English  |  正體中文  |  简体中文  |  Items with full text/Total items : 26994/38795
Visitors : 2391780      Online Users : 71
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40119

Title: Prediction of the influential operational compost parameters for monitoring composting process
Authors: Chitsan Lin;Chih-Chiang Wei;Chia-Cheng Tsai
Contributors: 國立臺灣海洋大學:海洋環境資訊學系
Date: 2016-07
Issue Date: 2017-01-16
Publisher: Environmental Engineering Science
Abstract: Abstract: In this study, two influential parameters were selected, pH and composting temperature, to monitor the composting process, and thus a pH prediction model and composting-temperature prediction model were constructed. We used artificial neural network-based multilayer perceptron (ANN-based MLP) to develop two prediction models. To compare the efficiency achieved using ANNs, traditional multiple-linear regression (MLR) was selected as a benchmark. Subsequently, we presented a composting flowchart to simulate real-time composting processes. Test data were collected from 13 experiments that were conducted in an open-air facility. We measured eight attributes: days being composted, pH, composting temperature, moisture content, food waste, mature compost, sawdust, and soil. Comparison of performance of 1- with 3-day-ahead prediction models revealed that the 1-day-ahead forecasts yielded superior values in terms of relative mean absolute error, relative root mean squared error, coefficient of correlation, and coefficient of efficiency than did the 2- and 3-day-ahead forecasts in both the ANN and MLR models. In predicting the maturity of food wastes, absolute time errors of degree of degradation were 0.67 and 1.22 days, respectively, when ANN and MLR models were used in 1-day-ahead prediction, which demonstrates that prediction was more accurate using ANN than using MLR. Thus, ANN-based prediction models can be regarded as being reliable, and proposed composting real-time forecast models can be effectively used in monitoring composting processes.
Relation: 33(7)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40119
Appears in Collections:[海洋環境資訊系] 期刊論文

Files in This Item:

File Description SizeFormat

All items in NTOUR are protected by copyright, with all rights reserved.


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback