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The importance of energy demands that have increased exponentially over the past century has led to the sourcing of other ideal power solutions as the potential replacement alternative to the conventional fossil fuel. However, the utilisation of fossil fuel has created severe environmental issues. The identification of other renewable sources is beneficial to replace the energy utilisation globally. Biomass is a highly favourable sustainable alternative to renewable resources that can produce cleaner, cheaper, and readily available energy sources in the future. The palm oil industry is essentially ideal for the availability of abundant biomass resources, where the multifaceted residues are vital for energy production through the conversion of biomass waste into value-added products simultaneously. This article discusses the utilisation of palm oil and its residues in the energy and transportation sector. Assessment and evaluation on the feasibility of palm oil and its residues were made on the current valorisation methods such as thermochemical and biochemical techniques. Their potential as transportation fuels were concurrently reviewed. This is followed by a discussion on future challenges of palm oil industries that will take place globally, including the prospects from government and nongovernment organisations for the development of palm oil as a sustainable alternative replacement to fossil fuel. Hence, this review aims to provide further insight into the possibilities of palm oil and its residues towards sustainable development with reduced environmental-related issues.

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In the study, Al7075-TiC composites were synthesized by using a novel dual step blending process followed by cold pressing and sintering. The effect of ball milling time on the microstructure of the synthesized composite powder was characterized using X-ray diffraction measurements (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and transmission electron microscopy (TEM). Subsequently, the integrated effects of the two-stage mechanical alloying process were investigated on the crystallite size and lattice strain. The crystallite size and lattice strain of blended samples were calculated using the Scherrer method. The prediction of the crystallite size and lattice strain of synthesized composite powders was conducted by an artificial neural network technique. The results of the mixed powder revealed that the particle size and crystallite size improved with increasing milling time. The particle size of the 3 h-milled composites was 463 nm, and it reduces to 225 nm after 7 h of milling time. The microhardness of the produced composites was significantly improved with milling time. Furthermore, an artificial neuron network (ANN) model was developed to predict the crystallite size and lattice strain of the synthesized composites. The ANN model provides an accurate model for the prediction of lattice parameters of the composites.

The maximum power point (MPP) of a solar photovoltaic (PV) module fluctuates over the whole day with the variation of weather conditions. In order to track it precisely and harvest it efficiently, an appropriate value of load has to be matched. A prerequisite to successful load matching is the knowledge of available real-time maximum power. For real-time maximum power transfer and robust load matching, a control strategy that combines the use of a dc-dc boost converter, fuzzy logic control, and a modified queen bee genetic algorithm has been proposed. Simulation results show the promise of the approach. It is believed that this research will lead to improvement in the efficiency of PV modules considering real-time weather conditions and intelligent control for robust load matching.