Pandey, Daya and Raza, Haider and Bhattacharyya, Saugat (2023) Development of explainable AI based predictive models for bubbling fluidised bed gasification process. Fuel, 351. p. 128971. DOI https://doi.org/10.1016/j.fuel.2023.128971
Pandey, Daya and Raza, Haider and Bhattacharyya, Saugat (2023) Development of explainable AI based predictive models for bubbling fluidised bed gasification process. Fuel, 351. p. 128971. DOI https://doi.org/10.1016/j.fuel.2023.128971
Pandey, Daya and Raza, Haider and Bhattacharyya, Saugat (2023) Development of explainable AI based predictive models for bubbling fluidised bed gasification process. Fuel, 351. p. 128971. DOI https://doi.org/10.1016/j.fuel.2023.128971
Abstract
In this study, seven different types of regression-based predictive modelling techniques are used to predict the product gas composition (H2, CO, CO2, CH4) and gas yield (GY) during the gasification of biomass in a fluidised bed reactor. The performance of different regression-based models is compared with the gradient boosting model (GB) to show the relative merits and demerits of the technique. Additionally, SHapley Additive exPlanations (SHAP)-based explainable artificial intelligence (XAI) method was utilised to explain individual predictions. This study demonstrates that the prediction performance of the GB algorithm was the best among other regression-based models i.e. Linear Regression (LR), Multilayer perception (MLP), Ridge Regression (RR), Least-angle regression (LARS), Random Forest (RF) and Bagging (BAG). It was found that at learning rate (lr) 0.01 and number of boosting stages (est) 1000 yielded the best result with an average root mean squared error (RMSE) of 0.0597 for all outputs. The outcome of this study indicates that XAI-based methodology can be used as a viable alternative modelling paradigm in predicting the performance of a fluidised bed gasifier for an informed decision-making process.
Item Type: | Article |
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Uncontrolled Keywords: | Gasification; Bubbling fluidised bed; Machine learning; Gradient boosting; Decision tree regression |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 05 Sep 2023 11:36 |
Last Modified: | 16 May 2024 21:53 |
URI: | http://repository.essex.ac.uk/id/eprint/35759 |
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