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Seasonal samples were taken from four selected stations on the Habbaniya lake. middle of Iraq during 1997, to study six heavy metals (Cu, Cd, Pb, Ni, Mn, and Zn), in water suspended particles, sediments and aquatic plants. Five plant species were collected, represented the most dominant in the lake, namely Myriophyllum verticillattum, Potamogeton crispus, P. pectinatus, Ceratophyllum demersum and Vallisnaria spiralis. Zn had the highest concentration among the studied metals in filtered water and suspended particles with an average of 4.08 µg/l and 26088 µg/g, respectively. Whereas, Mn had the highest in the sediments and all studied aquatic plants, which ranged between 213.3-2027 µg/g and 100-1725 µg/g, respectively, Meanwhile, Cd had the lowest concentration in all studied samples. [ABSTRACT FROM AUTHOR]

• Solubility enhancement through SCCO 2 system. • Machine learning approaches to estimate and optimize the salicylsalicylic acid. • KNN model has been used as the best model to optimize the solubility of drug. Over the last decades, significant drawbacks of organic solvents such as high toxicity have motivated the scientists to find more eco-friendly solvents. Supercritical fluids (SCFs), especially SCCO 2 , are known as a promising class of solvent, which have shown their indisputable potential of application in industrial-based pharmaceutical activities due to possessing various advantages such as high abundancy, low cost, and insignificant toxicity. Machine Learning (ML) is considered as a numerical approach to estimate drug solubility in pharmaceutical industry. The purpose of this manuscript is to estimate the solubility of salicylsalicylic acid in SCCO 2 and compare it with experimental data using machine learning (ML) approach. A regression problem with 32 input vectors is the subject of this study, which is being conducted. This dataset contains two input features (P and T) and one output feature. We utilized Decision Tree (DT), K-nearest neighbor (KNN), and Multilayer perceptron (MLP) regression models as the first time for salicylsalicylic acid, which had error rates of 1.10E-01, 1.07E-01, and 7.13E-01, respectively, when using the MAPE measure. In addition, the R-squared scores for the DT, KNN, and MLP models are 0.974, 0.996, and 0.809, respectively. The third statistic is MAE, in which the error rates of models are 5.27E-05 for DT, 5.53E-05 for KNN, and 2.61E-04 for MLP. The error rates of DT, KNN, and MLP are all 5.27E-05. Finally, KNN was the most general model, with optimal values of P = 400, T = 338.0, and Y = 0.00388 being obtained. [ABSTRACT FROM AUTHOR]