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The current study presents an artificial neural network model used to predict the boiling heat transfer coefficient of different coating thicknesses of a graphene-coated copper surface in the pool boiling experimental setup for deionized water. The surface characterization has been carried out to study the structure, morphology and surface behavior. The investigations are carried out to evaluate the boiling heat transfer coefficient, heat flux and wall superheat for various thicknesses of nano-coated surfaces experimentally, and the obtained results are compared with those of the reported studies and existing empirical correlations. After that, these results are compared with the outputs such as current, heat flux, wall superheat and boiling heat transfer coefficient obtained using a MATLAB-based artificial neural network model with coating thickness, surface roughness and voltage as input variables. The admirable accuracies are obtained with the predicted optimal model outputs with experimental observation in each test case. [ABSTRACT FROM AUTHOR]

The effective use of marine biomass has recently been identified as a feasible method of renewable energy production. Therefore, to facilitate the effective use of algae biomass, it is essential to develop techniques for the mass production of algal cultures. It is also important to develop artificial culture techniques that are not affected by natural phenomena such as weather. This study utilized a newly developed culture technology that uses seawater with high concentrations of dissolved CO2(CO2seawater). The experiments in this study were conducted to test the effects of CO2seawater on algal growth. In addition to experimental conditions that were previously investigated in industrial algae farming scenarios, it is also necessary to consider the effects of new parameters associated with increased CO2concentrations. The following four conditions were experimentally investigated: (1) the tolerance of algae to changes in pH, (2) the effect of CO2seawater on long-term culture growth, (3) the effect of continuous culture experiments, and (4) the effect of water flow rate on cultured algae. The results in terms of having excelled especially indicate that a 1-month long-term algae culture period in seawater with CO2concentration of 1.0% led to growth that was approximately 4.5 times faster than growth in seawater without the CO2additive. Furthermore, the results also provided useful information regarding the proper flow rate needed to enhance algal growth. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a detailed parametric study on a dual-mode silica gel–water adsorption chiller is performed. This advanced adsorption chiller utilizes effectively low-temperature solar or waste heat sources of temperature between 40 and 95°C. Two operation modes are possible for the advanced chiller. The first operation mode will be to work as a highly efficient conventional chiller where the driving source temperature is between 60 and 95°C. The second operation mode will be to work as an advanced three-stage adsorption chiller where the available driving source temperature is very low (between 40 and 60°C). With this very low driving source temperature in combination with a coolant at 30°C, no other cycle except an advanced adsorption cycle with staged regeneration will be operational. In this paper, the effect of chilled-water inlet temperature, heat transfer fluid flow rates and adsorption–desorption cycle time effect on cooling capacity and COP of the dual-mode chiller is performed. Simulation results show that both cooling capacity and COP values increase with the increase of chilled water inlet temperature with driving source temperature at 50 and 80°C in three-stage mode, and single-stage multi-bed mode, respectively. However, the delivered chilled-water temperature increases with chilled-water inlet temperature in both modes. [Copyright &y& Elsevier]