Prediction of hydrogen production by magnetic field effect water electrolysis using artificial neural network predictive models

Bilgiç, Gülbahar; Öztürk, Başak; Atasever, Sema; Şahin, Mükerrem; Kaplan, Hakan

doi:10.1016/j.ijhydene.2023.02.082

Cited by 10 publications

(4 citation statements)

References 34 publications

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“…This method transforms every categorical data into a column, and the presence and absence are respectively indicated with 1 and 0 values. Thanks to one-hot encoding, models understand the dataset better and perform better [12]. Another pre-processing technique that was applied to the dataset was normalization.…”

Section: Feature Extraction and Preprocessingmentioning

confidence: 99%

“…Recently, as in many different fields, there has been a great interest in machine learning (ML) to optimize the operating conditions of the gasification system. Regression analysis, artificial neural networks (ANN) [11,12], tree-based approaches like classification and random forest (RF) [13] based on regression trees (RT) and extreme trees, and support vector machines (SVM) [14] are frequently supported when modeling hydrogen generation processes from biomass [15].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Random Forest and Support Vector Regression Models in Predicting Hydrogen Production Process from Biomass

Bilgiç,

Gök

2024

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

Self Cite

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The need for energy in the world is increasing day by day and various energy production methods are used to meet this need. Production of hydrogen from biomass is one of these methods. Hydrogen production from biomass is a promising process to produce hydrogen and energy which has advantages such as the ability to use sustainable energy sources like biomass and solid waste, being carbon neutral, and increasing energy independence thanks to the variation of resources and the availability of local resources. The catalysts used in this process which can be conducted in three separate ways, affect hydrogen and energy production positively or negatively. One of the most important steps in effectively acquiring the ideal amount of product is predicting the outcomes of this procedure. This article compares a support vector regression (SVR) and random forest (RF) model to predict how various inputs used to produce hydrogen from biomass will affect hydrogen output. Additionally, the effect of catalyst addition on hydrogen yield in biomass processes was examined. In this context, 57 experimental studies from the literature were selected as a data set. From this data, 90% was selected for training and 10% for testing. The outputs were evaluated according to parameters such as R2, RMSE and MSE. The results show that RF and SVR models can significantly predict catalyst activity and hydrogen production.

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Section: Feature Extraction and Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Random Forest and Support Vector Regression Models in Predicting Hydrogen Production Process from Biomass

Bilgiç,

Gök

2024

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

Self Cite

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“… 18 Due to the advanced genetic algorithm coupled with neurons and hidden layers in the ANN, it can compute and train even with many experimentally obtained nonlinear data. 17 Thus, the predictive analytical outcome from ANN was generally more refined and accurate than RSM. Both models can be applied in several scientific domains to use experimental data to determine the functional relationships between the process's input variables and their output response variables.…”

Section: Introductionmentioning

confidence: 99%

“… 16 An ANN approach was successfully implemented in magnetic field-influenced water electrolysis to predict the hydrogen evaluation with a mean squared error (MSE) of 0.0112 and a correlation coefficient of 0.97. 17 An optimized numerical matrix can be formulated without any assumptions by RSM predicting the best possible outcome. By considering interactions among different numbers and sets of influential variables, RSM works with fewer experimental trials for validation, unlike ANN.…”

Section: Introductionmentioning

confidence: 99%

Optimizing methanol synthesis from CO₂ using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability

Kumar,

Nayak,

Chowdhury

et al. 2024

RSC Adv.

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Hydrogen production by 3D-printed electrodes

Pereira,

Neumsteir,

Bonacin

2024

Discov Mater

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The increasing demand for energy, depletion of traditional energy sources, and environmental concerns have generated an energy crisis in recent years. To address this challenge, innovative and low-cost solutions have been sought, such as the use of 3D printing for decentralized hydrogen production. In this paper, we present the production of conductive filaments prepared from polylactic acid (PLA) and Carbon Black (CB), which were used for the 3D printing of electrodes. The produced materials were characterized by scanning electron microscopy, thermal analysis, and electrochemical techniques. The 3D-printed electrodes were used as substrates for CoPi electrodeposition to evaluate their performance in hydrogen production. The 3D-printed electrodes, made from filaments prepared in the laboratory, demonstrated superior electrochemical performance and hydrogen production compared to electrodes printed compared with commercial filament. The PLA@CB72R and CoPi#PLA@CB72R electrodes showed better hydrogen production performance, 10.08 and 10.20 μmol min−1, respectively. This study provides a perspective for the manufacture of filaments and 3D-printed electrodes for energy conversion applications, such as hydrogen production.

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Prediction of hydrogen production by magnetic field effect water electrolysis using artificial neural network predictive models

Cited by 10 publications

References 34 publications

Comparison of Random Forest and Support Vector Regression Models in Predicting Hydrogen Production Process from Biomass

Comparison of Random Forest and Support Vector Regression Models in Predicting Hydrogen Production Process from Biomass

Optimizing methanol synthesis from CO₂ using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability

Hydrogen production by 3D-printed electrodes

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Prediction of hydrogen production by magnetic field effect water electrolysis using artificial neural network predictive models

Cited by 10 publications

References 34 publications

Comparison of Random Forest and Support Vector Regression Models in Predicting Hydrogen Production Process from Biomass

Comparison of Random Forest and Support Vector Regression Models in Predicting Hydrogen Production Process from Biomass

Optimizing methanol synthesis from CO2 using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability

Hydrogen production by 3D-printed electrodes

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Product

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About

Optimizing methanol synthesis from CO₂ using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability