Development of explainable AI-based predictive models for bubbling fluidised bed gasification process

© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).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, S Hapley Additive ex Planations (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) of0.0597 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.Peer reviewe

Application of Machine Learning Models with Numerical Simulations of an Experimental Microwave Induced Plasma Gasification Reactor

This thesis aims to contribute to the future development of this technology by providing an in-depth literature review of how this technology physically operates and can be numerically modeled. Additionally, this thesis reviews literature of machine learning models that have been applied to gasification to make accurate predictions regarding the system. Finally, this thesis provides a framework of how to numerically model an experimental plasma gasification reactor in order to inform a variety of machine learning models

Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach

The effect of different bed materials was included a as new input into an artificial neural network model to predict the gas composition (CO2, CO, CH4 and H2) and gas yield of a biomass gasification process in a bubbling fluidized bed. Feed and cascade forward back propagation networks with one and two hidden layers and with Levenberg-Marquardt and Bayesian Regulation learning algorithms were employed for the training of the networks. A high number of network topologies were simulated to determine the best configuration. It was observed that the developed models are able to predict the CO2, CO, CH4, H2 and gas yield with good accuracy (R2 > 0.94 and MSE < 1.7 × 10−3). The results obtained indicate that this approach is a powerful tool to help in the efficient design, operation and control of bubbling fluidized bed gasifiers working with different operating conditions, including the effect of the bed material

Thermodynamic evaluation of the gasification of municipal solid waste

The dependency on energy use is unavoidable in modern civilization. The burning of fossil fuels for energy use is regarded as one of the human activities that has a harmful environmental impact. Waste to energy is slowly becoming an evident argument that energy can be obtained from waste at a level that is enough to meet energy demands. Waste is viewed as a renewable source of energy and can lower emissions from the greenhouse gas (GHG) and mitigate climate change. The exploitation of municipal solid waste (MSW) can be implemented using various routes, either through thermal or biological conversion. The thermal conversion can be achieved through combustion, gasification, or pyrolysis. This study aimed to evaluate the gasification of municipal solid waste. The investigation focused on the effects the selected operating parameters have on the syngas composition, H2/CO ratio, and calorific value. The selection of the modelling approach focused on the problem statement. It was necessary to use a model that did not have a lot of limitations or relied on the geometry of the gasifier. A mathematical model that could analyse the selected operating parameters of the gasification process was utilized. A step-by-step procedure of the thermodynamic equilibrium model was implemented using MATLAB. The model was validated by comparing the predicted results of this study and empirical data in published literature. The results showed that operating parameters affected the amount of syngas quality, calorific value, and H2/CO ratio. The amount of carbon monoxide and nitrogen reduced with an increase in moisture content, and the amount of carbon dioxide increased with increased moisture content. A small amount of methane was recorded, with increased moisture content. Enhanced temperature brought about increased hydrogen while the amount of nitrogen remained constant. With high temperature, carbon dioxide composition reduced, and just over 1% of methane was recorded. The increased (ER) from 0.2 to 0.6 showed that ER has a notable impact on nitrogen. A sharp increase in nitrogen was noted when the ER increased while the amount of hydrogen and carbon monoxide decreased. Results showed acceptable agreement between the modelled data from this investigation and the experimental values reported in the literature. The overall conclusion is that the thermodynamic model gives accurate prediction results of the gasification process. Additionally, when the investigated operating parameters were adjusted, syngas composition, H2/CO ratio and calorific value were all affected (they either increased or reduced). Furthermore, it is concluded that the ER ratio is the most influential parameter in the gasification process

Environmental friendly fluidized bed combustion of solid fuels: a review about local scale modeling of char heterogeneous combustion

Purpose: Fluidized bed combustion is currently intensively developed throughout the world to produce energy from several types of solid fuels, while significantly reducing pollutant emissions with respect to conventional combustion units. Accurate models must be formulated at both bed and particle levels to operate efficiently such units, since local phenomena such as particle temperature and combustion rate are crucial aspects for process improvement and control. In this sense, this article proposes a classification of local scale models to represent the evolution of char heterogeneous combustion of any carbonaceous particles. Methods: Existing models are described and classified based on the characteristics of the governing equations, the thermal behavior of the gas and solid phases and the description of both the burning particle and the surrounding gas, under a heterogeneous or pseudo-continuous assumption. Criteria for choosing one model instead of others are also considered, depending on the case. The so-called Intrinsic Reactivity Models are described in detail for evaluating the pertinence of their simulated results. The use of CFD to build a simulation scheme of the solid combustion process at local scale is also presented and discussed. Results: A complete description of the solid fuel burning process is given, along with useful information concerning the evolution of different variables, such as particle internal temperature that governs the reaction rate and gas composition. Conclusions: This comparative analysis gives a strong basis to select the appropriate modeling approach. Finally, recommendations are proposed for model application and future development.Fil: Mazza, German Delfor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; ArgentinaFil: Soria, Jose Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; ArgentinaFil: Gauthier, Daniel. Centre National de la Recherche Scientifique; FranciaFil: Reyes Urrutia, Ramón Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; ArgentinaFil: Zambon, Mariana Teresa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; ArgentinaFil: Flamant, Gilles. Centre National de la Recherche Scientifique; Franci

Hydrogen production from plastic waste: A comprehensive simulation and machine learning study

Gasification, a highly efficient method, is under extensive investigation due to its potential to convert biomass and plastic waste into eco-friendly energy sources and valuable fuels. Nevertheless, there exists a gap in comprehension regarding the integrated thermochemical process of polystyrene (PS) and polypropylene (PP) and its capability to produce hydrogen (H2) fuel. In this study a comprehensive process simulation using a quasi-equilibrium approach based on minimizing Gibbs free energy has been introduced. To enhance H2 content, a water-gas shift (WGS) reactor and a pressure swing adsorption (PSA) unit were integrated for effective H2 separation, increasing H2 production to 27.81 kg/h. To investigate the operating conditions on the process the effects of three key variables in a gasification reactor namely gasification temperature, feedstock flow rate and gasification pressure have been explored using sensitivity analysis. Furthermore, several machine learning models have been utilized to discover and optimize maximum capacity of the process for H2 production. The sensitivity analysis reveals that elevating the gasification temperature from 500 °C to 1200 °C results in higher production of H2 up to 23 % and carbon monoxide (CO). However, generating H2 above 900 °C does not lead to a significant upturn in process capacity. Conversely, an increase in pressure within the gasification reactor is shown to decrease the system capacity for generating both H2 and CO. Moreover, increasing the mass flow rate of the gasifying agent to 250 kg/h in the gasification reactor has shown to be merely productive in process capacity for H2 generation, almost a 5 % increase. Regarding pressure, the hydrogen yield decreases from 22.64 % to 17.4 % with an increase in pressure from 1 to 10 bar. It has been also revealed that gasification temperature has more predominant effect on Cold gas efficiency (CGE) compared to gasification pressure and Highest CGE Has been shown by PP at 1200 °C. Among the various machine learning models, Random Forest (RF) model demonstrates robust performance, achieving R2 values exceeding 0.99

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier

A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well

Biomass Gasification and Applied Intelligent Retrieval in Modeling

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies