International Journal of Integrated Engineering
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Environmental Risk Assessment of Sarimukti Landfill Postfire in Indonesia
Full text linkIndonesia aims to ensure that 100% of urban waste is properly managed focusing 80% on waste collection and 20% on reduction, while transitioning toward a processing-based waste management system. Despite various solutions, achieving substantial progress remains difficult. Waste pollution, including unsightly waste, foul odors, and hazardous leachate, negatively impacts the environment. The fire at Sarimukti landfill was caused by careless disposal of cigarette butts during the dry season. Exacerbated the situation and affected more than 15 hectares. Therefore, an environmental quality evaluation using an Integrated Risk-Based Approach (IRBA) is required. According to Ministry of Public Works Regulation Number 03 of 2013, this evaluation is crucial before deciding whether to rehabilitate or close the landfill. This study characterizes waste during a fire disaster, assesses leachate quality in Sarimukti landfill treatment facility, and conducts a rapid environmental assessment using the IRBA method to determine landfill feasibility. The burned waste had an average moisture content of 10.41%, volatile matter of 49.04%, ash content of 50.95%, fixed carbon of 31.05%, and a calorific value of 3,391.19 cal/g. The leachate quality exceeded standards for BOD, COD, and total nitrogen, while pH, TSS, mercury, and cadmium remained within acceptable limits. The final Environmental Risk Index assessment yielded a very high hazard evaluation of 622.24, indicating that the landfill should be closed due to its significant environmental and social impacts
Application of Artificial Intelligence in Short-Term Load Forecasting at Low-Voltage Substations
Full text linkThis study presents a Short-Term Load Forecasting (STLF) process in Vietnam. A total of 13 features, including 9 electricity-related features and 4 time-related features, are extracted to predict the Total Active Power (TAP) of a three-phase low-voltage transformer station. The Mutual Information (MI) analysis results show that the Active Power (AP) features in the three phases have a significantly higher importance level than the other features in predicting TAP. Two algorithms, including Deep Neural Network (DNN) and Artificial Neural Network (ANN), are used to predict TAP. In addition to the two models using all 13 features, two corresponding models using only 3 AP features are also trained for comparison. Results show that, with 17 047 data points, models using 3 AP features have a suitable level of complexity for better results than similar models using all 13 features. The DNN model with 3 features yields the best results, with MAE, RMSE, and R2 values of 1.9089, 4.2880, and 0.9971, respectively. In future research, the number of data points will be improved to explore better features, and other features could also be considered
Solar Power Forecasting of 8 MWp Solar Farm Malacca using LSTM-based Model with Weather Forecast Data: A Case Study of Malaysia
Full text linkMalaysia\u27s tropical climate offers significant potential for photovoltaic (PV) installations due to abundant solar irradiance. However, the variability in solar energy generation due to weather fluctuations presents challenges for grid integration and energy reliability. The study evaluates the performance Univariate LSTM, Multivariate LSTM (with weather sensor data), Multivariate LSTM (with weather sensor and meteorological data), and Bidirectional LSTM (Bi-LSTM) models using input data from 8 MWp solar farm in Ayer Keroh, Malacca, along with weather sensor and meteorological data. Results show that the Univariate LSTM model consistently outperformed others across all forecasting horizons, achieving the lowest error metrics (MAE: 0.0275, MSE: 0.0037, RMSE: 0.0611) and the highest R² value (0.94), making it the most reliable choice for both short- and long-term forecasts. However, weather uncertainty remains a significant challenge, directly impacting solar power production. Thus, Multivariate LSTM models is more practical. From the result, Multivariate LSTM with weather sensor and MET input data demonstrated some advantages as its gives lowest error metrics (MAE: 0.0375, MSE: 0.0044, RMSE: 0.0664) and the highest R² value (0.92), compared to Multivariate LSTM with weather sensor input and Bi-LSTM model. However, at intermediate horizons the accuracy is decreased which might be caused by the increased complexity of meteorological inputs. In contrast, the Bi-LSTM model performed the weakest, with the highest error metrics, suggesting potential overfitting or limited generalization capabilities. This research provides valuable insights into the trade-offs between model simplicity and performance including across different forecasting horizons in renewable energy forecasting
Performance Evaluation of Palm Oil-based Grease for Wear and Vibration Control in Spur Gears
