Developing a Smart Farming Simulation Utilizing LoRa Communication and Presenting Knowledge on LoRa Communication System Components
เทคโนโลยี LoRa (Long Range) เป็นเทคโนโลยีที่สามารถสื่อสารข้อมูลได้ระยะไกลที่สุดในหลักกิโลเมตร ใช้พลังงานต่ำและไม่มีค่าบริการรายเดือน จึงเหมาะกับการพัฒนาการเกษตรซึ่งมีพื้นที่กว้างและWifiเข้าไม่ถึง ในรูปแบบของการทำเกษตรอัจฉริยะด้วยระบบการสื่อสารแบบLoRa
คณะเทคโนโลยีการเกษตร
The development of a board game to enhance cooking skills focuses on a popular and well-known dish—burgers. This study integrates learning with interactive gameplay, allowing players to gain knowledge about burgers, including their ingredients, preparation methods, and even the basics of running a burger business. Through hands-on activities and engaging game mechanics, players can develop both their culinary skills and entrepreneurial mindset while enjoying the fun and immersive experience of the board game.
คณะเทคโนโลยีการเกษตร
The design site of 22 hectares of agricultural land in this area is located in Tatata District, Iglenburg Province. The design concept of this area aims to encourage farmers and communities to earn income from the agricultural products produced in this area, and emphasizes that this area is a "living market", which can not only create food from a variety of crops, but also establish a balanced ecosystem and promote biodiversity in this area. The market will become a sustainable market area, which is not only beneficial to society in promoting economy, but also beneficial to the environment. The area will serve as a social and learning center, where community members can fully exchange agricultural information and experience and stimulate the economy in the community in another way.
คณะวิศวกรรมศาสตร์
This cooperative education project aims to enhance the efficiency of Hydrogen Manufacturing Unit 2 (HMU-2) and Pressure Swing Adsorption 3 (PSA-3) by using AVEVA Pro/II process modeling and a Machine Learning model for process simulation. The study found that the AVEVA Pro/II model predicted outcomes with deviations ranging from 0–35%, including a hydrogen flow rate deviation from the PSA unit of 12%, exceeding the company’s acceptable limit of 10%. To address this, a Machine Learning model based on the Random Forest algorithm was developed with hyperparameter tuning. The Machine Learning model demonstrated high accuracy, achieving Mean Squared Errors (MSE) of 8.48 and 0.18 for process and laboratory data, respectively, and R-squared values of 0.98 and 0.88 for the same datasets. It outperformed the AVEVA Pro/II model in predicting all variables and reduced the hydrogen flow rate deviation to 4.75% and 1.35% for production rates of 180 and 220 tons per day, respectively. Optimization using the model provided recommendations for process adjustments, increasing hydrogen production by 7.8 tons per day and generating an additional annual profit of 850,966.23 Baht.