Browsing by Author "Liu, Yanxin"

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    Facilitating the predictions of batch and continuous anaerobic digestion processes performance with statistical tools
    (Cranfield University, 2024-07) Liu, Yanxin; Jiang, Ying; Longhurst, Philip J.; Guo, Weisi
    Anaerobic digestion (AD) is a promising technology for waste management and renewable energy production. Determining the biomethane potential (BMP) of a material is crucial when considering it as feedstock for the digester. The practical BMP of a material and its degradation kinetics can be derived from the batch BMP test, typically taking at least 30 days, which can be onerous to the industrial operator. Many studies have attempted to predict BMP test results by building regressions between various feedstock physiochemical characteristics and BMP test result. However, these methods primarily predict the ultimate biogas yield of the BMP test and are unable to capture the reaction kinetics. Part I of this study proposed a method to predict the BMP test result of a material, not only the ultimate biogas yield but also the degradation kinetics, which was achieved by discovering a model that describes the biogas production well and then inferring parameters of this model from feedstock’s physiochemical characteristics. The machine learning (ML) model, Decision Tree, was adopted to predict the parameters of the time series model, first-order Autoregressive Model, from the characteristics including total solids, volatile solids, total volatile fatty acid, total ammonia nitrogen, chemical oxygen demand, alkalinity, elemental composition, pH, heavy metals, etc. The DT trained and tested by leave-one-out cross validation with 25 BMP test data had a mean absolute percentage error (MAPE) of 45.63% on the BMP test results, showing unsatisfactory prediction accuracy and unreliable feature importance analysis results. To obtain sufficient data for ML model training and avoid the consistency issues with BMP test data collected from diverse sources, a data augmentation method employing response surface design was proposed. With sufficient data for model training, eXtreme Gradient Boosting (XGBoost) models with three important features determined via feature importance analysis could predict biogas production model parameters with R²values above 0.99 on the test set. Despite the strong regression capabilities of ML models compared to simple statistical model, their explainability remains a challenge. The current popular methods for ML model interpretation focus on feature importance analysis. In this study, Meijei G-functions were used to interpret the predictions of the XGBoost model mathematically to enhance the accessibility and transparency of the black- box ML model to domain experts as a tool for material realistic BMP prediction. The general predictability of the mathematical metamodel was tested using 13 BMP test data sourced from the literature, all within the applicability range of the trained ML model, resulting in a mean absolute error of 38.074 mL CH ₄/g volatile solid added and MAPE of 15.424%. Besides the BMP of a material, operational parameters of the digester are critical to the performance of a continuous AD process. The application of Anaerobic Digestion Model No.1 (ADM1) for continuous AD simulation, which assists in decision-making in the AD industry by predicting digester performance under various operational schemes, is often hindered by its calibration difficulties, especially with limited data. Part II of this study presents a Bayesian inference-based framework to reliably calibrate ADM1 using only initial-stage digester data. A sequence of sensitivity analysis (SA) was applied to identify the most influential kinetic parameters and initial values to be calibrated. SA results revealed that steady-state biogas production was collaboratively influenced by the disintegration rate, hydrolysis rates, and initial concentrations of acetate degraders, cations, and anions. In contrast, Total Ammonia Nitrogen and pH of digestate were predominantly influenced by initial values of cations and anions. These findings challenge the common practice in ADM1 studies of only calibrating kinetic and stoichiometric parameters. Then, using biogas production and digestate data from less than two hydraulic retention times and informative prior distributions determined from domain knowledge, seven the most influential uncertain inputs were calibrated. The calibrated model predicted the steady state performance satisfactorily. The 95% credible intervals of the calibrated model encompassed 66.047% of the 10- day moving average trendline of the daily biogas flow data and all of the steady- state digestate pH and total chemical oxygen demand data.
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    ItemOpen Access
    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.