Machine Learning Models for Pyrolytic Conversion of Red Sea Seaweed

What is it about?

* The study aimed to develop a prediction model for the pyrolytic conversion of Red Sea seaweed (Sargassum sp.) using machine learning tools. * Two machine learning algorithms, Artificial Neural Network (ANN) and Support Vector Machine (SVM), were investigated for this purpose. * Pyrolysis experiments were conducted on the seaweed samples, and the obtained data on mass loss, time, and temperature were used to train and test the prediction model. * The results showed that ANN provided better accuracy than SVM, with higher R2 values and lower error rates. * This suggests that machine learning-based tools, such as ANN, can be reliable means for forecasting pyrolytic conversion. * The findings have implications for understanding and optimizing the pyrolysis process of seaweed, which can be valuable for developing efficient pyrolytic systems and utilizing seaweed biomass as a renewable energy source.

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Photo by Markus Winkler on Unsplash

Photo by Markus Winkler on Unsplash

Why is it important?

The study focuses on the prediction of pyrolytic conversion of Red Sea seaweed using machine learning tools, specifically Artificial Neural Network (ANN) and Support Vector Machine (SVM) . The use of machine learning algorithms in predicting pyrolytic conversion is a unique approach that can provide accurate results and insights into the behavior of complex models . The comparison between ANN and SVM in terms of accuracy and reliability for pyrolytic conversion prediction is a timely contribution to the field . The study addresses the challenge of the complex and non-linear relationship between kinetic parameters and biomass components in model development . The findings highlight the potential of machine learning-based tools, particularly ANN, as reliable means for forecasting pyrolytic conversion, which can have implications for optimizing pyrolysis processes and utilizing seaweed biomass as a renewable energy source . The difference it might make to help increase readership: The unique approach of using machine learning algorithms for pyrolytic conversion prediction in the context of Red Sea seaweed can attract readers interested in both machine learning and renewable energy applications . The comparison between ANN and SVM can be of interest to researchers and practitioners in the field of machine learning, as it provides insights into the performance of these algorithms in a specific application domain . The potential implications of the study's findings for optimizing pyrolysis processes and utilizing seaweed biomass as a renewable energy source can attract readers interested in sustainable energy solutions and biomass utilization.