During the recent years, PM2.5 concentration is rising above the safety exposure limit in Thailand. PM2.5 could have originated from various sources such as exhaust fumes, open air burning, wildfire, etc. This concludes that all cities or places would have different PM source contributions. Most studies regarding the PM source findings were done based on chemical analysis. Our research team would like to predict the PM sources physically by nanostructures analysis. These methods would require the PM dust to be collected in a limited amount of time and dry. The use of paper filters may cause contamination from filter material which may cause errors in result evaluation. Our team suggests using Electrostatic Precipitator (ESP) where electrostatics is used to capture PM dust. This research mainly focuses on designing and building the ESP system for PM collection whereas the requirement is to collect at least 100 mg of PM dust within 1 day which would be adequate for nanostructure analysis. The study revealed that the customized ESP system could achieve of up to 80% collecting efficiency (which is more than the commercial ESP that we previously used), there’s a also a parametric study of relationships between flow velocity and collecting efficiency where collecting efficiency is inversely proportional to flow velocity. The suggested air velocity is not to exceed 2 m/s. However, there’re still more room for improvement of the ESP system for PM collection such as the convenience of PM collection process which resulted from the ESP construction geometry and sizes.
มลพิษทางอากาศนั้นส่งผลกระทบโดยตรงต่อสิ่งแวดล้อมและสุขภาพของสิ่งมีชีวิตทั้งในระยะสั้นและระยะยาว มลพิษทางอากาศนั้นสามารถจำแนกได้เป็นหลายกลุ่ม นั่นคือ มลพิษในรูปแบบสถานะแก๊ส ได้แก่ แก๊สโอโซน แก๊สไนโตรเจนออกไซค์ เป็นต้น และ มลพิษในรูปแบบของแข็ง ได้แก่ ฝุ่นละอองต่างๆ ซึ่งจะถูกจำแนกด้วยขนาดเป็นหลัก ในที่นี้ ฝุ่นละอองขนาดเล็ก (Particulate Matter) ตัวย่อ “PM” จะเป็นประเด็นสำคัญของงานวิจัยนี้เนื่องด้วยในปัจจุบัน สถานการณ์ฝุ่น PM2.5 ใน ประเทศไทยโดยเฉพาะภาคกลางและภาคเหนือ มีแนวโน้มที่จะรุนแรงขึ้นในฤดูหนาวของทุกปี
คณะวิศวกรรมศาสตร์
Design and Development of a Remote Battery Management System This research focuses on the design and development of a battery management system that enables remote monitoring and control, allowing users to customize battery cell properties as needed. The system is specifically designed for use with graphene battery cells and can be effectively applied to alternative energy systems for residential use.
วิทยาลัยการจัดการนวัตกรรมและอุตสาหกรรม
The Age-Defying Dates Palm Serum is an innovative skincare product formulated with date palm extract, known for its high antioxidant content that helps reduce wrinkles and retain skin moisture. Combined with hyaluronic acid, the serum enhances hydration and skin elasticity. This research explores factors influencing consumers’ purchasing intentions, revealing a preference for anti-aging properties, product safety, and natural ingredients. This serum aims to provide a novel option in the skincare market by utilizing high-quality natural extracts.
คณะเทคโนโลยีการเกษตร
Durian is a crucial economic crop of Thailand and one of the most exported agricultural products in the world. However, producing high-quality durian requires maintaining the health of durian trees, ensuring they remain strong and disease-free to optimize productivity and minimize potential damage to both the tree and its fruit. Among the various diseases affecting durian, foliar diseases are among the most common and rapidly spreading, directly impacting tree growth and fruit quality. Therefore, monitoring and controlling leaf diseases is essential for preserving durian quality. This study aims to apply image analysis technology combined with artificial intelligence (AI) to classify diseases in durian leaves, enabling farmers to diagnose diseases independently without relying on experts. The classification includes three categories: healthy leaves (H), leaves infected with anthracnose (A), and leaves affected by algal spot (S). To develop the classification model, convolutional neural network (CNN) algorithms—ResNet-50, GoogleNet, and AlexNet—were employed. Experimental results indicate that the classification accuracy of ResNet-50, GoogleNet, and AlexNet is 93.57%, 93.95%, and 68.69%, respectively.