Journal
BIORESOURCE TECHNOLOGY
Volume 102, Issue 5, Pages 4083-4090Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.biortech.2010.12.046
Keywords
Biogas; Anaerobic digestion; Monitoring; NIRS; Software sensor
Funding
- European Social Fund
- UK Biotechnology and Biological Sciences Research Council
Ask authors/readers for more resources
In this study two approaches to predict the total alkalinity (expressed as mg L-1 HCO3-) of an anaerobic digester are examined: firstly, software sensors based on multiple linear regression algorithms using data from pH, redox potential and electrical conductivity and secondly, near infrared reflectance spectroscopy (NIRS). Of the software sensors, the model using data from all three probes but a smaller dataset using total alkalinity values below 6000 mg L-1 HCO3- produced the best calibration model (R-2 = 0.76 and root mean square error of prediction (RMSEP) of 969 mg L-1 HCO3-). When validated with new data, the NIRS method produced the best model (R-2 = 0.87 RMSEP = 1230 mg L-1 HCO3-). The NIRS sensor correlated better with new data (R-2 = 0.54). In conclusion, this study has developed new and improved algorithms for monitoring total alkalinity within anaerobic digestion systems which will facilitate real-time optimisation of methane production. (C) 2010 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available