Generalized parameter estimation and calibration for biokinetic models using correlation and single variable optimisations: Application to sulfate reduction modelling in anaerobic digestion. (1st October 2017)

• Record Type: Journal Article
• Authors: Ahmed, Wasim; Rodríguez, Jorge
• Is Part Of: Water research. Volume 122(2017)
• Page Start: 407
• Page End: 418
• Publication Date: 2017-10-01
• Subjects: Anaerobic digestion -- Model parameter estimation -- Parameter calibration -- Sulfate reduction model -- ADM1 -- Parameter optimisation; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2017.05.067
• Languages: English
• ISSNs: 0043-1354

Abstract

Abstract: 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. Graphical abstract: Highlights: A general method for estimation and calibration biokinetic parameters is proposed. Mechanistic correlations followed by single parameter optimisations are proposed. A sensitivity-based hierarchical & sequential single parameter optimisation is used. The sequential single parameter optimisation is computationally inexpensive. The method was applied to an ADM1 extension for sulfate reduction with good results.