Full text linkThe reliability and efficiency of gear systems are critically dependent on effective lubrication, which minimizes friction and wear between gear teeth. While conventional mineral-based greases are widely used, growing environmental concerns and the demand for renewable alternatives have prompted interest in sustainable lubricants. The formulation of palm oil-based grease and its application in gear systems remain underexplored, particularly regarding tribological performance under dynamic operating conditions. This study addresses this gap by fabricating a custom gear test rig to investigate the surface wear and vibration characteristics of spur gears lubricated with palm olein-based grease enhanced with molybdenum disulfide (MoS₂) nanoparticles. The experiment was conducted with an applied torque of 0.31 Nm, operating at 300 rpm for 240,000 cycles. Lubrication performance was assessed through surface wear analysis using Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS), while vibration data were collected after 240,000 gear cycles using a data acquisition system. The formulated grease PG was benchmarked against a commercial mineral-based grease. Results indicate that the MoS₂ enhanced palm oil grease exhibits superior tribological behavior, with very small mass loss of 6.28x10-2 %, indicating low surface wear. The grease exhibited moderate vibration levels at 0.8 m/s², performing better than mineral grease MG1 and slightly less effectively than MG2 in suppressing gear vibration. EDS analysis confirmed the presence of MoS₂ elements on the worn gear surfaces, indicating effective film formation and adherence. These findings highlight the potential of palm oil-based grease as a sustainable alternative for gear lubrication, offering superior wear resistance and lower vibration levels for better operational efficiency
Zinc Oxide (ZnO) Nanoparticles from Eichhornia Crassipes for Inactivating Pathogenic Bacteria in Greywater
Full text linkPathogenic bacteria are dangerous bacteria found in greywater produced from household activities. E. Coli and S. aureus are among bacteria that have become an environmental issue and cause negative impact to the ecosystem. The ZnO nanoparticles (NPs) have been found as good deactivating agents for bacteria in greywater. This research examines how pathogenic bacteria in greywater can be neutralized by utilizing ZnO NPs derived from the aquatic grass, Eichhornia crassipes. E. Coli and S. aureus were made using the serial dilution technique by successive resuspension in predetermined amounts of liquid diluent. E. Coli and S. aureus were mature by culture on the agar plate. The pathogenic inactivation efficiency of ZnO NPs was optimized by varying ZnO NPs dosage and irradiation time using Response Surface Methodology (RSM). The microstructural analysis demonstrated that the ZnO involved of bigger and smaller particles with sizes in the scale of 51.9 nm to 31.0 nm. The greatest inactivation efficiency of ZnO-E. crassipes for bacteria in greywater was 3.498 log for E. Coli and 3.368 log for S. aureus. The ZnO-E. crassipes has the potential to inactivate pathogenic bacteria
Electric Field Characteristics of Various Percentages of LLDPE-Natural Rubber Composition Under Moisture Conditions
Full text linkEnhancing the insulation performance of high-voltage cables is critical for ensuring long-term reliability in modern power systems. One major concern is the degradation of insulation due to sustained exposure to high electric fields, which can lead to flashover events. This study focuses on the electric field behavior of Linear Low-Density Polyethylene (LLDPE) blended with Natural Rubber (NR), examined under both dry and moisture-exposed conditions. Composite samples containing 0% to 30% NR were prepared through mechanical blending and submerged in water for 70 days to evaluate moisture absorption. To support simulation work, the relative permittivity of each sample in both conditions was measured using a Keysight 16514B dielectric test fixture.These permittivity values were incorporated into a COMSOL electrostatic model with cylindrical electrodes to simulate electric field distributions. Among all tested compositions, the sample with 30% natural rubber consistently demonstrated the lowest electric field intensity, even under moist conditions. This enhanced performance is attributed to the influence of natural rubber on the dielectric properties, promoting a more uniform electric field distribution despite higher water uptake. The findings suggest that LLDPE-NR composites with higher NR content hold significant potential for improving insulation in high-voltage cable applications
Flowability Properties of PLA/PA12 Composite with Varying Wollastonite Concentrations for 3D Printing Applications
Full text linkThis study evaluates the rheological properties and flow behaviour of PLA/PA12 composites with varying concentrations of wollastonite (WA) ceramic particles (5, 10, and 15 wt.%) to enhance their biological performance and printability in 3D printing applications. Achieving the right balance of viscosity and flow is crucial for producing high-quality filaments and reliable 3D printed structures. Comprehensive rheological analysis and material characterization were conducted, including particle size distribution, SEM, and EDX. The flow behaviour index (n) was calculated, and physical observations of extruded materials were assessed for surface quality and dimensional consistency. The 10 wt.% WA composite consistently demonstrated superior rheological properties, exhibiting optimal pseudoplastic behavior with an n value range of 0.073–0.439 and a viscosity of 5919 Pa·s at 140 °C, which was the lowest among the composites tested, ensuring smooth extrusion and structural integrity. SEM analysis showed a uniform microstructure with well-dispersed WA particles in the 10 wt.% WA composite, while the 5 wt.% WA and 15 wt.% WA composites displayed suboptimal particle distribution. Physical observations confirmed that the 10 wt.% WA composite produced a smooth, consistent extrudate, essential for high-quality filament fabrication and reliable 3D printing. These findings highlight the 10 wt.% WA composite as the most promising candidate for efficient and effective 3D printing
