Volume 15, Issue 1 (5-2016)                   TB 2016, 15(1): 198-207 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Goli A, Talaiekhozani A. Comparison of Regression Model and Modified Monod kinetic Model to Predict the Removal of Formaldehyde in Trickling Biofilter. TB 2016; 15 (1) :198-207
URL: http://tbj.ssu.ac.ir/article-1-2118-en.html
Jami Institute of Technology, Esfahan, Iran , atalaei@jami.ac.ir
Abstract:   (3303 Views)

Abstract

Introduction: Formaldehyde is a toxic, mutagen and probably carcinogen compound that can be released to air by world different industries. The present study aimed to investigate the kinetic parameters of a trickling bio-filter as well as to present a simple regression model.

Methods: The data of previous studies on formaldehyde vapor removal by bio-trickling filter in a laboratory scale was used to determine rmax and Km. Moreover, the data were applied to develop a simple regression model.

Results: Formaldehyde removal efficiency in different input concentrations was predicted by both regression and kinetic models. All results were compared with actual data in the pilot study.

Conclusion: The results of the present study revealed that although regression model has a high precision, it only could predict the mean of bio-filter efficiency in formaldehyde removal. Kinetic model demonstrated some extent of error in predicting, though it has a good alignment with the actual data, and thus, the results of this model can approximately predict ups and downs of system navigation.

Full-Text [PDF 182 kb]   (1198 Downloads)    
Type of Study: Research | Subject: General
Received: 2016/05/17 | Accepted: 2016/05/17 | Published: 2016/05/17

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Tolooebehdasht

Designed & Developed by : Yektaweb