Recent in Engineering Science and Technology

Screw Conveyor Design to Reduce Musculoskeletal Disorders (MSDs) Risk in Coconut Shell Handling

2025-04-16T12:05:29+00:00

This study aims to analyze the working posture of operators involved in feeding coconut shells into a processing machine, using the Rapid Entire Body Assessment (REBA) method. The assessment yielded a total REBA score of 9, which indicates a high level of ergonomic risk requiring immediate corrective action. This elevated score is attributed to repetitive movements during the material handling process. To mitigate the risk of Musculoskeletal Disorders (MSDs), a specially designed portable screw conveyor is proposed as an assistive tool. The proposed conveyor is expected to significantly reduce physical strain and improve the overall ergonomics of the task. Visual illustrations of the design and technical drawing are provided to support the implementation concept. This study highlights the importance of ergonomic interventions in industrial settings to promote worker health and operational efficiency.

Design of a Fertilizer Lifting Crane for the Canycom S25A Fertilizer Spreader Unit

2025-04-10T01:43:06+00:00

Manual lifting of fertilizer bags in plantation operations often leads to musculoskeletal disorders (MSDs) among workers due to repetitive, high-load, and ergonomically poor movements. This study aims to design an ergonomic fertilizer lifting crane integrated into the Canycom S25A Fertilizer Spreader with a 650-liter hopper to reduce physical strain and improve operational efficiency. A hydraulic crane system was designed with dual-segment arms, four lifting hooks, and two hydraulic cylinders, actuated via the unit’s PTO engine. Ergonomic evaluation was performed using the REBA assessment tools. The simulation results indicated that the maximum stress on critical components was within safe limits, with a factor of safety above 1.5. Postural analysis showed a significant improvement, where REBA scores decreased from 12 to 2 after the crane was introduced. The design offers a reliable, low-cost, and easily manufactured solution that enhances worker safety, reduces ergonomic risk, and increases productivity in fertilizer loading processes, especially in rugged field environments. The crane can be adopted in various agricultural applications where safe material handling is essential.

Keywords: Ergonomics, Fertilizer Lifting Crane, Hydraulic system, REBA, Agricultural Mechanization

Effects of Red Rosela Tea (Hibiscus sabdariffa) as An Organic Inhibitor for Low Carbon Steel in An Environment of Sodium Chloride 3,5%

2025-10-13T08:04:34+00:00

Corrosion is an unavoidable phenomenon that is commonly observed in everyday life, particularly in infrastructure and tools constructed from low carbon steel. The corrosion of low carbon steel in chloride-rich environments presents a critical issue across numerous industrial sectors. One widely adopted method for mitigating corrosion involves the application of corrosion inhibitors, chemical substances that even in small concentrations, can significantly suppress the rate of metal degradation in corrosive environments. This research examines the potential of red roselle extract (Hibiscus sabdariffa) as a green corrosion inhibitor for low carbon steel in a 3.5% sodium chloride solution. The corrosion inhibition performance will be evaluated based on weight loss measurements over varying exposure periods of 3, 6, 9, and 12 days. Red roselle tea concentration of 10 g/L will be utilized, supplemented with an additional 2 mL of liquid inhibitor. The results of this study indicate that the maximum corrosion inhibition efficiency reaches 16% after 9 days of immersion, while the lowest efficiency is observed at 8% after 3 days. pH measurements and corrosion potential analysis suggest that red roselle tea contributes to reducing the corrosion rate by forming a protective layer on the surface of low carbon steel.

Study of the Effect of Volume of Moringa Leaves and Purple Sweet Potato Extracts as a Green Corrosion Inhibitor on the Corrosion of API 5L Steel Metals in 0.2 M HCl Environments

2025-09-06T03:01:13+00:00

This study aimed to investigate the corrosion inhibition mechanism of moringa leaves extract (Moringa oleifera) and purple sweet potato extract (Ipomoea batatas) extract as environmentally friendly inhibitors for low carbon API 5L steel in a 0.2 M HCl solution. Potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) tests were conducted with varying concentrations and combinations of the two inhibitors to evaluate their corrosion inhibition performance. The results indicated that both inhibitors individually function effectively as green corrosion inhibitors. However, their combination did not offer adequate protection for API 5L steel in a 0.2 M HCl environment. FTIR analysis of the inhibitors confirmed the presence of flavonoid compounds in both extracts. Potentiodynamic polarization tests showed that increasing the concentration of moringa leaves extract resulted in a decrease in the corrosion rate and an increase in %IE, with the highest efficiency reaching 73.08%. Similarly, an increase in the volume of purple sweet potato extract also resulted in a reduced corrosion rate, with a maximum inhibition efficiency of 65.31%. However, the combination of both inhibitors led to an increase in the corrosion rate. The results of the EIS test demonstrated that both inhibitors protect the metal by forming a protective film layer on its surface. The adsorption behavior of the inhibitors corresponds to a physical adsorption process and aligns with the Langmuir adsorption isotherm model.

Comparing MLP and 1D-CNN Architectures for Accurate RUL Forecasting in Lithium Batteries

2025-09-18T12:20:10+00:00

Accurately forecasting the Remaining Useful Life (RUL) of lithium-ion batteries is critical for optimizing battery management and ensuring operational reliability. This study compares the performance of two deep learning architectures—a Multilayer Perceptron (MLP) and a one-dimensional Convolutional Neural Network (1D-CNN)—in predicting RUL using datasets from CALCE batteries B35, B36, and B37. Data preprocessing involved outlier removal, missing value handling, and feature normalization, with key features extracted including Resistance, Constant Voltage Charging Time (CVCT), and Constant Current Charging Time (CCCT). Correlation analyses confirmed strong relationships between these features and RUL. Both models were trained and validated on preprocessed data, and their predictive accuracies were assessed using Root Mean Square Error (RMSE) and coefficient of determination (R2). Results indicated that while both architectures effectively captured battery degradation patterns, the MLP consistently outperformed the 1D-CNN, achieving on average 5% lower RMSE and 1.5% higher R2 across all tested batteries. These findings suggest that simpler fully connected networks may suffice for this forecasting task under the given feature set and preprocessing conditions. This work provides valuable insights into neural network model selection for battery health prognostics, guiding the development of efficient and accurate predictive maintenance strategies.

Front Cover Vol 03 No 04 2025

2025-11-01T07:51:11+00:00

Recent in Engineering Science and Technology (RiESTech), a peer-reviewed quarterly engineering journal, publishes theoretical and experimental high-quality papers to promote engineering and technology's theory and practice. In addition to peer-reviewed original research papers, the Editorial Board welcomes original research reports, state-of-the-art reviews, and communications in the broadly defined field of recent engineering science and technology. RiESTech covers topics contributing to a better understanding of Engineering Science and Technology, and their applications. RiESTech is concerned with scientific research on Industrial and Manufacturing Engineering, Mechanical Engineering, Material Science and Nanotechnology, Chemical Engineering, and Bioengineering, Electrical and Electronic Engineering.

Table of Content Vol 03 No 04 2025

2025-11-01T07:53:21+00:00