Abstract Bauxite ore is a sedimentary rock which is known to be the primary ore for alumina (Al2O3) production and can be further refined as Aluminum (Al). This element is vital and widely used in transportation, packaging, electrical appliances and household products. The Bayer process is the most common process used for alumina extraction. However, producing a high grade of alumina is challenging due to the presence of impurities. This study investigates the effect of without (Method 1) and with pre-treatments (Method 2) prior to the Bayer process, to produce precipitated alumina trihydrate (ATH) from Malaysian bauxite. In Method 2, the raw bauxite (-45 μm) underwent pre-treatments including roasting at 500°C and a wet magnetic separation at 3.0 A. Whereas, the Bayer process in both methods was performed using 3.0 M NaOH with a liquid to solid ratio of 1:5, stirred at 400 rpm and heated at 90°C for 1 hour. The pregnant solution underwent precipitation by adding 6 g of Al2O3 seeds, stirred at 200 rpm at 70°C for 24 hours and left for 5 days. The raw bauxite of Felda Bukit Goh, Kuantan, Pahang, mainly consists of 48.02 wt. % Fe2O3, 31.85 wt. % Al2O3, 14.10 wt. % TiO2 and 4.92 wt. % SiO2. Gibbsite was the predominant mineral. Via AAS analysis, the Al2O3 grade detected was 35.2%. After the Bayer process, it was observed that the Al2O3 grades of the bauxite residues in methods 1 and 2 were 32.15 % and 28.20 %, respectively. This indicates that there was more dissolution of Al2O3 over pre-treatments. The Al2O3 grades measured from the precipitated ATH can be achieved up to 77.14% with 7.64% recovery, whereas without pre-treatments, 70.44% Al2O3 with 5.72% recovery.

Abstract Precise benthic habitat mapping is essential for the efficient management and preservation of tropical marine ecosystems. This study assesses the effectiveness of Unmanned Aerial Vehicle (UAV) technology for high-resolution mapping of the benthic habitats adjacent to Mantanani Besar Island, Malaysia. Employing a Support Vector Machine (SVM) algorithm, high-spatial-fidelity imagery (GSD = 3.72 cm/pixel) was classified into two hierarchical tiers: a binary Level 1 (L1) scheme (live coral and non-living substrate) and a multi-class Level 2 (L2) scheme consisting of six distinct classes (branching coral, massive coral, patch coral, sand, coral rubble, and submerged rock). The UAV-derived classification attained an Overall Accuracy (OA) of 88.60% (κ = 0.8625), illustrating the efficacy of this platform for detailed habitat description. The findings reveal that live coral occupies roughly 37.55 ha (30%) of the examined area, whilst non-coral substrates, mainly sand and rubble, constitute the remaining 88.86 ha (70%). The occurrence of fragmented benthic substrates, especially in settlement locales, indicates considerable past reef degradation possibly caused by sedimentation and human activities. This study highlights the use of UAVs as a precise instrument for marine surveillance, delivering essential spatial data for the conservation of Mantanani's biodiversity.

Abstract The amount of moisture content in drying process is crucial. Determining accurate process control, product quality and energy efficiency in food processing is necessary to ensure the quality of the drying food. In this study, Response Surface Methodology (RSM), a feedforward Neural Network (NN), and a Random Forest (RF) model were developed for moisture content of avocado pulp dried under controlled convective conditions. A Central Composite Design with number of experiments = 30 was used to explore four factors which are hot-air temperature (denoted as A, ranging from 40 to 70 °C), drying time (B, 13–20 h), wind speed (C, 0.12–0.26 m/s), and raw material thickness (D, 0.5–1.25 cm). A second-order polynomial model which was developed using RSM, was fitted to the experimental data and compared with other models, NN and RF using cross-validation to improve robustness. The results showed that RSM model performed significantly better than the other two models. The RSM model achieved coefficient of determination of 0.80 followed by NN, 0.43 and RF, 0.34. Feature-importance was used on ML models to determine the ranking of the factors based on how significant the factors influence the drying process. It was identified that wind speed and drying time as the main factors to represent the final moisture content, directly affect mass-transfer control in this application. The results highlight that small datasets can outperform the machine learning model especially for characterizing food drying processes.