Browsing by Author "Xu, Zhehua"
Now showing 1 - 1 of 1
Results Per Page
Sort Options
- ItemADM1 Parameter Calibration Method based on partial least squares regression framework for industrial-scale anaerobic digestion modelling(Stellenbosch : Stellenbosch University, 2019-12) Xu, Zhehua; Burger, A. J.; Louw, Tobias M.; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.ENGLISH ABSTRACT: Anaerobic Digestion Model 1 (ADM1) is the mainstay modelling tool for Anaerobic Digestion research and development. Its growing popularity is attributed to its sophisticated yet expandable structure. Not only does ADM1 encompass a broad range of biochemical, physicochemical and inhibition reactions, it provides the modeller a structured framework to add or remove reactions per application requirements. Two major challenges that ADM1 faces are the difficulty in translating common quality indicators into ADM1’s 26 state variables, and the complication with calibrating a large number of model parameters – 58 by default. There is currently no consensus with regards to the parameter calibration approach. Researchers utilise various sensitivity analysis techniques to identify sensitive parameters, but the selection of parameters to be calibrated relies largely on the modeller’s discretion. In some cases, decisions are simply made based on prior or expert knowledge. Since the installation, operation and maintenance of advanced instrumentation are often expensive, most industrial digesters are inadequately monitored and thus intentionally over-designed. A model that can be used on-site with acceptable accuracy could serve as a soft sensor to forecast inhibition risks and automate preventive actions. Therefore, this study aimed to develop a standardised way to calibrate parameters when optimising ADM1 models built for industrial-scale digesters. The proposed method, Partial Least Squares (PLS) Method, consists of four steps. In Step 1, a series of Monte Carlo simulations is carried out. For each Monte Carlo run, ADM1 is executed with all its model parameters sampled from independent probability distributions. These probability distributions were obtained by conducting a literature survey across 62 publications and all published parameters compiled into a domain which represents the uncertainty range of each parameter. In Step 2, a multivariate regression technique called PLS Regression (PLSR) is applied to the Monte Carlo results. The motives for employing PLSR are to reduce parameter dimensionality and to identify the underlying relationships between the model parameters and the model outputs. In Step 3, these relationships, which are mathematically described as PLS weights, loadings and latent variables, are utilised to guide parameter calibration. Lastly, the calibrated parameter set is validated against unseen data. This method successfully improved, in the absence of any modeller’s bias, the overall accuracy of a model based on data from an industrial-scale digester. The model is tasked to fit six typical plant measurements: Volatile Fatty Acids (VFA), ammonia, Volatile Suspended Solids (VSS), pH, methane gas flow & carbon dioxide gas flow. A configuration consisting of at least 500 Monte Carlo runs and two latent variables is required to produce a reasonably accurate fit. Although the use of more latent variables could enable PLSR to capture interactions of lesser weighted output variables, the model becomes increasingly prone to overfitting. However, it is envisaged that more latent variables would be necessary if more outputs are modelled. It is recommended to start the PLSR algorithm with one latent variable and only introduce more if necessary. Different parameter calibration methods produce different model outcomes. The PLS Method was benchmarked against two other methods, namely the Group Method and the “Brute Force” Method. In the former method, kinetic parameters were grouped into the three groups of sensitivities (High, Medium, Low) as suggested in the ADM1 Scientific and Technical Report. The three groups are then calibrated sequentially in order of decreasing sensitivity. The “Brute Force” Method involved calibrating all 58 parameters without any particular sequence, prioritisation or expert inputs. Lower and upper limits are, however, set as per the minimum and maximum values identified from the literature. Besides proving to be a suitable method for industrial-scale digester modelling, the PLS Method was found to exhibit several unique traits: • It is the only method that did not show signs of overfitting. • It is the only method that concluded the model optimisation with all calibrated parameter values within the surveyed minimum and maximum range. • It converges on the objective function 30-60% faster than the Group Method and 14 times quicker than the “Brute Force” Method The success is attributed to the fundamentals of PLS regression. Unlike other regression methods where parameters are adjusted independently, PLS enables parameters to be manipulated collectively in a manner that ensures maximum impact on the outputs while considering collinearities among the parameters. This guided approach effectively mitigates the so-called “curse of dimensionality” and, potentially, overfitting and thereby speeds up the calibration process.