Design of a High Efficiency Single-Bit Full Adder Using Modified Gate Diffusion Input (MGDI) Technique
Full text linkAdders are essential parts of digital systems where critical design factors like size, power consumption, and latency are critical. This work presents a single-bit full adder based on the Modified Gate Diffusion Input (MGDI) technique to enhance the efficiency of these parameters. Extensive simulations were conducted using Mentor Graphics and 130nm CMOS technology, with extensive analysis comparing the proposed adder\u27s performance against a standard CMOS adder across different voltage supply levels. The proposed adder utilizes only 8 transistors, significantly fewer than the 28 transistors required in conventional CMOS full adders. The MGDI technique proves highly effective, reducing power dissipation by 98.8%, area consumption by 18.1%, and propagation delay by 86.1%, while also simplifying circuit complexity. The suggested adder continuously exhibits decreasing power consumption and shorter propagation delay as the supply voltage rises, highlighting its appropriateness for high-performance, low-power applications. The reduced transistor count and minimized wiring complexity further establish the proposed adder as a compelling alternative to traditional CMOS designs
Effect of Scour and Seismicity on the Bridge’s Response
Full text linkBridges are critical points in rail and road networks, and their failure due to natural hazards like tsunamis, earthquakes, ground movements, or floods can cause significant losses. Identifying weaknesses and measuring bridge strength is crucial for disaster resilience. Current methods rely heavily on engineering judgment, lacking dependable quantitative evaluations. This framework offers a comprehensive approach to creating numerical simulations and assessing bridge resilience against multiple hazards. The project involves simulating a 130-metre pre-stressed concrete bridge exposed to seismic and scour depth hazards using the CSI Bridge software. The simulations consider three scour depth levels (1Df, 1.5Df, and 2Df), five earthquakes, and varying seismic intensities (0.25 to 1.5 Peak Ground Acceleration, PGA, with 0.125 increments). The foundation depth (Df) is 2.5 metres, and simulations are run under clay soil. Nonlinear Time History Analysis (NTHA) is employed for its suitability for inelastic beam-column elements under dynamic loading. Results indicate that increasing the scour depth from 1.5Df to 2Df increased pier displacement by 21.68% in clay
Optimizing Solar Panel Cleaning with Kalman Filter-Enhanced Mobile Robotics
Full text linkThis study proposes the use of the Kalman filter method to accurately determine the position of the robot so that it can monitor the efficiency of the solar panels. This method is applied to the mobile solar panel cleaning robot, the Kalman filter is used to process data from the Inertial Measurement Unit (IMU) sensor on the robot specifically on the z-axis to accurately determine the position of the robot in the Cartesian coordinate system. The robot\u27s performance tests show that the accuracy of the displacement measurement of the encoder corresponds to the pulse value. The test results showed that the use of the Kalman filter could significantly reduce the total error in the sensor data, namely when before using the Kalman filter, the total error from the reference axis gradient was 47.17 degrees, while by using the Kalman filter, the total error was 0.23 degrees, which means that the effectiveness of dust cleaning by the robot showed that the robot was able to reach the target coordinates with a high level of accuracy. Then, the mobile solar panel cleaning robot is taken simultaneously to monitor and maintain the efficiency of the solar panel in terms of dust and temperature drop. The efficiency of solar panels with a temperature drop of 5-6 degrees Celsius. The result of this study is a solar panel cleaning robot equipped with the Kalman filter algorithm to lower the temperature and clean dust. The total movement error of the robot was 0.73 for the X coordinates and 0.79 for the Y coordinates. The decrease in temperature had a positive effect on the increase in power by 2% from 85% to 87%. The results of this study show that the performance of the system is maintained in optimal conditions even though temperature fluctuations are successfully treated to increase the efficiency of the system, the temperature reduction according to the standard conditions (STC) is still not optimal, so further research and improvement is needed in the temperature reduction to achieve higher efficiency