Stirling engine is the external heated engine that heat is sup-plied externally to the heater part of the engine. Thus, Stirling cycle engine can be employed with various sources of renewable energy such as biomass, biofuel, solar energy, geothermal energy, recovery heat, and waste. The integration of gasifier, burner, and heat engine as a power system offers more fuel choices of each local area with potential resources resulting independent from shortage and cost fluctuation of fossil fuel. This research aims to investigate the integration of the Stirling engine with a wood pellet gasifier for electric power generation. Biomass can be controlled to have continuously combustion with ultra-low toxic emission. Stirling engine, therefore, is a promising alternative in small-scale-electricity production. Even though many biomass-powered Stirling engines were successfully constructed and marketed but these engines and the use of biomass resources as fuel for power generation are quite new concepts in some developing countries. Especially, the capital cost of this engine is high and unaffordable for installation compared to other power systems. Therefore, this research aims to the study attractive and feasibility of the compact Stirling engine with green energy.
เนื่องจากความต้องการพลังงานที่มีมากขึ้น แต่เชื้อเพลิงฟอสซิลซึ่งเป็นแหล่งพลังงานหลักมีอยู่อย่างจำกัดและเป็นสาเหตุหนึ่งของมลพิษและภาวะโลกร้อน ดังนั้นพลังงานทางเลือกจึงเป็นกุญแจสำคัญเพื่อความยั่งยืนด้านพลังงาน ประเทศไทยมีศักยภาพของพลังงานชีวมวลจากเกษตรกรรม ดังนั้นการพัฒนาระบบผลิตไฟฟ้าที่มลพิษต่ำและสามารถใช้ได้กับแหล่งพลังงานทดแทนจึงจำเป็นอย่างยิ่ง โดยเฉพาะเครื่องยนต์สเตอร์ลิงซึ่งมีโครงสร้างชิ้นส่วนไม่ซับซ้อน ปราศจากการสันดาปภายในเครื่องยนต์จึงเป็นเครื่องยนต์ที่มีศักยภาพผลิตไฟฟ้าด้วยพลังงานสะอาดและเป็นมิตรกับสิ่งแวดล้อมและความสำเร็จของโรงไฟฟ้าเครื่องยนต์สเตอร์ลิง ในประเทศไทย เพื่อคนไทย
คณะวิทยาศาสตร์
A new colorimetric assay for the rapid detection of tannic acid in beverage samples based on displacement phenomenon of aggregated gallic acid-modified platinum nanoparticles is developed for the first time. PtNPs were functionalized with gallic acid, promoting the formation of the green-hued aggregated nanoparticles. While colorimetry offers a rapid method for identifying tannic acid, challenges remain in sensitivity and accuracy of detection on the PtNPs colorimetric probe, particularly in the presence of anthocyanin interferences. To address this, we developed a sample preparation method to degrade anthocyanin in beverages. Tannic acid was easily displaced onto the gallic acid-coated PtNPs surfaces, causing dispersion and resulting in a visible color change from green to orange−brown. Under the optimal conditions, the colorimetric sensor exhibited a linear response in the range of 1−2,000 µmol L−1 (R2 = 0.9991). The limit of detection (LOD) and the limit of quantification (LOQ) were found at 0.02 and 0.09 µmol L−1, respectively. The proposed sensor expressed superior selectivity over other interfering substances and demonstrated excellent precision with a relative standard deviation (RSD) of 1.00%−3.36%. More importantly, recoveries ranging from 95.0−104.7% were obtained, indicating the capability of proposed colorimetric sensor to detect tannic acid rapidly and accurately in real beverage samples.
คณะวิศวกรรมศาสตร์
Under The National Broadcasting and Telecommunications Commission (NBTC), the Telecommunication Enforcement Bureau collects a lot of data on service quality by monitoring and controlling the quality of telecommunications services, mainly by assessing mobile network infrastructure. The NBTC used Microsoft Excel for data analysis but became ineffective and slow. We used Python programming for preparation, analysis, and data processing to address this. Raw data was obtained from the Syberiz program in CSV format, processed in Python, and displayed on a dashboard. The dashboard, developed using Power BI, meets NBTC's telecommunications quality standards. It features maps, test results, and graphical representations. This method enhances the dashboard's appearance and usability and speeds up data processing and visualization compared to Microsoft Excel. This project is primarily designed to help the Telecommunication Enforcement Bureau's operations by making data processing and display for telecommunications quality monitoring faster, more effective, and easier to use.
คณะวิทยาศาสตร์
This special problem aims to study and compare the performance of predicting the air quality index (AQI) using five ensemble machine learning methods: random forest, XGBoost, CatBoost, stacking ensemble of random forest and XGBoost, and stacking ensemble of random forest, SVR, and MLP. The study uses a dataset from the Central Pollution Control Board of India (CPCB), which includes fifteen pollutants and nine meteorological variables collected between January, 2021 and December, 2023. In this study, there were 1,024,920 records. The performance is measured using three methods: root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination. The study found that the random forest and XGBoost stacking ensemble had the best performance measures among the three methods, with the minimum RMSE of 0.1040, the minimum MAE of 0.0675, and the maximum of 0.8128. SHAP-based model interpretation method for five machine learning methods. All methods reached the same conclusion: the two variables that most significantly impacted the global prediction were PM2.5 and PM10, respectively.