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<p>This study presents a data-driven framework for predicting key thermophysical properties including dynamic viscosity, speed of sound, and density of aqueous solutions containing aliphatic biogenic polyamines (ABPs), which are of growing importance in biochemical and industrial applications. A total of 198 experimentally measured data points were used to train and validate five advanced machine learning models: K-Nearest Neighbors (KNN), Ensemble Learning (EL), Convolutional Neural Networks (CNN), Adaptive Boosting (AdaBoost), and Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN). Hyperparameter optimization was conducted using the Coupled Simulated Annealing (CSA) algorithm. The study focused on three ABPs including Putrescine dihydrochloride (PUT), Cadaverine dihydrochloride (CAD), and Spermine tetrahydrochloride (SPER) across a range of molar masses, concentrations, and temperatures. Sensitivity analysis using the Monte Carlo method revealed ABP type and molar mass as dominant factors influencing the target properties. Among the models, MLP-ANN demonstrated the highest predictive accuracy for density and viscosity, while EL performed best for speed of sound. The results confirm the potential of machine learning as a reliable, efficient, and cost-effective alternative to conventional experimental approaches for modeling complex solution behavior.</p>

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Accurate prediction of density, viscosity, and speed of sound in aqueous aliphatic biogenic polyamine solutions using data-driven modeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月8日

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  • Suleiman Ibrahim Mohammad,
  • Hamza Abu Owida,
  • Asokan Vasudevan,
  • Suhas Ballal,
  • Shaker Al-Hasnaawei,
  • Subhashree Ray,
  • Naveen Chandra Talniya,
  • Aashna Sinha,
  • Vatsal Jain,
  • Ahmad Abumalek

摘要

This study presents a data-driven framework for predicting key thermophysical properties including dynamic viscosity, speed of sound, and density of aqueous solutions containing aliphatic biogenic polyamines (ABPs), which are of growing importance in biochemical and industrial applications. A total of 198 experimentally measured data points were used to train and validate five advanced machine learning models: K-Nearest Neighbors (KNN), Ensemble Learning (EL), Convolutional Neural Networks (CNN), Adaptive Boosting (AdaBoost), and Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN). Hyperparameter optimization was conducted using the Coupled Simulated Annealing (CSA) algorithm. The study focused on three ABPs including Putrescine dihydrochloride (PUT), Cadaverine dihydrochloride (CAD), and Spermine tetrahydrochloride (SPER) across a range of molar masses, concentrations, and temperatures. Sensitivity analysis using the Monte Carlo method revealed ABP type and molar mass as dominant factors influencing the target properties. Among the models, MLP-ANN demonstrated the highest predictive accuracy for density and viscosity, while EL performed best for speed of sound. The results confirm the potential of machine learning as a reliable, efficient, and cost-effective alternative to conventional experimental approaches for modeling complex solution behavior.