Eric Hu

Thermodynamic advantages of using solar energy in the regenerative Rankine power plant

Abstract Use of solar energy is of great importance in reducing greenhouse gases emission. Although there are some advances in solo solar power systems, the efficiencies and costs of these systems are not so attractive. This paper presents a new approach for solar power utilization, i.e. using solar heat to replace the extracted steam to heat the feedwater in the regenerative Rankine plant. The exergy merit index EMI (the ratio of the work generated by the saved steam to the exergy supplied by the solar heat) of such solar-aided systems can be greater than 100% while the maximum exergy efficiencies of the solo solar power systems can never reach 100%. The analysis of a three-stage regenerative Rankine plant shows that, by using solar energy, the work generated can be increased up to 30% while the EMI reaches 101.28%. Therefore low-grade heat and other waste heat can be utilized as a valuable source of work in this way. The “aided” concept has special meaning for solar power utilization. In summer, both the solar radiation and the electrical load demand reach their peaks. The increased solar radiation can generate increased power to meet the increased power demand. In addition, the solar aided system can also eliminate the variability in power output in solo solar power systems.

Random forest based lung nodule classification aided by clustering

An automated lung nodule detection system can help spot lung abnormalities in CT lung images. Lung nodule detection can be achieved using template-based, segmentation-based, and classification-based methods. The existing systems that include a classification component in their structures have demonstrated better performances than their counterparts. Ensemble learners combine decisions of multiple classifiers to form an integrated output. To improve the performance of automated lung nodule detection, an ensemble classification aided by clustering (CAC) method is proposed. The method takes advantage of the random forest algorithm and offers a structure for a hybrid random forest based lung nodule classification aided by clustering. Several experiments are carried out involving the proposed method as well as two other existing methods. The parameters of the classifiers are varied to identify the best performing classifiers. The experiments are conducted using lung scans of 32 patients including 5721 images within which nodule locations are marked by expert radiologists. Overall, the best sensitivity of 98.33% and specificity of 97.11% have been recorded for proposed system. Also, a high receiver operating characteristic (ROC) A(z) of 0.9786 has been achieved.

Photosynthesis of hydrogen and methane as key components for clean energy system

Abstract While researchers are trying to solve the world’s energy woes, hydrogen is becoming the key component in sustainable energy systems. Hydrogen could be produced through photocatalytic water-splitting technology. It has also been found that hydrogen and methane could be produced through photocatalytic reduction of carbon dioxide with water. In this exploratory study, instead of coating catalysts on a substrate, pellet form of catalyst, which has better adsorption capacity, was used in the photo-reduction of carbon dioxide with water. In the experiment, some water was first absorbed into titanium dioxide pellets. Highly purified carbon dioxide gas was then discharged into a reactor containing these wet pellets, which were then illuminated continuously using UVC lamps. Gaseous samples accumulated in the reactor were extracted at different intervals to analyze the product yields. The results confirmed that methane and hydrogen were photosynthesized using pellet form of TiO2 catalysts. Hydrogen was formed at a rate as high as 0.16 micromoles per hourμmol h–1). The maximum formation rate of CH4 was achieved at 0.25 μmol h–1 after 24 h of irradiation. CO was also detected.

A medium-temperature solar thermal power system and its efficiency optimisation

This paper firstly expounds that the reheat-regenerative Rankine power cycle is a suitable cycle for the parabolic trough collector, a popular kind of collector in the power industry. In a thermal power cycle, the higher the temperature at which heat is supplied, the higher the efficiency of the cycle. On the other hand, for a given kind of collector at the same exiting temperature, the higher the temperature of the fluid entering the collector, the lower the efficiency of the collector. With the same exiting temperature of the solar field and the same temperature differences at the hottest end of the superheater/reheater and at the pinch points in the heat exchangers (e.g., the boiler) in the cycle, the efficiencies of the system are subject to the temperature of the fluid entering the collector or the saturation temperature at the boiler. This paper also investigates the optimal thermal and exergetic efficiencies for the combined system of the power cycle and collector. To make most advantage of the collector, the exiting fluid is supposed to be at the maximum temperature the collector can harvest. Hence, the thermal and exergetic efficiencies of the system are related to the saturation temperature at the boiler here.

