Eco Grow Pellets are high-porosity plant-growing clay pellets made from ceramic industrial sediment, blended with ground chicken bone to enhance calcium and essential minerals, promoting strong and healthy plant growth. They are suitable for all types of plants, especially those requiring well-aerated soil with good water drainage. Eco Grow Pellets are an innovative clay-based growing medium designed to optimize plant cultivation efficiency. Their high porosity structure allows for excellent air and water circulation, reducing soil compaction and waterlogging—common causes of root rot and stunted growth. Additionally, the pellets are enriched with calcium and essential minerals from ground chicken bones, reinforcing plant structure and enhancing root strength, enabling better nutrient absorption. This product is made from 100% recycled ceramic industrial sediment, aligning with the principles of Zero Waste and the BCG Economy Model. It helps minimize industrial waste while transforming discarded materials into high-value, eco-friendly growing media. Eco Grow Pellets are ideal for vegetables, flowers, and potted plants, offering ease of use, cleanliness, and safety. They contribute to sustainable agriculture by improving both crop productivity and environmental health.
ผลิตภัณฑ์ “Eco Grow Pellet” เป็นเม็ดดินเผาน้ำหนักเบา ซึ่งผลิตจากวัสดุเหลือทิ้งในอุตสาหกรรมเซรามิกส์ และพัฒนาเพื่อเพิ่มมูลค่าสูงสุดให้กับวัสดุเหลือใช้ โดยสอดคล้องกับแนวคิด Zero Waste และหลักการของ BCG Economy Model ที่เน้นการใช้ทรัพยากรให้คุ้มค่าในทุกช่วงของวงจรชีวิต นอกจากนี้ โครงการยังเพิ่มปุ๋ยจากกระดูกไก่ ซึ่งเป็นแหล่งแคลเซียมและฟอสฟอรัสที่สำคัญ เพื่อเสริมคุณค่าทางอาหารสำหรับพืช และช่วยลดการพึ่งพาปุ๋ยเคมี โดยเน้นกระบวนการผลิตที่ยั่งยืนและเป็นมิตรต่อสิ่งแวดล้อม
คณะวิศวกรรมศาสตร์
Currently, lithium batteries are widely used in electronic devices and electric vehicles, making the estimation of their State of Health (SOH) crucial. Accurate SOH estimation helps extend battery lifespan, reduce maintenance costs, and prevent safety issues such as overheating or explosions. This project aims to study and analyze mathematical models of batteries and develop SOH estimation techniques using Neural Networks to enhance accuracy and evaluation speed. The experiment involved collecting charge and discharge data from three lithium battery cells under controlled temperature conditions while maintaining a constant current. The current, voltage, and time data were recorded and analyzed to determine the battery capacity for each cycle. These data were then used to train a Neural Network model. The results demonstrated an effective method for predicting battery health status. The outcomes of this project can contribute to the development of a Battery Management System (BMS) that improves battery efficiency and longevity. Additionally, it provides a foundation for applying artificial intelligence techniques in the energy sector effectively.
คณะบริหารธุรกิจ
This research aimed to develop the mixed tea from longan peels and seeds. Population studied were longan farmers who planted longan and preserved the longan product in Ampur Wang Nam Yen, Sa Kaeo Province. From the results, it was found that from By-product in the production of dehydrated longan, longan peels and seeds, which can be processed into ready-to-drink powdered tea. This not only helps reduce waste from the production process but also contributes to generating additional income from these by-products.
คณะวิศวกรรมศาสตร์
Jaundice, a common condition in infants that results from high bilirubin levels in the blood, often requires early diagnosis and monitoring to prevent severe complications, especially in newborns. Traditional diagnostic methods can be time-consuming and subject to human error. This study proposes an approach for real-time jaundice detection using advanced image processing techniques and machine learning algorithms. By analyzing images captured in RGB color spaces, pixel values are extracted and processed through Otsu’s thresholding and morphological operations to detect color patterns indicative of jaundice. A classifier model is then trained to distinguish between normal and jaundiced conditions, offering an automated, accurate, and efficient diagnostic tool. The system’s potential to operate in real-time makes it particularly suited for clinical settings, providing healthcare professionals with timely insights to improve patient outcomes. The proposed method represents a significant innovation in healthcare, combining artificial intelligence and medical imaging to enhance the early detection and management of jaundice, reducing reliance on manual interventions and improving overall healthcare delivery.