• Energy Research
• engineering and technology close
• QA close
• BH close

Abstract By applying the principles of the second law of thermodynamics and utilizing the HSC Chemistry software, the thermodynamic equilibrium and efficiency analysis of the CaSO4 CaO water splitting cycle was performed in this investigation. The temperatures desirable and the equilibrium compositions allied with the thermal reduction of CaSO4 and the re-oxidation of CaO via water splitting reaction were estimated. The obtained results indicate that the thermal reduction temperature (TH) required to completely decompose the CaSO4 was decerased from 2220 to 1890 K due to the rise in the molar flow rate of ( n ˙ A r ) from 1 to 50 mol/s. In addition, the consequence of the TH, n ˙ A r , and the water splitting temperature ( T L ) on the process parameters such as total amount of solar energy needed, re-radiation losses, energy dissipated by the water splitting reactor and others associated with the CaSO4 CaO water splitting cycle was scrutinized. By utilizing higher n ˙ A r from 1 to 50 mol/s, the TH was decreased from 2200 to 1890 K. However, as the n ˙ A r was increased from 1 to 50 mol/s, the amount of heat energy needed to heat the Ar was also upsurged from 12.5 to 625.6 kW. This rise in the Q ˙ A r − h e a t i n g , directly reflected into an increase in the Q ˙ s o l a r − c y c l e from 1063.4 up to 2653.9 kW. The findings of this study further confirms that the maximum solar-to-fuel energy conversion efficiency ( η s o l a r − t o − f u e l ) equal to 27.4% was realized by conducting the CaSO4 CaO water splitting cycle at TH = 2220 K, n ˙ A r = 1 mol/s, and TL = 1100 K. By using 50% of the recuperable heat, the η s o l a r − t o − f u e l of the CaSO4 CaO water splitting cycle can be enhanced up to 36.2%.

Abstract By applying the principles of the second law of thermodynamics and utilizing the HSC Chemistry software, the thermodynamic equilibrium and efficiency analysis of the CaSO4 CaO water splitting cycle was performed in this investigation. The temperatures desirable and the equilibrium compositions allied with the thermal reduction of CaSO4 and the re-oxidation of CaO via water splitting reaction were estimated. The obtained results indicate that the thermal reduction temperature (TH) required to completely decompose the CaSO4 was decerased from 2220 to 1890 K due to the rise in the molar flow rate of ( n ˙ A r ) from 1 to 50 mol/s. In addition, the consequence of the TH, n ˙ A r , and the water splitting temperature ( T L ) on the process parameters such as total amount of solar energy needed, re-radiation losses, energy dissipated by the water splitting reactor and others associated with the CaSO4 CaO water splitting cycle was scrutinized. By utilizing higher n ˙ A r from 1 to 50 mol/s, the TH was decreased from 2200 to 1890 K. However, as the n ˙ A r was increased from 1 to 50 mol/s, the amount of heat energy needed to heat the Ar was also upsurged from 12.5 to 625.6 kW. This rise in the Q ˙ A r − h e a t i n g , directly reflected into an increase in the Q ˙ s o l a r − c y c l e from 1063.4 up to 2653.9 kW. The findings of this study further confirms that the maximum solar-to-fuel energy conversion efficiency ( η s o l a r − t o − f u e l ) equal to 27.4% was realized by conducting the CaSO4 CaO water splitting cycle at TH = 2220 K, n ˙ A r = 1 mol/s, and TL = 1100 K. By using 50% of the recuperable heat, the η s o l a r − t o − f u e l of the CaSO4 CaO water splitting cycle can be enhanced up to 36.2%.

Abstract The Cold Lake development, located in Alberta, Canada, is the world’s largest heavy oil in situ thermal development. At Cold Lake, operated by Imperial Oil Resources, an ExxonMobil affiliate, the Cyclic Steam Stimulation (CSS) process is used to produce 23,500 m3/d (150 kB/d) of heavy oil. In 2009, Cold Lake produced its one billionth barrel (160 million m3) of heavy oil. The Nabiye project will be the fifth central steam generation and fluid processing hub added at Cold Lake. Nabiye (Dené for Otter) continues the historical Cold Lake development concept of maximizing value through the utilization of a phased development strategy. Relative to current operations, the key reservoir difference at Nabiye is reduced pay thickness. Averaging 12 meters (40 feet), Nabiye pay is about half as thick as the initial pads of the previous expansion (Mahkeses). While reservoir of similar thickness as Nabiye is currently being developed as Productivity Maintenance pads to sustain production in the existing operation, the risk profile for Nabiye is higher because new plant investment is required. As Cold Lake develops more challenging subsurface environments, more advanced reservoir engineering techniques must be employed to mitigate risk. This paper describes the extensive use of both thermal simulation and wellbore integrity modeling to complement analog performance prediction techniques. This paper will demonstrate how the Nabiye project is effectively commercializing an unconventional resource by integrating analog performance data and advanced reservoir and geomechanical modeling. The application of (1) thermal simulation for performance prediction and (2) geomechanical modeling for steam strategy optimization will be presented.

