綠足跡運用之油槽智慧儲存管理

摘要在全球暖化急劇加速的21世紀裡，各國政府與民間以綠足跡為概念之節能減碳與環保回收再利用為主要研究課題，若能減緩資源耗損，並提高資源使用率，則對未來後世子孫有著建設性協助。而人工智慧是20世紀末的重大科學研究成就，運用機器學習方式解決複雜的人類問題，本研究主要以事業廢棄物清除處理廠之油槽儲存智慧管理為例，經歷史數據進行線性迴歸分析後，協助廠商以數位智慧化管理油槽廢油進出及儲存，提供做產業數位化升級與人工成本樽節並作為廠商擴廠之參考，在回收過程中，運用綠足跡觀念導入，算出廢油槽及淨油槽使用輸出率及年限，協助廠商做有利之商業規劃。根據107年至110年A級與B級油品進出油狀況，做線性預估，其結果如下：得到以下結論：一、經由線性回歸的成長量為成長基礎，預估A級廢油槽每個月倉儲量，將於113年1月將達180.66公噸需要增加1個大型儲存槽， 117年1月達366.30公噸，需要增加第二個大型儲存槽。二、經由線性回歸的成長量為成長基礎，預估每個月的倉儲量，B級廢油槽將於111年3月達187.57公噸，需要增加1個大型儲存槽， 114年5月達273.83公噸需要增加第二個大型儲存槽。三、目前A級及B級淨油(此指出油)共槽，若依照目前使用情況與機台運作情況，淨油槽儲存容量391.5公噸。其淨油槽將於119年3月達490.63公噸，需要增加1個大型淨油儲存槽。四、從輸出率發現A級廢油產生之固態爐渣僅4%而B級廢油則42%之固態廢棄物，皆為可計算之產出，目前基於綠足跡回收環保理念，對於固態廢棄物做量化整理後，亦可再行利用。

關鍵字

綠足跡；油槽；智慧儲存

並列摘要

ABSTRACT The 21st Century is marred by accelerating global warming. The concept of green footprint which embodies energy conservation, carbon reduction as well as recycling employed by governments and private sectors around globe is the main focus of this study. If energy consumption were to be alleviated, with resource usage increased, future generations would benefit from the constructive support. Artificial intelligence is a major scientific research achievement of late 20th Century, as use of machine learning has solved many complex human problems. This study mainly utilizes smart management of oil tank storage in industrial/commercial waste disposal plants as example. After linear regression analysis of historical data, businesses are assisted with digitized smart management of tank waste oil intake, outflow and storage, serving as reference for industry digitization, manual labor cost reduction as well as expansion. During the recycling process, the concept of green footprint is introduced, calculating usage, output rate and service life of waste oil tanks and purified oil tanks, aiding businesses to make better informed commercial planning. According to intake and outflow of A grade and B grade oils from 2018~2021, after linear estimates, results are as follows: I. With post linear regression growth volume as growth basis, estimated monthly storage volume of A grade waste oil tank shall reach 180.66 tonnes by January, 2024, requiring addition of 1 large storage tank; 366.30 tonnes by January, 2028, requiring addition of a second large storage tank. II. With post linear regression growth volume as growth basis, it is estimated that monthly storage volume of B grade waste oil stage tank shall reach 187.57 tonnes by March, 2022, requiring addition of 1 large storage tank; reaching 273.83 tonnes by May, 2025, requiring addition of a second large storage tank. III. Both A and B grade purified oil (i.e. outflow) share same storage tank. Based on current usage and operating status, the storage volume is at 391.5 tonnes. Said tank will reach 490.63 tonnes by March, 2030, requiring an additional 1 large purified oil storage tank. IV IV. Output rate indicates that A grade waste oil yields only 4% solid slags; whereas B grade waste oil yields 42% in solid waste, both of which are calculable yields. Currently with the recycling concept of green footprint, once the aforementioned solid wastes are quantified and treated, they will become reusable.