This project aims to propose a design for a red offal processing room in a pork processing plant that processes 500 pigs per day or 80 pigs per hour. Each pig weighs approximately 105 kilograms, with 3.47% of the weight consisting of red offal. The process involves separating liver, gall bladder, heart, lungs, spleen, and kidneys as required. These parts are then chilled in cold water to reduce their temperature to below 7°C before packaging and sealing. Sorting is based on the number of pieces and weight, depending on the type of product. The processing times of sorting chilling and packaging vary depending on the product's type and size. The design was developed using data collected from the current production line and referenced standards. The room layout was planned using Systematic Layout Planning (SLP) principles to analyze activity relationships within the room and define functional areas. Equipment sizes and the required number of operators were calculated to ensure optimal use of space. The red offal processing room was designed with an area of 56 square meters. After the layout design was completed, a 3D model was created using SketchUp 2024, and the workflow and operations were simulated and analyzed using Flexsim 2024
บริษัท เบทาโกร จำกัด (มหาชน) ได้มีแผนการขยายโรงงานแปรรูปสุกรทั้งในประเทศและต่างประเทศ การออกแบบกระบวนการผลิตจึงเป็นสิ่งสำคัญที่ทำให้โรงงานที่จะก่อตั้งนั้นสามารถใช้งานได้อย่างมีประสิทธิภาพทั้งในด้านกระบวนการการผลิตและความสะอาด เพื่อทำให้สินค้าที่ส่งมอบแก่ผู้บริโภคมีคุณภาพที่ดี โดยผลิตภัณฑ์ที่สำคัญของโรงงานแปรรูปสุกร ได้แก่ กลุ่มชิ้นส่วนสุกรตัดแต่ง และกลุ่มเครื่องในสุกร ซึ่งประกอบไปด้วยเครื่องในแดง และเครื่องในขาว โครงงานนี้ได้รับมอบหมายให้ศึกษากระบวนการทำงานเพื่อออกแบบห้องเครื่องในแดงให้รองรับการผลิตที่สูงขึ้นและมีความหลากหลายของผลิตภัณฑ์มากขึ้น โดยกระบวนการทำงานภายในห้องถูกต้องเป็นไปตามหลักการปฏิบัติที่ดีสำหรับโรงแปรรูปสุกรของกรมปศุสัตว์
คณะวิทยาศาสตร์
Bacteriocins are microbial peptides that demonstrate potency against pathogens. This study evaluated the inhibitory effects on pathogens and characterized the bacteriogenomic profile of strain TKP1-5, isolated from the feces of Anas platyrhynchos domesticus. Strain TKP1-5 was characterized using phenotypic traits, 16S rRNA sequencing, and Whole-Genome Sequencing (WGS). It exhibited growth in the presence of 2-6% NaCl, temperatures of 25-45°C, and pH levels ranging from 3 to 9. Based on ANIb, ANIm, and dDDH values, strain TKP1-5 was identified as Lactococcus lactis. Whole genome analysis revealed that strain TKP1-5 harbors the Nisin Z peptide gene cluster with a bit-score of 114.775. The antimicrobial spectrum of bacteriocin TKP1-5 showed inhibitory effects against pathogenic bacteria including Pediococcus pentosaceus JCM5885, Listeria monocytogenes ATCC 19115, Enterococcus faecalis JCM 5803T, Salmonella Typhimurium ATCC 13311ᵀ, Aeromonas hydrophila B1 AhB1, Streptococcus agalactiae 1611 and Streptococcus cowan I. Genomic analysis confirmed L. lactis TKP1-5 as a non-human pathogen without antibiotic resistance genes or plasmids. Furthermore, L. lactis TKP1-5 contains potential genes associated with various probiotic properties and health benefits. This suggests that L. lactis TKP1-5, with its antibacterial activity and probiotic potential, could be a promising candidate for further research and application in the food industry.
คณะวิศวกรรมศาสตร์
The Thai Sign Language Generation System aims to create a comprehensive 3D modeling and animation platform that translates Thai sentences into dynamic and accurate representations of Thai Sign Language (TSL) gestures. This project enhances communication for the Thai deaf community by leveraging a landmark-based approach using a Vector Quantized Variational Autoencoder (VQVAE) and a Large Language Model (LLM) for sign language generation. The system first trains a VQVAE encoder using landmark data extracted from sign videos, allowing it to learn compact latent representations of TSL gestures. These encoded representations are then used to generate additional landmark-based sign sequences, effectively expanding the training dataset using the BigSign ThaiPBS dataset. Once the dataset is augmented, an LLM is trained to output accurate landmark sequences from Thai text inputs, which are then used to animate a 3D model in Blender, ensuring fluid and natural TSL gestures. The project is implemented using Python, incorporating MediaPipe for landmark extraction, OpenCV for real-time image processing, and Blender’s Python API for 3D animation. By integrating AI, VQVAE-based encoding, and LLM-driven landmark generation, this system aspires to bridge the communication gap between written Thai text and expressive TSL gestures, providing the Thai deaf community with an interactive, real-time sign language animation platform.
คณะอุตสาหกรรมอาหาร
This study aims to investigate the encapsulation of anthocyanins in water-in-oil-in-water (W/O/W) emulsions and their spray-drying process to enhance anthocyanin stability against external factors such as light, temperature, and pH changes. The W/O/W emulsion was prepared using suitable surfactants and dried using a spray dryer at an inlet temperature of 120–140°C and an outlet temperature not lower than 80°C. The results showed that the composition ratios of water, oil, and surfactants significantly influenced the physical and chemical properties of the emulsion, as well as the encapsulation efficiency of anthocyanins. The spray-dried W/O/W emulsion demonstrated effective anthocyanin retention and improved long-term stability, making it applicable for food and health-related products.