Enhanced machine learning for nanomaterial identification of photo thermal hydrogen production

Ramkumar, G.; Tamilselvi, M.; Jebaseelan, S. D. Sundarsingh; Mohanavel, V.; Kamyab, Hesam; Prabu, R. Thandaiah

doi:10.1016/j.ijhydene.2023.07.128

Cited by 17 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study employed Artificial Neural Networks (ANN) to predict the optimal photocatalyst and conducted accuracy evaluations to validate the results. Remarkably, the ANN model exhibited a high accuracy rate of 91.66%, affirming the selection of RuO 2 /TiO 2 /Pt/C as the most promising photocatalyst (Ramkumar et al 2023). Liu et al used ensemble learning to forecast hydrogen evolution by splitting water using TiO 2 .…”

Section: Introductionmentioning

confidence: 87%

A machine learning ensemble approach for predicting solar-sensitive hybrid photocatalysts on hydrogen evolution

Bakır,

Orak,

Yüksel

2024

Phys. Scr.

View full text Add to dashboard Cite

Hydrogen, as the lightest and most abundant element in the universe, has emerged as a pivotal player in the quest for sustainable energy solutions. Its remarkable properties, such as high energy density and zero emissions upon combustion, make it a promising candidate for addressing the pressing challenges of climate change and transitioning towards a clean and renewable energy future. In an effort to improve efficiency and reduce experimental costs, we adopted machine learning techniques in this study. Our focus turned to predictive analyses of hydrogen evolution values using three photocatalysts, namely, graphene-supported LaFeO3 (GLFO), graphene-supported LaRuO3 (GLRO), and graphene-supported BiFeO3 (GBFO), examining their correlation with varying levels of pH, catalyst amount, and H2O2 concentration. To achieve this, a diverse range of machine learning models are used, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), XGBoost, Gradient Boosting, and AdaBoost—each bringing its strengths to the predictive modeling arena. An important step involved combining the most effective models—Random Forests, Gradient Boosting, and XGBoost—into an ensemble model. This collaborative approach aimed to leverage their collective strengths and improve overall predictability. The ensemble model emerged as a powerful tool for understanding photocatalytic hydrogen evolution. Standard metrics were employed to assess the performance of our ensemble prediction model, encompassing R squared, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The yielded results showcase exceptional accuracy, with R squared values of 96.9%, 99.3%, and 98% for GLFO, GBFO, and GLRO, respectively. Moreover, our model demonstrates minimal error rates across all metrics, underscoring its robust predictive capabilities and highlighting its efficacy in accurately forecasting the intricate relationships between GLFO, GBFO, and GLRO values and their influencing factors.

show abstract

Section: Introductionmentioning

confidence: 87%

A machine learning ensemble approach for predicting solar-sensitive hybrid photocatalysts on hydrogen evolution

Bakır,

Orak,

Yüksel

2024

Phys. Scr.

View full text Add to dashboard Cite

show abstract

“…According to Refs. [1][2][3][4], rapid economic development has a negative impact on the global environment, resulting in countries around the world investing a lot of labor and material resources in designing new catalysts to reduce environmental pollution [5][6][7][8][9][10][11]. In the early stage of the development of photocatalysts, it is only through constantly preparing different catalysts and conducting photocatalytic experiments that effective photocatalysts can be found [12,13].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction

Wang,

Mo,

et al. 2024

Catalysts

View full text Add to dashboard Cite

Photocatalysts have made great contributions to the degradation of pollutants to achieve environmental purification. The traditional method of developing new photocatalysts is to design and perform a large number of experiments to continuously try to obtain efficient photocatalysts that can degrade pollutants, which is time-consuming, costly, and does not necessarily achieve the best performance of the photocatalyst. The rapid development of photocatalysis has been accelerated by the rapid development of artificial intelligence. Intelligent algorithms can be utilized to design photocatalysts and predict photocatalytic performance, resulting in a reduction in development time and the cost of new catalysts. In this paper, the intelligent algorithms for photocatalyst design and photocatalytic performance prediction are reviewed, especially the artificial neural network model and the model optimized by an intelligent algorithm. A detailed discussion is given on the advantages and disadvantages of the neural network model, as well as its application in photocatalysis optimized by intelligent algorithms. The use of intelligent algorithms in photocatalysis is challenging and long term due to the lack of suitable neural network models for predicting the photocatalytic performance of photocatalysts. The prediction of photocatalytic performance of photocatalysts can be aided by the combination of various intelligent optimization algorithms and neural network models, but it is only useful in the early stages. Intelligent algorithms can be used to design photocatalysts and predict their photocatalytic performance, which is a promising technology.

show abstract

Inhibition of norfloxacin on fermentative hydrogen production: Performance evaluation and metagenomic analysis

Gao,

Sun,

Sun

et al. 2024

Chemical Engineering Journal

View full text Add to dashboard Cite

Enhanced machine learning for nanomaterial identification of photo thermal hydrogen production

Cited by 17 publications

References 36 publications

A machine learning ensemble approach for predicting solar-sensitive hybrid photocatalysts on hydrogen evolution

A machine learning ensemble approach for predicting solar-sensitive hybrid photocatalysts on hydrogen evolution

Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction

Inhibition of norfloxacin on fermentative hydrogen production: Performance evaluation and metagenomic analysis

Contact Info

Product

Resources

About