Browsing by Author "Liu, Yanxin"

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    A scheme for anaerobic digestion modelling and ADM1 model calibration
    (Associazione Italiana Di Ingegneria Chimica (AIDIC), 2022-11-30) Liu, Yanxin; Jiang, Ying; Bortone, Immacolata
    Anaerobic digestion (AD) is a technology that produces biogas, also known as renewable natural gas, from organic waste materials under the activity of anaerobic microorganisms. In recent years, an increasing attention on energy produced from renewable resources has led to the need and development of tools helping with improving the process performance and design of AD, such as the Anaerobic Digestion Model No.1 (ADM1). ADM1 is a process-based model that can predict the biogas yield and identify potential prohibitions in the AD process from the properties of the feedstock and inoculum. Initial values of state variables and model parameters need to be calibrated when applying ADM1 to a particular feedstock. In this study, an ADM1 model using differential algebraic equations (DAE) system, called DAE ADM1, was developed. Specifically, the influence of the initial values of AD process state variables on the calibration of model stoichiometric and kinetic parameters were investigated, by comparing them with literature data, by highlighting their high impact on the model setup.
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    Shortening the standard testing time for residual biogas potential (RBP) tests using biogas yield models and substrate physicochemical characteristics
    (MDPI, 2023-02-01) Liu, Yanxin; Guo, Weisi; Longhurst, Philip J.; Jiang, Ying
    The residual biogas potential (RBP) test is a procedure to ensure the anaerobic digestion process performance and digestate stability. Standard protocols for RBP require a significant time for sample preparation, characterisation and testing of the rig setup followed by batch experiments of a minimum of 28 days. To reduce the experimental time to obtain the RBP result, four biogas kinetic models were evaluated for their strength of fit for biogas production data from RBP tests. It was found that the pseudo-parallel first-order model and the first-order autoregressive (AR (1)) model provide a high strength of fit and can predict the RBP result with good accuracy (absolute percentage errors < 10%) using experimental biogas production data of 15 days. Multivariate regression with decision trees (DTs) was adopted in this study to predict model parameters for the AR (1) model from substrate physicochemical parameters. The mean absolute percentage error (MAPE) of the predicted AR (1) model coefficients, the constants and the RBP test results at day 28 across DTs with 20 training set samples are 4.76%, 72.04% and 52.13%, respectively. Using five additional data points to perform the leave-one-out cross-validation method, the MAPEs decreased to 4.31%, 59.29% and 45.62%. This indicates that the prediction accuracy of DTs can be further improved with a larger training dataset. A Gaussian Process Regressor was guided by the DT-predicted AR (1) model to provide probability distribution information for the biogas yield prediction.