This paper proposes a method, namely MDKS (Kennard-Stone algorithm based on Mahalanobis distance), to divide the data into training and testing subsets for developing artificial neural network (ANN) models. This method is a modified version of the Kennard-Stone (KS) algorithm. With this method, better data splitting, in terms of data representation and enhanced performance of developed ANN models, can be achieved. Compared with standard KS algorithm and another improved KS algorithm (data division based on joint x - y distances (SPXY) method), the proposed method has also shown a better performance. Therefore, the proposed technique can be used as an advantageous alternative to other existing methods of data splitting for developing ANN models. Care should be taken when dealing with large amount of dataset since they may increase the computational load for MDKS due to its variance-covariance matrix calculations.
Microwave technology has emerged as one of the useful methods for nanoparticle synthesis. Despite many studies on this area, its underlying mechanism has not been clearly understood. In this study, in-situ observation of nanoparticle growth was carried out and the profiles and behaviors of produced bubbles and particles under microwave effect were reported. Sizes of bubble and nanoparticle particle were measured during and after microwave irradiation using a DLS apparatus and behavior of particle growth and superheating effect were observed. From the experimental data, it is apparent that the maximum bubble sizes were greatly influenced by the irradiation power and solute concentration. Particle number density, which is related to the initial solute concentration, is also an important factor for the bubble size produced during the irradiation. Finally, through in-situ observation of superheating effect, the behavior was frequently caused by the irradiation at higher power. To prevent superheating effect, influencing factors such as the irradiation power and number density should be controlled to ensure a stable operation of particle formation process.
Biodiesel is one of the most promising method to replace the fossil fuels because it is more environmentally friendly. Nevertheless, biodiesel manufacturing costs are much higher compared to conventional fossil fuels. Thus, the biodiesel should be synthesizing from reusable wastes to minimize the production cost. Homogeneous catalyst is the most common catalyst employed in the commercial biodiesel field. However, there are some drawbacks in using homogeneous catalyst in the reaction such as the difficulties faced in separation process of the homogeneous catalyst from the mixture of product. The presence of promising current technology has proved that the utilization of heterogeneous catalyst can assist in overcoming the existing problem of homogeneous catalytic reaction, especially in wastewater generation. The heterogeneous catalysts are more environmentally friendly, easier to separate and its reusability property. Despite its low production cost and its beneficial use as an eco-friendly waste recycle method, waste materials may possess qualities and characteristics that differ from the conventional homogeneous catalyst prior to biodiesel production. This review paper focused in the recent discovery of the heterogeneous catalyst synthesized from natural bio-waste materials, especially CaO-based such as eggshells, seashells and bones for biodiesel production. Apart from that, gypsum, part of the construction waste is proposed as the newly found heterogeneous catalyst. Gypsum exists abundantly due to the rapid development of the economics where construction and demolition activities are happening daily. The utilization of these construction waste-based catalysts may able to provide a sustainable route for biodiesel production. This review will enhance the development and existing scientific data in the area of biodiesel production and the synthesis of CaObased catalyst especially the synthesis of CaO-based catalysts from construction material.
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