Cutting-Edge Machine Learning Techniques for Accurate Prediction of Agglomeration Size in Water-Alumina Nanofluids

Nanoparticle agglomeration is one of the most problematic phenomena during nanofluid synthesis by a two-step procedure. Understanding and accurately estimating agglomeration size is crucial, as it significantly affects nanofluids' properties, behavior, and successful applications. To the best of our knowledge, the literature has not yet applied machine learning methods to estimate alumina agglomeration size in water-based nanofluids. So, this research employs a range of machine learning models-Random Forest, Adaptive Boosting, Extra Trees, Categorical Boosting, and Multilayer Perceptron Neural Networks-to predict alumina agglomeration sizes in water-based nanofluids. To this end, a comprehensive experimental database, including 345 alumina agglomeration sizes in water-based nanofluids, compiled from 29 various sources from the literature, is utilized to train these models and monitor their generalization ability in the testing stage. The models estimate agglomeration size based on multiple factors: alumina concentration, ultrasonic time, power, frequency, temperature, surfactant type and concentration, and pH levels. The relevancy test based on the Pearson method clarifies that Al2O3 agglomeration size in water primarily depends on ultrasonic frequency, ultrasonic power, alumina concentration in water, and surfactant concentration. Comparative analyses based on numerical and graphical techniques reveal that the Categorical Boosting model surpasses others in accurately simulating this complex phenomenon. It effectively captures the intricate relationships between key features and alumina agglomeration size, achieving an average absolute relative deviation of 6.75%, a relative absolute error of 12.83%, and a correlation coefficient of 0.9762. Furthermore, applying the leverage method to the experimental data helps identify two problematic measurements within the database. These results validate the effectiveness of the Categorical Boosting model and contribute to the broader goal of enhancing our understanding and control of nanofluid properties, thereby aiding in improving their practical applications.