Abstract The Cold Lake development, located in Alberta, Canada, is the world’s largest heavy oil in situ thermal development. At Cold Lake, operated by Imperial Oil Resources, an ExxonMobil affiliate, the Cyclic Steam Stimulation (CSS) process is used to produce 23,500 m3/d (150 kB/d) of heavy oil. In 2009, Cold Lake produced its one billionth barrel (160 million m3) of heavy oil. The Nabiye project will be the fifth central steam generation and fluid processing hub added at Cold Lake. Nabiye (Dené for Otter) continues the historical Cold Lake development concept of maximizing value through the utilization of a phased development strategy. Relative to current operations, the key reservoir difference at Nabiye is reduced pay thickness. Averaging 12 meters (40 feet), Nabiye pay is about half as thick as the initial pads of the previous expansion (Mahkeses). While reservoir of similar thickness as Nabiye is currently being developed as Productivity Maintenance pads to sustain production in the existing operation, the risk profile for Nabiye is higher because new plant investment is required. As Cold Lake develops more challenging subsurface environments, more advanced reservoir engineering techniques must be employed to mitigate risk. This paper describes the extensive use of both thermal simulation and wellbore integrity modeling to complement analog performance prediction techniques. This paper will demonstrate how the Nabiye project is effectively commercializing an unconventional resource by integrating analog performance data and advanced reservoir and geomechanical modeling. The application of (1) thermal simulation for performance prediction and (2) geomechanical modeling for steam strategy optimization will be presented.

Abstract The thermal performance of the thermosyphon water heater unit was analyzed to show its applicability in Bahrain, using data of several sunny, cloudy and hazy days in winter. The performance of this unit was studied under various maximum daily solar intensities, ranging from 1, 2 and 3 on a cloudy day, upto 695 W/m 2 on a sunny day, with the daily outside temperature ranges between 25–19°C. The results show that the system has an average efficiency of 38% with storage tank temperature above 50°C. These results show that this system is quite suitable for application in Bahrain weather conditions.

Abstract The thermal performance of the thermosyphon water heater unit was analyzed to show its applicability in Bahrain, using data of several sunny, cloudy and hazy days in winter. The performance of this unit was studied under various maximum daily solar intensities, ranging from 1, 2 and 3 on a cloudy day, upto 695 W/m 2 on a sunny day, with the daily outside temperature ranges between 25–19°C. The results show that the system has an average efficiency of 38% with storage tank temperature above 50°C. These results show that this system is quite suitable for application in Bahrain weather conditions.

The random forest (RF) classifier, which is a combination of tree predictors, is one of the most powerful classification algorithms that has been recently applied for fault detection and diagnosis (FDD) of industrial processes. However, RF is still suffering from some limitations such as the noncorrelation between variables. These limitations are due to the direct use of variables measured at nodes and therefore the only use of static information from the process data. Thus, this article proposes two enhanced RF classifiers, namely the Euclidean distance based reduced kernel RF (RK-RF $_{\text{ED}}$ ) and K-means clustering based reduced kernel RF (RK-RF $_{\text{Kmeans}}$ ), for FDD. Based on the kernel principal component analysis, the proposed classifiers consist of two main stages: feature extraction and selection, and fault classification. In the first stage, the number of observations in the training data set is reduced using two methods: the first method consists of using the Euclidean distance as dissimilarity metric so that only one measurement is kept in case of redundancy between samples. The second method aims at reducing the amount of the training data based on the K-means clustering technique. Once the characteristics of the process are extracted, the most sensitive features are selected. During the second phase, the selected features are fed to an RF classifier. An emulated grid-connected PV system is used to validate the performance of the proposed RK-RF $_{\text{ED}}$ and RK-RF $_{\text{Kmeans}}$ classifiers. The presented results confirm the high classification accuracy of the developed techniques with low computation time.

The random forest (RF) classifier, which is a combination of tree predictors, is one of the most powerful classification algorithms that has been recently applied for fault detection and diagnosis (FDD) of industrial processes. However, RF is still suffering from some limitations such as the noncorrelation between variables. These limitations are due to the direct use of variables measured at nodes and therefore the only use of static information from the process data. Thus, this article proposes two enhanced RF classifiers, namely the Euclidean distance based reduced kernel RF (RK-RF $_{\text{ED}}$ ) and K-means clustering based reduced kernel RF (RK-RF $_{\text{Kmeans}}$ ), for FDD. Based on the kernel principal component analysis, the proposed classifiers consist of two main stages: feature extraction and selection, and fault classification. In the first stage, the number of observations in the training data set is reduced using two methods: the first method consists of using the Euclidean distance as dissimilarity metric so that only one measurement is kept in case of redundancy between samples. The second method aims at reducing the amount of the training data based on the K-means clustering technique. Once the characteristics of the process are extracted, the most sensitive features are selected. During the second phase, the selected features are fed to an RF classifier. An emulated grid-connected PV system is used to validate the performance of the proposed RK-RF $_{\text{ED}}$ and RK-RF $_{\text{Kmeans}}$ classifiers. The presented results confirm the high classification accuracy of the developed techniques with low computation time.