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This study presents the empirical exploration of food waste recovery throughout the electrostatic separation process. In addition, the paper discusses the potential of artificial neural network (ANN) in predicting the responses. A five-level three-factor Taguchi orthogonal array (OA) design of experiment was employed as an initiative to optimize the prediction process. The electrostatic separation process was modelled using ANN by considering the recovered food waste and misclassified middling product during separation. A multi-layer feed-forward network developed in MATLAB was constructed. It was found that the results from the experiment and predicted model were in very good agreement. To our best knowledge, this is the first report for prediction of food waste separation performance employing ANN and Taguchi design.

We present an analysis on the performance of two popular dual offset antennas design, i.e. the offset Cassegrain and Gregorian reflector antennas. In our study, we have adopted the design parameters for the Cassegrain configuration used in the Atacama Large Milimeter Array (ALMA) project. Modifications on the original parameters are made so as to meet the design requirement of the offset configurations. To reduce spillover loss, we have adjusted the angle between the axis of the primary reflector and that of the sub-reflector to 0.20 o. The results obtained from the physical optics computation show that the amplitude at the main lobe of the Gregorian configuration is approximately 74.02 dB, while that of the Cassegrain configuration is approximately 74 dB. The maximum (relative) side lobe level, SLL dB for the Cassegrain and Gregorian configurations are found as-3.67 dB and-3.69 dB respectively. Although the magnitude of the main lobe for both configurations is comparable, the Gregorian antenna gives relatively lower SLL dB. In other words, the Gregorian configuration performs relatively better than its Cassegrainian counterpart.

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