backup power
- Ghayur, Adeel, Verheyen, Vincent
- Authors: Ghayur, Adeel , Verheyen, Vincent
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 Australasian Universities Power Engineering Conference, AUPEC; Melbourne, Australia; 19th-22nd November 2017. p. 1-5
- Full Text: false
- Reviewed:
- Description: Climate Change mitigation by adopting renewable energies and the depleting gas reservoirs of Australia’s Gippsland Basin have introduced insecurity in the Australian energy sector. Urgent measures are needed to avoid future grid failures. This study proposes underground storage of biomethane (CH4) to meet peak and backup power demands. The depleted gas reservoirs and coal seams of Gippsland are candidates for such a storage. In this study, a facility converting waste biomass into methane and storing it in depleted gas reservoir for meeting peak/backup electricity demand is modelled and simulated. In the model, 200 t/d of biomass is anaerobically digested into methane. Despite this practicable yet relatively small scale when combined with storage, the facility generates 14,000 t (20 million m3) of methane per year, enough to generate over 80,000 MWh of electricity on demand via fuel cells. These results demonstrate the potential for bio-renewables contributing to large scale power demand.
Assessing transformer oil quality using deep convolutional networks
- Alam, Mohammad, Karmakar, Gour, Islam, Syed, Kamruzzaman, Joarder, Chetty, Madhu, Lim, Suryani, Appuhamillage, Gayan, Chattopadhyay, Gopi, Wilcox, Steve, Verheyen, Vincent
- Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
- Full Text:
- Reviewed:
- Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
- Description: E1
- Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
- Full Text:
- Reviewed:
- Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
- Description: E1
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