Automated detection of lung nodules in computed tomography images: a review

Lung nodules refer to a range of lung abnormalities the detection of which can facilitate early treatment for lung patients. Lung nodules can be detected by radiologists through examining lung images. Automated detection systems that locate nodules of various sizes within lung images can assist radiologists in their decision making. This paper presents a study of the existing methods on automated lung nodule detection. It introduces a generic structure for lung nodule detection that can be used to represent and describe the existing methods. The structure consists of a number of components including: acquisition, pre-processing, lung segmentation, nodule detection, and false positives reduction. The paper describes the algorithms used to realise each component in different systems. It also provides a comparison of the performance of the existing approaches.

Reducing energy consumption of vehicle air conditioning system by an energy management system

This paper presents an energy management system to reduce the energy consumption of a vehicle when its air conditioning system is in use. The system controls the mass flow rate of the air by dynamically adjusting the blower speed and air-gates opening under various heat and loads circumstances. Simulations were conducted for a travelling vehicle operating the air conditioning system without and with the developed energy management system. The results show that the comfort temperature within the cabin room is achieved for reduced amount of energy consumption.

An intelligent energy management model for a parallel hybrid vehicle under combined loads

To exploit the benefits offered by parallel HEVs, an intelligent energy management model is developed and evaluated in this paper. Despite most existing works, the developed model incorporates combined wind/drag, slope, rolling, and accessories loads to minimise the fuel consumption under varying driving conditions. A slope prediction unit is also employed. The engine and the electric motor can output power simultaneously under a heavy-load or a slopped road condition. Two simulation were conducted namely slopped-windy-prediction and slopped-windy-prediction-hybrid. The results indicate that the vehicle speed and acceleration is smoother where the hybrid component was included. The average fuel consumption for the first and second simulations were 7.94 and 7.46 liter/100 km, respectively.

Multi-zone temperature prediction in a commercial building using artificial neural network model

Predicting temperature in buildings equiped with Heating, ventilation and air-conditioning (HVAC) systems is a crucial step to take when implementing a model predictive control (MPC). This prediction is also challenging because the buildings themselves are nonlinear, have many uncertainties and strongly coupled. Artificial neural networks (ANNs) have been used in previous studies to solve such a modeling problem. Unlike most of the studies that have only considered small-scale, single zone modeling task, this paper presents a novel ANN modeling method for the modeling inside a real world multi-zone building. By comparing ANN models with different input variables, it was found that the prediction accuracies can be greatly improved when the thermal interactions were considered. The proposed models were used to perform both single-zone and multi-zone temperature prediction and achieved very good accuracies.

Exergetic and Parametric Study of a Solar Aided Coal-Fired Power Plant

A solar-aided coal-fired power plant realizes the integration of a fossil fuel (coal or gas) and clean energy (solar). In this paper, a conventional 600 MW coal-fired power plant and a 600 MW solar-aided coal-fired power plant have been taken as the study case to understand the merits of solar-aided power generation (SAPG) technology. The plants in the case study have been analyzed by using the First and Second Laws of Thermodynamics principles. The solar irradiation and load ratio have been considered in the analysis. We conclude that if the solar irradiation was 925 W/m 2 and load ratio of the SAPG plant was 100%, the exergy efficiency would be 44.54% and the energy efficiency of the plant (46.35%). It was found that in the SAPG plant the largest exergy loss was from the boiler, which accounted for about 76.74% of the total loss. When the load ratio of the unit remains at 100%, and the solar irradiation varies from 500 W/m 2 to 1,100 W/m 2 , the coal savings would be in the range of 8.6 g/kWh to 15.8 g/kWh. If the solar irradiation were kept at 925 W/m 2 while the load ratio of the plant changed from 30% to 100%, the coal savings could be in the range of 11.99 g/kWh to 13.75 g/kWh.

Photocatalytic Production of Methane and Hydrogen Through Reduction of Carbon Dioxide with Water Using Titania Pellets

This paper presents an experimental study on employing a pellet form of catalyst in photo-reduction of carbon dioxide with water. Water was first absorbed into titania pellets. Highly purified carbon dioxide gas was then discharged into a reactor containing the wet pellets, which were then illuminated continuously for 65 hours using UVC lamps. Analysing the products accumulated in the reactor confirmed that methane and hydrogen were produced through photo-reduction of carbon dioxide with water. No other hydrocarbons were detected. Increasing the temperature in the reactor has showed little change on the amount of methane produced.