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The goal of this review is to examine the current state of the art in nucleate pool boiling heat transfer in a variety of different fluids. The review is divided into many sections that discuss heat transfer in pool boiling, such as pool boiling of nanofluids, boiling behavior of water–glycerin combinations, and operational parameters. With the appropriate mixes of hydrocarbons and other commercial liquids, higher heat transfer coefficients may be produced. Coatings of nanoparticles with varying layer thicknesses applied to the heater surface may be optimized to improve heat transfer from the pool to the surrounding water. The heat transfer hypothesis elucidates the peculiarities of each pool's boiling regime. It is also possible to expand it to flow boiling by combining pool boiling liquid motion with external mechanical force. Other phase transitions, such as condensation, solidification, and melting, can also be described using boiling heat flow processes. Pool boiling performance can be improved by making a variety of adjustments to the heating surfaces as well as by using pure liquids in the water. Improvements can be made to boiling parameters such as the heat flux, the critical heat flux, the heat transfer coefficient, bubble development and departure, and so forth. A nanoparticle addition to a pure liquid or a surface coating on a heating surface can improve heat transfer and boiling properties by increasing the surface area of the liquid. Pool boiling critical heat flux was enhanced with Al2O3-water nano fluid. Authors used three different powder sizes of Al2O3 which were 0.05, 0.3 and 1.0 μm. Addition of alumina particle in water increases the boiling heat flux. Critical heat flux (CHF) was significantly enhanced using Titania and Alumina nano particles in water as compared to pure water. Average size of nano particle used was 85 nm measured by scattering electron microscope. Enhancement in Critical heat flux is due to nano particle coating on heating surface. Characteristics of nucleate boiling are greatly affected by the operating pressure. Miniature flat heat pipe (MFHP) with evaporator having micro grooved heat transfer surface gives 50% increment in critical heat flux at atmosphere pressure whereas this value increases up to 150% at 7.4 kPa pressure. The addition of CNT (carbon nanotube) to the base liquid increases the critical heat flux. Transmission electron microscopy confirms the average size of a nanoparticle as 15 nm. Authors found that by decreasing pressure from atmosphere condition critical heat flux increases to 200% with CNT/water nano fluid as compared to deionized water. SiC-water nanofluids of 100 nm size were experimented with at volume concentrations of 0.001%, 0.001%, and 0.01%. The size of the nanoparticle was confirmed by a scattering electron microscope. Authors concluded that at 0.01% of nano particle enhances critical heat flux to 105%. [ABSTRACT FROM AUTHOR]

Repeated boiler tube leak trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. In this study two artificial intelligent monitoring systems specialized in boiler tube leak trips have been proposed. The first intelligent warning system (IWS-1) represents the use of pure artificial neural network system whereas the second intelligent warning system (IWS-2) represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. The Extreme Learning Machine (ELM) methodology was also adopted in IWS-1 and compared with traditional training algorithms. Genetic algorithm (GA) was adopted in IWS-2 to optimize the ANN topology and the boiler parameters. An integrated data preparation framework was established for 3 real cases of boiler tube leak trip based on a thermal power plant in Malaysia. Both the IWSs were developed using MATLAB coding for training and validation. The hybrid IWS-2 performed better than IWS-1.The developed system was validated to be able to predict trips before the plant monitoring system. The proposed artificial intelligent system could be adopted as a reliable monitoring system of the thermal power plant boilers.

Transformer failures have a significant cost impact on the operation of an electrical network. In many utilities, transformers have been operating for many years past their expected usable life. As power demand has surged, transformers in some areas are being loaded beyond their rated capacity to meet the demand. One of the vital components in a transformer is the on-load tap changer (OLTC), which regulates the voltage in the distribution network. This study aims to review several condition-monitoring techniques (online and offline) that can monitor the health of the OLTC and assure the safety of the transformer's OLTC from irreparable damage by detecting the defect at an earlier stage, which is preceded by the specification of typical faults. This paper also discussed the common faults of the OLTC and the root causes of these faults. The OLTC is prone to mechanical faults due to its frequently changing mechanism in the tap operation. The OLTC are also prone to oil as well as thermal faults. As a result, it is critical to monitor OLTC conditions while they are in use. Proper management of condition monitoring (CM) for the OLTC is useful and necessary to increase availability and achieve optimised operating. Condition monitoring (CM) and diagnostics methods (DM) have been developing since the 1950s. CM and DM have been implemented to diagnose and detect an incipient fault, especially for the OLTC. Many techniques, online and offline, are being used to monitor the condition of the OLTC to prevent failure and minimize outages. These DM and CM will prolong the operational cycle and avoid a major disaster for the OLTC, which is an unfavorable scenario. [ABSTRACT FROM AUTHOR]

Tube leakage in boilers has been a major contribution to trips which eventually leads to power plant shut downs. Training of network and developing artificial neural network (ANN) models are essential in fault detection in critically large systems. This research focusses on the ANN modelling through training and validation of real data acquired from a sub-critical boiler unit. The artificial neural network (ANN) was used to develop a compatible model and to evaluate the working properties and behaviour of boiler. The training and validation of real data has been applied using the feed-forward with back-propagation (BP). The right combination of number of neurons, number of hidden layers, training algorithms and training functions was run to achieve the best ANN model with lowest error. The ANN was trained and validated using real site data acquired from a coal fired power plant in Malaysia. The results showed that the Neural Network (NN) with one hidden layers performed better than two hidden layer using feed-forward back-propagation network. The outcome from this study give us the best ANN model which eventually allows for early detection of boiler tube leakages, and forecast of a trip before the real shutdown. This will eventually reduce shutdowns in power plants.

The implementation of renewable energy brings numerous advantages including reduction of power transmission cost and minimization of the global warming problems. The investigation of the influencing operational parameters as well as optimization of the solar energy system is the key factors to enhance the power conversion efficiency. The different optimization methods in solar energy applications have been utilized to improve performance efficiency. However, the development of optimal methods under the intermittent nature of solar energy resources remains key issues to be explored. Therefore, this paper presents a comprehensive review of the main generic objectives of optimization in renewable energy systems, such as solar energy systems. Moreover, this study introduces the most recent intelligent optimization methods used in solar energy systems with regard to its functions, constraints, research gaps and contributions. From this review, it can be concluded that the main objectives of optimizations methods are to reduce minimize investment, operation and maintenance costs and emissions to enhance the system reliability. This review also outlines a brief discussion of various challenges and issues of solar energy optimization. Finally, the review delivers some effective future directions toward the development of an efficient and stable solar PV system. • A comprehensive review on the optimization objectives in solar energy systems are explained. • Intelligent control strategies and optimization methods are utilized in solar energy systems. • Optimizations strategies reduce emissions and costs of system into maximizing reliability. • Solar energy systems enhance the output power and minimize the interruptions in the connected load. • This review highlights the challenges on optimization to increase efficient and stable PV system. [ABSTRACT FROM AUTHOR]