Assessment of Ex-Vitro Anaerobic Digestion Kinetics of Crop Residues Through First Order Exponential Models: Effect of LAG Phase Period and Curve Factor

Kinetic studies of AD (Anaerobic Digestion) process are useful to predict the performance of digesters and design appropriate digesters and also helpful in understanding inhibitory mechanisms of biodegradation. The aim of this study was to assess the anaerobic kinetics of crop residues digestion wit...

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Bibliographic Details
Published in:Mehran University Research Journal of Engineering and Technology Vol. 32; no. 4; pp. 649 - 660
Main Authors: ABDUL RAZAQUE SAHITO, RASOOL BUX MAHAR, KHAN MUHAMMAD BROHI
Format: Journal Article
Language:English
Published: Mehran University of Engineering and Technology 01-10-2013
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Summary:Kinetic studies of AD (Anaerobic Digestion) process are useful to predict the performance of digesters and design appropriate digesters and also helpful in understanding inhibitory mechanisms of biodegradation. The aim of this study was to assess the anaerobic kinetics of crop residues digestion with buffalo dung. Seven crop residues namely, bagasse, banana plant waste, canola straw, cotton stalks, rice straw, sugarcane trash and wheat straw were selected from the field and were analyzed on MC (Moisture Contents), TS (Total Solids) and VS (Volatile Solids) with standard methods. In present study, three first order exponential models namely exponential model, exponential lag phase model and exponential curve factor model were used to assess the kinetics of the AD process of crop residues and the effect of lag phase and curve factor was analyzed based on statistical hypothesis testing and on information theory. Assessment of kinetics of the AD of crop residues and buffalo dung follows the first order kinetics. Out of the three models, the simple exponential model was the poorest model, while the first order exponential curve factor model is the best fit model. In addition to statistical hypothesis testing, the exponential curve factor model has least value of AIC (Akaike's Information Criterion) and can generate methane production data more accurately. Furthermore, there is an inverse linear relationship between the lag phase period and the curve factor.
ISSN:0254-7821
2413-7219