Srinakharinwirot University Engineering Journal

Enhancing Engineering Design Skills and Environmental Awarenessthrough a STEM-Based Educational Game

2025-10-22T20:33:28+07:00

Addressing climate change necessitates educational innovations to foster engineering problem-solving skills among youth. This research aims to evaluate the efficiency and effectiveness of a STEM-based educational game in enhancing engineering design skills and environmental awareness. The participants were 36 eleventh-grade students selected via stratified random sampling. Research instruments included the ‘Zero Carbon’ board game, a situational test for engineering design skills, and an environmental awareness questionnaire. Data were analyzed using descriptive statistics and a one-sample t-test against an 80% criterion. The findings revealed that the game innovation met the efficiency criteria (E1/E2 = 80.93/83.05). Moreover, students’ post-intervention engineering design skills and environmental awareness were significantly higher than the 80% criterion (p < .05). This study thus confirms that a STEM-based educational game is an effective instrument for developing engineering competencies and fostering sustainable environmental consciousness.

The Study and Analysis of Causes and Preventive Guidelines for Fire Incidents in Plastic Pellet Factories by What If Analysis

2026-01-23T09:26:14+07:00

This research aims to investigate the factors contributing to fire incidents and to assess the risk level in a recycled plastic pellet manufacturing plant. Frequent fire occurrences in the past, each causing severe damage, highlight the necessity of conducting a systematic study using the What-If Analysis technique. This flexible qualitative method enables the identification of potential hazards even in the absence of prior incidents and facilitates risk prioritization through the development of a structured risk assessment. The analysis revealed six major risk factors: deteriorated machinery and lack of maintenance, operational errors by workers, unsafe storage of flammable materials, absence of documents for operating procedures, the nonstandard electrical system installation, and insufficient firefighting equipment and emergency-drill readiness. This study applied the What If Analysis technique to assess the risks in the recycled plastic pellet of a case study factory, involving a total of twenty-six events across twenty-one production steps. The results indicated that two events (7.69%) fell under Risk Level 4 (Unacceptable), all associated with the melting process. Additionally, eleven events (42.31%) were classified as Risk Level 3 (High Risk) and were found in shredding, high-speed washing, dewatering, hot air drying, plastic storage, and certain parts of the melting process. Meanwhile, thirteen events (50.00%) were categorized as Risk Level 2 (Acceptable with Control) and occurred in feeding, sorting, conveying, general washing, pellet cutting, and drying steps. The study results can lead to recommendations for developing an effective risk management plan for the factory. The outcomes of this study provide a practical guideline for systematically assessing fire risk for other recycled plastic pellet factories. Moreover, it can be applied to other industries with similar thermal and mechanical processes.

Development of National Freight Transport Analytics System Using GPS-Based Truck Data

2026-02-16T20:16:42+07:00

Road freight transport by trucks is the dominant freight transport mode in Thailand and plays a crucial role in the national economy and supply chains. However, freight transport planning and management at the national level has long been constrained by the lack of detailed data that accurately reflects real freight movement patterns in both spatial and temporal dimensions. This academic article aims to present the development process of National Freight Transport Analytics system using GPS-based truck data. The system utilizes GPS data collected from trucks, integrated with big data analytics techniques, to generate insights into freight movement patterns, supply chain structures, and truck operational behaviors. The developed system (validated with average trip lengths and traffic counts on road within acceptable limit’s results) is capable of processing massive volumes of GPS data, amounting to hundreds of millions of records per day, and visualizing the results through key performance indicators (KPIs). For instance, the system is capable of analyzing the overall freight movements of more than three million trips per month, derived from more than 5,000 of unique activity patterns at truck stop points and commodity trip chain. These outputs support evidence-based policymaking, logistics infrastructure planning, and freight transport management at the national scale.

Real-Time Federated Learning for Smart Energy Management in an Educational Building at RMUTSV

2025-09-16T10:30:14+07:00

Efficient electrical energy management in the Industrial Technician School Building at RMUTSV is crucial due to the complex and continuously fluctuating load characteristics. This research proposes a real-time framework based on Federated Learning (FL) for multi-phase load forecasting (Phases A, B, and C) and adaptive anomaly detection. The system integrates deep learning techniques, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Autoencoder networks. Experiments using real-time time-series data collected over a four-month period (December 2024 - March 2025) revealed that the FL model achieved a Mean Absolute Error (MAE) of 1.24A, comparable to the centralized model (MAE = 1.15A). Phase B exhibited the highest volatility, with 41 anomalies detected (54.7%). A positive correlation between load and temperature was observed (r = +0.68). The total loss graph decreased within 50 training rounds, indicating strong learning capability. After implementation, the system reduced peak load by an average of 6.3% and significantly decreased abnormal events, enhancing stability and minimizing energy loss. This study demonstrates that FL is an effective, secure, and sustainable approach for deploying Artificial Intelligence (AI) in smart grid systems.