This study examined the effects of calcium oxide (CaO) and natural zeolite (NZ) catalysts on the pyrolysis of palm kernel shell (PKS) to improve conversion efficiency and bio-oil quality. A 300 g sample of PKS was pyrolyzed in a stainless-steel reactor at 500 oC under four conditions: non-catalytic, CaO-catalyzed, NZ-catalyzed, and dual-catalyst (CaO‒NZ). The results showed that both CaO and NZ significantly affected the yields of bio-oil, non-condensable gas (NCG), and char. CaO enhanced cracking to produce lighter bio-oils (C2-C6), whereas NZ acted mainly on heavy intermediates; this upgrading produced both heavier condensed oxygenates and light fragments that remain non-condensable, leading to the highest NCG yield. The catalysts had little effect on bio-oil density but influenced viscosity, pH, and heating value. The dual catalyst produced bio-oil with improved properties: density 980 kg/m3, viscosity 1.63 cSt, pH 4.1, and heating value 30.76 MJ/kg. Mechanistically, CaO promoted cracking into lighter hydrocarbons (C2–C6), whereas NZ favored oligomerization, forming heavier molecules. In combination, CaO suppressed the oligomerization effect of NZ, giving a molecular distribution similar to CaO alone. Although the overall phenolic content changed only slightly, the catalysts significantly altered the ratio of simple to complex phenols. In addition, the dual catalyst facilitated the conversion of esters into ketones, further enhancing bio-oil composition.
Indonesia is known as a disaster-prone region. Located on the Pacific Ring of Fire, it is one of the countries most susceptible to various types of natural disasters. The province of Aceh is one of the regions in Indonesia that frequently experiences earthquakes due to its geographical location in a highly seismically active area. Therefore, an analysis of earthquake data clustering in the province of Aceh is necessary. The aim of this research is to determine earthquake clusters, which can be used for disaster mitigation and prevention measures. This research begins with examining the distribution pattern of data through a scatterplot. If the data distribution pattern shows varying density levels, then a clustering analysis using Ordering Points to Identify the Clustering Structure (OPTICS) is conducted. In the OPTICS algorithm, two parameters are required before clustering: minimum points (MinPts) and xi. The clustering results are evaluated using the silhouette coefficient (SC). Further data exploration is carried out by: (1) reducing the MinPts value, (2) clustering based on the supremum of the SC, and (3) identifying earthquake events with potential tsunami risks. The purpose of this data exploration is to increase the number of clusters formed while still considering the SC value limit, thus expanding the regions identified as earthquake-prone. The best clustering results are determined by comparing the obtained clusters. The best clustering result was found at MinPts = 20 and xi = 0.05, forming 4 clusters with a silhouette coefficient value of 0.72, indicating a strong cluster structure. This information is expected to benefit the public by raising awareness of earthquake-prone areas.
Hauling rocks or minerals in open-pit mines often experience inefficiencies due to a lack of real-time monitoring and assignment of dump trucks, resulting in reduced productivity and error prone manual data recording. This study evaluates the impact of the Fleet Management System (FMS) on dump truck productivity and discrepancies in gold grade data using four data sources: manual production data, block model data, raw FMS data, and geological sampling data from the processing plant. Key productivity metrics including payload, job efficiency, fixed time, and truck speed were analyzed along with comparisons of gold grade data between Ore Movement and Ore Crusher datasets. Results showed a 15% productivity increase for the CAT773 dump truck (from 147 BCM/km to 169 BCM/km) and a 15.6% rise for the CAT745 (from 96 BCM/km to 111 BCM/km). Additionally, the discrepancy in gold grade decreased by 5.3%, achieving a tonnage difference of 0%. Statistical analysis via a paired t-test confirmed significant improvements (p-value < 0.05). The FMS implementation significantly enhances productivity and gold grade data accuracy in mining material transport, driven by improvements in payload, job efficiency, fixed time, and truck speed.
This study investigates the influence of CNC router machine parameters on wood surface roughness during engraving operations. Surface roughness directly affects paint adhesion, appearance, and quality of finished wood products. The main problem is that current machining practices use the same parameters for all wood types, leading to inconsistent surface quality. The objective is to find optimal cutting parameters for different wood species using the Taguchi method. Three wood types were tested: Merbau (hardwood), Pine (softwood), and Plywood (manufactured wood). The experiment used a CNC Router Z2-1325 with three parameters at three levels each: spindle speed (5,000-10,000 rpm), feed rate (100-300 mm/min), and depth of cut (3-9 mm). The Taguchi L9 orthogonal array design was used, requiring only nine experimental runs. Surface roughness was measured using a Mitutoyo Surftest SJ-210 portable tester. Results showed that different wood types require different optimal parameters. Merbau achieved the best surface finish (Ra = 2.093 μm) with 8,000 RPM spindle speed, 200 mm/min feed rate, and 9 mm depth of cut. Pine performed best (Ra = 4.318 μm) at 8,000 rpm, 100 mm/min, and 3 mm depth. Plywood achieved Ra = 2.093 μm at 8,000 rpm, 200 mm/min, and 6 mm depth. Statistical analysis showed that depth of cut was most important for Pine (p = 0.014), while 8,000 rpm was consistently optimal across all wood types. The optimization approach improved surface quality by 20-35% compared to standard practices. This study provides the systematic comparison of CNC machining parameters across hardwood, softwood, and manufactured wood categories. The findings give manufacturers practical guidelines for achieving better surface quality while reducing costs in wood processing industries. The Taguchi method proved effective for optimizing multiple parameters simultaneously with minimal experimental effort.
The growing need for renewable energy and effective waste management has encouraged the development of microbial fuel cells (MFCs), which convert organic waste into electricity. This study investigates electricity generation from food waste leachate, specifically rice slurry, using a dual-chamber MFC. The anode chamber was filled with rice slurry as the substrate, while the cathode chamber contained water with potassium permanganate as the oxidizing agent. Experimental trials were conducted under three conditions: (i) electrode comparison between copper and carbon graphite plates, (ii) temperature variations at room temperature and direct sunlight, and (iii) addition of potassium ferricyanide in the anode and potassium permanganate in the cathode chambers. Performance was evaluated by monitoring voltage, current, and power output across 10 days. Results showed a peak voltage of 137 mV, current of 1.37 mA, and maximum power of 0.1877 mW using graphite electrodes on Day 5. Elevated temperatures enhanced microbial activity and energy generation, while chemical additives improved electron transfer and redox efficiency. These findings demonstrate the potential of MFCs to provide dual benefits of renewable energy generation and sustainable waste reduction, supporting scalable eco-friendly energy solutions.
