The Automatic Classification and Scheduling System for Hazardous Industrial Waste Processing Factory

• Main Authors: Pen-Chun Liao, 廖珮君
• Other Authors: 廖宜恩
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/m4mqh4

碩士 === 國立中興大學 === 資訊科學與工程學系所 === 107 === Business waste is any waste that generated by business activities but not by the life of employees. Business waste includes hazardous industrial waste and general business waste. According to the law, the business waste must be processed following the regulatory requirements and be dealt with by a qualified waste treatment factory. In order to achieve the maximum operational efficiency, how to process the most volumes of waste within a certain time period is the goal of factories to achieve. The steps of processing waste include weighing, inspection, laboratory testing, classifying, scheduling, and processing. While most of the steps are dealt with by manual paperwork, the drawbacks, such as time consuming, difficulty of data integration, relying on experience, have occurred. This paper proposes a system automation model for collecting and integrating data of waste, as well as classifying and scheduling of processing line. The method of classification is basic on historical processing rules and nozzle characteristics. The algorithm of scheduling considers the congestion of processing line and the possibility of using an alternative nozzle. The purposes of this paper are finding a solution for saving processing time, avoiding repeating jobs, improving the problems of congestion or idle of lines, and so on. Therefore, improving performance of treatment can be achieved finally. The experimental results show that data collection and integration can be done automatically and quickly using the proposed model. Comparing to manual paperwork, which spends 3 hours one day in average for classifying and scheduling, the proposed system reduces processing time, which required only 60 minutes. Also, the operation rates of nozzles increased to 10% in average which effectively reduces the cost of temporary storage of waste.