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Nanofluids are stable suspensions of nano fibers and particles in fluids. Recent investigations show that thermal behavior of these fluids, such as improved thermal conductivity and convection coefficients are superior to those of pure fluid or fluid suspension containing larger size particles. The use of enhanced thermal properties of nanofluids in a loop heat pipe (LHP) for the cooling of computer microchips is the main aim of this study. Thus, the Fe2NiO4-H2O served as the working fluid with nanoparticle mass concentrations ranged from 0 to 3 % in LHP for the heat input range from 20W to 60W was employed. Experimental apparatus and procedures were designed and implemented for measurements of the surface temperature of LHP. Then, a commercial liquid cooling kit of LHP system similar as used in experimental study was installed in real desktop PC CPU cooling system. The test results of the proposed system indicate that the average decrease of 5.75oC (14%) was achieved in core temperatures of desktop PC CPU charged with Fe2NiO4-H2O as compared with pure water under the same operating conditions. The results from this study should find it’s used in many industrial processes in which the knowledge on the heat transfer behavior in nanofluids charged LHP is of uttermost importance.

With an ever increasing demand for energy, Malaysia has become a nation that thrives on solid power generation sector to meet the energy demand and supply market. In a coal fired power plant, soot blowing operation is commonly used as a cleaning mechanism inside the boiler. There are many types of sequence available for this soot blowing operation. Hence, there is no efficient ways in utilizing the soot blowing operation to enhance the efficiency of boiler. Soot blowing optimization requires specific set of data preparation and simulation in order to achieve the best modal. Computational Fluid Dynamics (CFD) is used to model a 700MW super-critical boiler, whereby parameters with effect to soot blowing operation is studied. Two different boiler condition is studied to analyze parameters in a clean and faulty boiler. Artificial Neural Network (ANN) is used to train neural network modal with back propagation method to determine the best modal that will be used to predict soot blowing operation. Combination of neural network different number of neurons, hidden layers, training algorithm, and training functions is trained to find the modal with lowest error. By improving soot blowing sequence, efficiency of boiler can be improved by providing best parameter and model. This model is then used as a reference for advisory tool whereby a Neural Network Predictive Tool is suggested to the station to predict the soot blowing operation that occurs.

Coal fired power plant becoming preferable power plant type to support electricity demand mainly in Asia due to stable coal price and low maintenance. However, most coal fired plant operator struggle with condition where coal undergo incomplete combustion and produced unburned carbon where can be found in ashes especially in fly ash. Higher percentage of unburned carbon in fly ash reflects the lower efficiency of furnace and contributes to financial loses for plant operators. This problem also leads to technical issues such as slagging and clinkering and further reduces the efficiency of furnace. The plant operator determines the amount of unburned carbon by using conventional method and this proves be a challenge to identify and rectify the problem on day basis due time constraint to obtain results of unburned carbon. Thus in this paper, best Artificial Neural Network model was derived to develop intelligent monitoring system to predict unburned carbon level on more daily basis. By this model, the power producer can predict the unburned carbon level by using data in power plant to predict the unburned carbon level in short period of time.

Water scarcity presents a pressing global challenge, particularly in regions facing rising demands amidst diminishing freshwater resources. This research investigates the efficiency and freshwater production of solar desalination systems, focusing on both passive and active distillation methods. While previous studies have highlighted advancements in solar still technologies, this review critically assesses their performance based on criteria such as productivity, efficiency, and water production costs. Key findings indicate that double-slope solar stills exhibit 50% improvements in daily water output compared to passive variants. Additionally, active solar stills incorporating the Parabolic Trough Collectors technology demonstrate the highest productivity, achieving 12.4 L/m2 per day. The integration of supplementary purification technologies, including optical upgrades and nanomaterials, further enhances the solar still thermal properties and efficiency. Remarkably, the Hybrid Solar Still achieves the lowest recorded water cost ($0.009 per L), underscoring the economic viability of solar desalination. Addressing scientific gaps, this study emphasizes the importance of concurrently maximizing freshwater production and efficiency in solar desalination designs, calling for integrated approaches that leverage concentrated solar energy for sustainable water provision. [ABSTRACT FROM AUTHOR]

The main aim of this study is to enhance the thermal performance of loop heat pipe (LHP) charged with nanofluid as the working fluid. Thus, experiments are conducted to investigate heat transfer characteristics of using diamond-H2O nanofluid with nanoparticle mass concentration ranged from 0% to 3% in a LHP as a working medium for heat input range from 20 W to 60 W. The three-dimensional model, laminar flow and heat transfer governing equations are solved using the finite volume method. The simulations are carried out with three-dimensional model based on the characterization of the working fluid inside the LHP to give an insight into the heat transfer and fluid flow mechanism. The LHP performance is evaluated in terms of temperature distributions and total thermal resistance of LHP. It is inferred that the temperatures obtained at all points in evaporator side of LHP charged with diamond-H2O nanofluid are lower and reach their steady state faster than LHP charged with pure water. At the constant heat input, test results showed the average decrease of 5.7%−10.8% at nanoparticle mass concentrations ranging from 0.5% to 3% in Rth of LHP as compared with pure water (0%). [ABSTRACT FROM AUTHOR]