TY - JOUR

T1 - Evaluation of near infrared spectroscopy and software sensor methods for determination of total alkalinity in anaerobic digesters

AU - Ward, Alastair J

AU - Hobbs, Philip J

AU - Holliman, Peter J

AU - Jones, Davey L.

N1 - Copyright © 2010 Elsevier Ltd. All rights reserved.

PY - 2011/3/1

Y1 - 2011/3/1

N2 - In this study two approaches to predict the total alkalinity (expressed as mg L(-1)HCO(3)(-)) 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)HCO(3)(-) produced the best calibration model (R(2)=0.76 and root mean square error of prediction (RMSEP) of 969 mg L(-1)HCO(3)(-)). When validated with new data, the NIRS method produced the best model (R(2)=0.87 RMSEP=1230 mg L(-1)HCO(3)(-)). 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.

AB - In this study two approaches to predict the total alkalinity (expressed as mg L(-1)HCO(3)(-)) 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)HCO(3)(-) produced the best calibration model (R(2)=0.76 and root mean square error of prediction (RMSEP) of 969 mg L(-1)HCO(3)(-)). When validated with new data, the NIRS method produced the best model (R(2)=0.87 RMSEP=1230 mg L(-1)HCO(3)(-)). 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.

KW - Algorithms

KW - Bacteria, Anaerobic/metabolism

KW - Bicarbonates/analysis

KW - Hydrogen-Ion Concentration

KW - Linear Models

KW - Methane/biosynthesis

KW - Models, Theoretical

KW - Refuse Disposal/methods

KW - Software

KW - Spectrophotometry, Infrared/methods

U2 - 10.1016/j.biortech.2010.12.046

DO - 10.1016/j.biortech.2010.12.046

M3 - Article

C2 - 21227685

VL - 102

SP - 4083

EP - 4090

JO - Bioresource technology

JF - Bioresource technology

SN - 0960-8524

IS - 5

ER -