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Abstract

The accurate prediction of thermogravimetric properties is critical for evaluating the suitability of natural materials like Dawakin Tofa clay for heat storage applications, but traditional linear models often fail to capture the complex, non-linear relationships inherent in such datasets. This study develops a hybrid intelligence framework integrating Bilateral Neural Network (BNN), Kernel Support Vector Machine (KSVM), Step-Wise Linear Regression (SWLR), and Robust Linear Regression (RLR) to predict the derivative weight of Dawakin Tofa clay based on 5,030 experimentally obtained instances. Comprehensive data preprocessing, including normalization, feature selection, and dataset splitting (80% training and 20% testing), ensured high-quality inputs for the models. The results demonstrated that non-linear models significantly outperformed linear approaches, with BNN achieving a coefficient of determination R² of 0.999, a Mean Absolute Error (MAE) of 0.004377, and a Mean Absolute Percentage Error (MAPE) of 9.6% on the testing dataset. Similarly, KSVM achieved an R² of 0.999, MAE of 0.012134, and MAPE of 26.7%, indicating its robust predictive capabilities. In contrast, linear models performed poorly, with SWLR and RLR yielding R² values of 0.03 and -0.41, respectively, and unacceptably high MAPE values of 612% and 53.5%. The findings underscore the limitations of linear models in predicting complex thermogravimetric behaviors and highlight the transformative potential of advanced machine learning techniques like BNN and KSVM. Furthermore, these results align with global sustainability efforts, including SDG 7 and 12, by optimizing the use of locally available, eco-friendly materials for energy storage. This study provides a replicable framework for leveraging artificial intelligence to enhance material characterization, offering a significant step toward developing sustainable energy solutions.

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