Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach

Bahadar, Ali; Kanthasamy, Ramesh; Sait, Hani Hussain; Zwawi, Mohammed; Algarni, Mohammed; Ayodele, Bamidele Victor; Cheng, Chin Kui; Wei, Lim Jun

doi:10.1016/j.chemosphere.2021.132052

Cited by 42 publications

(6 citation statements)

References 37 publications

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“…Yoon and Moon [20] reported that the GPR model accurately models energy consumption in a commercial building. The modeling of hydrogen-rich syngas production from co-gasification agriculture waste using SVM and GPR has been reported by Bahadar et al [19]. The hydrogen-rich syngas was accurately predicted by the SVM and GPR as indicated by the high R 2 (>0.9), indicating a strong correlation between the actual and the predicted values.…”

Section: Sources Technological Process Hydrogen Produced Referencementioning

confidence: 67%

“…The SVM modeling resulted in 95% diagnosis accuracy at constant diagnosis time. SVM has been reported to be robust in the predictive modeling of solar thermal energy systems [19]. However, the authors observed that the SVM predictive accuracy was less than other machine learning algorithms, such as random forest.…”

Section: Sources Technological Process Hydrogen Produced Referencementioning

confidence: 99%

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Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent

et al. 2022

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Organic-rich substrates from organic waste effluents are ideal sources for hydrogen production based on the circular economy concept. In this study, a data-driven approach was employed in modeling hydrogen production from palm oil mill effluents and activated sludge waste. Seven models built on support vector machine (SVM) and Gaussian process regression (GPR) were employed for the modeling of the hydrogen production from the waste sources. The SVM was incorporated with linear kernel function (LSVM), quadratic kernel function (QSVM), cubic kernel function (CSVM), and Gaussian fine kernel function (GFSVM). While the GPR was incorporated with the rotational quadratic kernel function (RQGPR), squared exponential kernel function (SEGPR), and exponential kernel function (EGPR). The model performance revealed that the SVM-based models did not show impressive performance in modeling the hydrogen production from the palm oil mill effluent, as indicated by the R2 of −0.01, 0.150, and 0.143 for LSVM, QSVM, and CSVM, respectively. Similarly, the SVM-based models did not perform well in modeling the hydrogen production from activated sludge, as evidenced by R2 values of 0.040, 0.190, and 0.340 for LSVM, QSVM, and CSVM, respectively. On the contrary, the SEGPR, RQGPR, SEGPR, and EGPR models displayed outstanding performance in modeling the prediction of hydrogen production from both oil palm mill effluent and activated sludge, with over 90% of the datasets explaining the variation in the model output. With the R2 > 0.9, the predicted hydrogen production was consistent with the SEGPR, RQGPR, SEGPR, and EGPR with minimized prediction errors. The level of importance analysis revealed that all the input parameters are relevant in the production of hydrogen. However, the influent chemical oxygen demand (COD) concentration and the medium temperature significantly influenced the hydrogen production from palm oil mill effluent, whereas the pH of the medium and the temperature significantly influenced the hydrogen production from the activated sludge.

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Section: Sources Technological Process Hydrogen Produced Referencementioning

confidence: 67%

Section: Sources Technological Process Hydrogen Produced Referencementioning

confidence: 99%

Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent

et al. 2022

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“…19 Mutlu et al used a multicriteria ML approach to predict the hydrogen-rich syngas distribution and provided theoretical guidance for the biomass-coal cogasification reaction. 20 Serrano et al verified the variation pattern of tar content with equivalence ratio and temperature by comparing it with experimental results based on the ANN tar prediction model. 21 Ge et al reviewed the feasibility of ML techniques to enhance the high-value utilization of lignocellulosic biomass.…”

Section: Introductionmentioning

confidence: 99%

“…Aguado et al integrated hybrid ML models to predict the hydrogen concentration in a downdraft fixed-bed biomass gasifier and used the model as a virtual sensor to calibrate or replace the actual sensor . Mutlu et al used a multicriteria ML approach to predict the hydrogen-rich syngas distribution and provided theoretical guidance for the biomass-coal cogasification reaction . Serrano et al verified the variation pattern of tar content with equivalence ratio and temperature by comparing it with experimental results based on the ANN tar prediction model .…”

