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
คณะเทคโนโลยีการเกษตร
The reason of this project is How to make water system during breeding Golden apple snail&Green caviar with limitation area. Make sure that waterfowl system can work
คณะเทคโนโลยีสารสนเทศ
This research presents a deep learning method for generating automatic captions from the segmentation of car part damage. It analyzes car images using a Unified Framework to accurately and quickly identify and describe the damage. The development is based on the research "GRiT: A Generative Region-to-text Transformer for Object Understanding," which has been adapted for car image analysis. The improvement aims to make the model generate precise descriptions for different areas of the car, from damaged parts to identifying various components. The researchers focuses on developing deep learning techniques for automatic caption generation and damage segmentation in car damage analysis. The aim is to enable precise identification and description of damages on vehicles, there by increasing speed and reducing the work load of experts in damage assessment. Traditionally, damage assessment relies solely on expert evaluations, which are costly and time-consuming. To address this issue, we propose utilizing data generation for training, automatic caption creation, and damage segmentation using an integrated framework. The researchers created a new dataset from CarDD, which is specifically designed for cardamage detection. This dataset includes labeled damages on vehicles, and the researchers have used it to feed into models for segmenting car parts and accurately labeling each part and damage category. Preliminary results from the model demonstrate its capability in automatic caption generation and damage segmentation for car damage analysis to be satisfactory. With these results, the model serves as an essential foundation for future development. This advancement aims not only to enhance performance in damage segmentation and caption generation but also to improve the model’s adaptability to a diversity of damages occurring on various surfaces and parts of vehicles. This will allow the system to be applied more broadly to different vehicle types and conditions of damage inthe future
คณะวิศวกรรมศาสตร์
-