TY - JOUR
T1 - Generalized parameter estimation and calibration for biokinetic models using correlation and single variable optimisations
T2 - Application to sulfate reduction modelling in anaerobic digestion
AU - Ahmed, Wasim
AU - Rodríguez, Jorge
N1 - Funding Information:
This work has been supported by Masdar Institute of Science and Technology (grant number 14WAAA2) and the Government of Abu Dhabi. The authors would like to thank Dr. Ernesto L. Barrera (Study Center of Energy and Industrial Processes, Sancti Spiritus University, Cuba) for his response to our queries.
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017
Y1 - 2017
N2 - In this work, a generalized method for the estimation of biokinetic parameters in anaerobic digestion (AD) models is proposed. The method consists of a correlation-based approach to estimate specific groups of parameters mechanistically, followed by a sensitivity-based hierarchical and sequential single parameter optimisation (SHSSPO) calibration method for the remaining groups of parameters. The method was evaluated to estimate and calibrate the parameter values for sulfate reduction processes when included into the IWA Anaerobic Digestion Model No. 1 (ADM1) and simulations were compared with experimental data from literature. Under the proposed method, a large number of biokinetic parameters, namely biomass yields, maximum specific uptake rates, and half saturation constants, can first be estimated using mechanistic correlations. This achieves a significant reduction in the number of parameters to be fitted to data. For the remaining parameters, a method is proposed based on the overall sensitivity and degree of ubiquity of each parameter to establish a hierarchy in a sequential single parameter optimisation against the experimental data. This approach aims at eliminating the uncertainty on optimality (and therefore parameter identification) associated to multivariable parameter calibration problems. The method was applied to the sulfate reduction related parameters and led to the hydrogen sulfide inhibition parameters as the only ones requiring optimisation against experimental data. Comparison of the proposed SHSSPO performance with that of multi-dimensional parameter optimisation methods shows a superior performance in terms of overall error and computation times. Also, final simulation results led to model predictions of similar, if not better, quality than those achieved by multivariable parameter optimisation methods. The experimental variables optimized for included liquid effluent concentrations of sulfur species and volatile fatty acids as well as effluent methane gas flow. Overall, the proposed parameter estimation and calibration method provides a deterministic step-by-step approach to parameter estimation that decreases identifiability uncertainty at a very low computational effort. The results obtained suggest that the method could be generically applied with similar success to other biokinetic models frequently used in wastewater treatment.
AB - In this work, a generalized method for the estimation of biokinetic parameters in anaerobic digestion (AD) models is proposed. The method consists of a correlation-based approach to estimate specific groups of parameters mechanistically, followed by a sensitivity-based hierarchical and sequential single parameter optimisation (SHSSPO) calibration method for the remaining groups of parameters. The method was evaluated to estimate and calibrate the parameter values for sulfate reduction processes when included into the IWA Anaerobic Digestion Model No. 1 (ADM1) and simulations were compared with experimental data from literature. Under the proposed method, a large number of biokinetic parameters, namely biomass yields, maximum specific uptake rates, and half saturation constants, can first be estimated using mechanistic correlations. This achieves a significant reduction in the number of parameters to be fitted to data. For the remaining parameters, a method is proposed based on the overall sensitivity and degree of ubiquity of each parameter to establish a hierarchy in a sequential single parameter optimisation against the experimental data. This approach aims at eliminating the uncertainty on optimality (and therefore parameter identification) associated to multivariable parameter calibration problems. The method was applied to the sulfate reduction related parameters and led to the hydrogen sulfide inhibition parameters as the only ones requiring optimisation against experimental data. Comparison of the proposed SHSSPO performance with that of multi-dimensional parameter optimisation methods shows a superior performance in terms of overall error and computation times. Also, final simulation results led to model predictions of similar, if not better, quality than those achieved by multivariable parameter optimisation methods. The experimental variables optimized for included liquid effluent concentrations of sulfur species and volatile fatty acids as well as effluent methane gas flow. Overall, the proposed parameter estimation and calibration method provides a deterministic step-by-step approach to parameter estimation that decreases identifiability uncertainty at a very low computational effort. The results obtained suggest that the method could be generically applied with similar success to other biokinetic models frequently used in wastewater treatment.
KW - ADM1
KW - Anaerobic digestion
KW - Model parameter estimation
KW - Parameter calibration
KW - Parameter optimisation
KW - Sulfate reduction model
UR - http://www.scopus.com/inward/record.url?scp=85020703484&partnerID=8YFLogxK
U2 - 10.1016/j.watres.2017.05.067
DO - 10.1016/j.watres.2017.05.067
M3 - Article
C2 - 28622633
AN - SCOPUS:85020703484
SN - 0043-1354
VL - 122
SP - 407
EP - 418
JO - Water Research
JF - Water Research
ER -