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
คณะวิศวกรรมศาสตร์
The production process of the food rancidity indicator label consists of three main steps: 1) preparation of the indicator solution, 2) preparation of the cellulose solution, and 3) formation of the sheet. The indicator solution includes bromothymol blue and methyl red, which act as indicators. The cellulose solution consists of hydroxypropyl methylcellulose, carboxymethyl cellulose, sodium hydroxide, polyethylene glycol 400, and the indicator solution. For the sheet formation, the cellulose solution was mixed with natural latex to increase flexibility and impart hydrophobic properties. After drying, the invention appears as a thin, dark blue label. When exposed to volatile compounds from rancid food, the label changes color from dark blue to green, and then to yellow, corresponding to the increasing amount of volatile compounds from the rancid food.
คณะวิทยาศาสตร์
This special problem aims to compare the performance of machine learning methods in time series forecasting using lagged time periods as independent variables. The lagged periods are categorized into three groups: lagged by 10 units, lagged by 15 units, and lagged by 20 units. The study employs four machine learning methods: Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The time series data simulated as independent variables diverse including characteristics: Random Walk data, Trending data, and Non-Linear data, with sample sizes of 100, 300, 500, and 700. The research methodology involves splitting the data into 90% for training and 10% for testing. Simulations and analysis are performed using the R programming language, with 1,000 iterations conducted. The results are evaluated based on the average mean squared error (AMSE) and the average mean absolute percentage error (AMAPE) are calculated to identify the best performing method. The research findings revealed that for Random Walk data, the best performing methods are Random Forest and Support Vector Machine. For Trend data, the best performing methods are Random Forest. For Non-Linear data, the best performing methods are Support Vector Machine. When tested with real-world data, the results show that for the Euro-to-Thai Baht exchange rate, the best methods are Random Forest and Support Vector Machine. For the S&P 500 Index in USD, the best performing methods are Random Forest. For the Bank of America Corp Index in USD, the best performing methods are Support Vector Machine.
คณะวิศวกรรมศาสตร์
Inventing robots for the TPA Robotics Competition Thailand Championship 2024, game “Rice Way, Thai Way to the International Way (HARVEST DAY)”