A novel modeling strategy for the prediction on the concentration of H2 and CH4 in raw coke oven gas

Lei, Yang; Chen, Yuming; Chen, Jinghai; Li, Xinyan; Wu, Xiaoqin; Chen, Yuqiu

doi:10.1016/j.energy.2023.127126

Cited by 14 publications

(2 citation statements)

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“…Azarpour et al [21] proposed a hybrid model combining a first-principle model and artificial neural network, with the aim of predicting the kinetic constant of deactivation of catalysts in a fixed bed. Lei Y. et al [22] present a hybrid model proposal using four machine learning models (Artificial Neural Networks, Random Forest, XGBoost and LightGBM) for the prediction of hydrogen and methane in raw coke oven gas, presenting coefficients of determination equal to 0.99952 and 0.99964 for the prediction of hydrogen and methane concentrations, respectively, for the best model (LightGBM). Shahbaz et al [23] constructed an ANN for the prediction of the palm kernel bark steam gasification process using CaO as adsorbent and coal ash as a catalyst.…”

Section: The Process Of Gasification Of Biomass In Supercritical Watermentioning

confidence: 99%

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Hybrid Modeling of Machine Learning and Phenomenological Model for Predicting the Biomass Gasification Process in Supercritical Water for Hydrogen Production

Santos

Zelioli

Mariano

2023

Eng

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Process monitoring and forecasting are essential to ensure the efficiency of industrial processes. Although it is possible to model processes using phenomenological approaches, these are not always easy to apply and generalize due to the complexity of the processes and the high number of unknown parameters. This work aims to present a hybrid modeling architecture that combines a phenomenological model with machine learning models. The proposal is to enable the use of simplified phenomenological models to explain the basic principles behind a phenomenon. Next, the data-oriented model corrects deviations from the simplified model predictions. The research hypothesis consists of showing the benefits of integrating prior knowledge of chemical engineering in simplifying data-based models, enhancing their generalization and improving their interpretability. The gasification process of lignin biomass with supercritical water was used as a case study for this methodology and the variable to be observed was the production of hydrogen. The real experimental data of this process were augmented using Gibbs energy minimization with the Peng–Robinson equation of state, thus generating a more voluminous database that was considered as real process data. The ideal gas model was used as a simplified model, producing significant deviations in predictions (relative deviations greater than 20%). Deviations (∆H2 = H2real−H2predict) were used as the target variable for the machine learning model. Linear regression models (LASSO and simple linear regression) were used to predict ∆H2 and this variable was added to the simplified forecast model. This consisted of the hybrid prediction of the resulting hydrogen formation (H2predict). Among the verified models, the simple linear regression adjusted better to the values of ∆H2 (R2 = 0.985) and MAE smaller than 0.1. Thus, the proposed hybrid architecture allowed for the prediction of the formation of hydrogen during the gasification process of lignin biomass, despite the thermodynamic limitations of the ideal gas model. Hybridization proved to be robust as a process monitoring tool, providing the abstraction of non-idealities of industrial processes through simple, data-oriented models, without losing predictive power. The objective of the work was fulfilled, presenting a new possibility for the monitoring of real industrial processes.

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Section: The Process Of Gasification Of Biomass In Supercritical Watermentioning

confidence: 99%

“…The chemical potential of the liquid phase components is calculated as a function of the saturation pressure, and the Antoine equation (Equation (22)) will be used to calculate this property.…”

Section: Phenomenological Modeling Of the Processmentioning

confidence: 99%