Section: Introductionmentioning

confidence: 99%

Disentangled Representation Aided Physics-Informed Neural Network for Predicting Syngas Compositions of Biomass Gasification

Ren,

Wu,

Weng

et al. 2024

Energy Fuels

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Machine learning (ML) has been extensively studied and applied in the biomass gasification field currently. However, insufficient experimental data tends to cause a mismatch between the ML model and physical mechanism, particularly for the feedstocks that do not appear in the training data set, becoming a significant challenge in creating credible ML models for biomass gasification. Therefore, this study proposes a disentangled representation-aided physics-informed neural network method, briefly called DR-PINN, to predict biomass gasification syngas components. First, the DR-PINN extracts the latent variables to represent the feedstock properties through disentangled representation learning and generates synthetic samples in the gasificationrelated latent variable space to cover a full range of feedstock types. Then, DR-PINN employs inequality constraints to embed a priori monotonic relationships into the model training loss function. Finally, experimental and synthetic samples are simultaneously considered in the model training process to realize the synergy and complementarity of actual data information and existing physical knowledge using an evolutionary algorithm. As a result, DR-PINN shows good prediction performance (the feedstocks within the training data set: R 2 ≈ 0.96, root-mean-square error (RMSE) ≈ 1.7; the feedstocks outside the training data set: R 2 ≈ 0.81, RMSE ≈ 3). Moreover, even with the feedstocks outside the training data set, the DR-PINN model can strictly abide by the prior physical monotonic relationships, with the physical consistency degree equal to 1. Overall, the proposed DR-PINN demonstrates superior generalization and interpretability compared to other methods, such as RF, GBR, SVM, ANN, and PINN.

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“…The SVM and GPR have been widely used in modeling various processes. Bahadar et al 29 employed SVM and GPR for modeling the effect of process parameters on the production of hydrogen‐rich syngas by biomass and coal Co‐gasification. The study revealed that the SVM and GPR were robust in modeling the interaction between the input and targeted output parameters resulting in a good prediction of the hydrogen‐rich syngas.…”

Section: Introductionmentioning

confidence: 99%

Interaction effect of process parameters and Pd‐electrocatalyst in formic acid electro‐oxidation for fuel cell applications: Implementing supervised machine learning algorithms

Hossain

Ali

Rushd

et al. 2022

Intl J of Energy Research

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The increasing interest in renewable and sustainable energy production as a means of attaining net-zero carbon emissions in the near future has spurred research attention in the development of fuel cells that convert chemical energy to electrical energy. In this study machine learning algorithms namely Support Vector Machine (SVM) regression, Regression Trees, and Gaussian Process Regression (GPR) were configured for modeling the effect of palladium supported on carbon nanotube used for formic acid electro-oxidation. The effect of process parameters such as the amount of palladium, the amount of sodium tetrahydridoborate (NaBH 4 ), amount of water, and the electro-oxidation reaction time on the formic acid electro-oxidation to generate current density was evaluated by the various models. The trained SVM regressions models incorporated with linear, quadratic, cubic, and fine Gaussian kernel functions, as well as the Boosted and the Bagged regression Trees, did not show impressive performance as indicated by a low coefficient of determination (R 2 ) < 0.5 and high prediction errors. However, the SVM regression modeled with Median Gaussian kernel function, the GPR incorporated with rotational quadratic and squared exponential kernel functions displayed higher performance with R 2 > 0.6 but less than 0.7. The optimization of the SVM, Ensemble Tree, and GPR models resulted in significant performance with R 2 of 0.82, 0.83, and 0.85, respectively. The sensitivity analysis using modified Garson algorithm to determine how each of these parameters influences the predicted current density from the direct formic acid fuel cells showed that the level of importance of the input parameters on the predicted current density can be ranked as Pd composition > electro-oxidation time > amount of water > NaBH 4 proportion.

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Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach

Cited by 42 publications

References 37 publications

Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent

Comparative Analysis of Support Vector Machine Regression and Gaussian Process Regression in Modeling Hydrogen Production from Waste Effluent

Disentangled Representation Aided Physics-Informed Neural Network for Predicting Syngas Compositions of Biomass Gasification

Interaction effect of process parameters and Pd‐electrocatalyst in formic acid electro‐oxidation for fuel cell applications: Implementing supervised machine learning algorithms

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