Abstract. Polymers applications have been progressively increased in sciences and engineering including chemistry, pharmacology science, and chemical and petroleum engineering due to their attractive properties. Amongst the all types of polymers, partially Hydrolyzed Polyacrylamide (HPAM) is one of the widely used polymers especially in chemistry, and chemical and petroleum engineering. Capability of solution viscosity increment of HPAM is the key parameter in its successful applications; thus, the viscosity of HPAM solution must be determined in any study. Experimental measurement of HPAM solution viscosity is time-consuming and can be expensive for elevated conditions of temperatures and pressures, which is not desirable for engineering computations. In this communication, Multilayer Perceptron neural network (MLP), Least Squares Support Vector Machine approach optimized with Coupled Simulated Annealing (CSA-LSSVM), Radial Basis Function neural network optimized with Genetic Algorithm (GA-RBF), Adaptive Neuro Fuzzy Inference System coupled with Conjugate Hybrid Particle Swarm Optimization (CHPSO-ANFIS) approach, and Committee Machine Intelligent System (CMIS) were used to model the viscosity of HPAM solutions. Then, the accuracy and reliability of the developed models in this study were investigated through graphical and statistical analyses, trend prediction capability, outlier detection, and sensitivity analysis. As a result, it has been found that the MLP and CMIS models give the most reliable results with determination coefficients (R 2 ) more than 0.98 and Average Absolute Relative Deviations (AARD) less than 4.0%. Finally, the suggested models in this study can be applied for efficient estimation of aqueous solutions of HPAM polymer in simulation of polymer flooding into oil reservoirs.

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In this paper adaptive neuro-fuzzy inference system (ANFIS) is developed to predict the oil prices of the organization of petroleum exporting countries (OPEC). The novel aspect of the proposed model is the proposed features set fed the ANFIS. In the numerical studies, the proposed method is tested to modeling OPEC oil time series as a case study. According to the comparative results, ANFIS with proposed variables set shows higher accuracy than conventional neural networks in oil price prediction.

This study aimed to determine the presence of phthalates and their concentration in household's drinking water and to examine their potential risk for inhabitants in urban regions of Isfahan, Iran. During the summer and winter of 2017, samples were extracted from 33 private residences via dispersive liquid-liquid microextraction with some modifications. Gas chromatography-mass spectrometry was used to determine the presence of four major phthalates. According to the results, four phthalates, including dibutyl phthalate, benzyl butyl phthalate (BBP), diethyl phthalate, and di(2-ethylhexyl)phthalate (DEHP), were present in the samples. The highest contamination with phthalates was attributed to DEHP (606.89 ng/l). Except for BBP, the mean concentrations of other PAE compounds were higher in summer than in winter. The mean concentration of DEHP in sampling points with plastic pipes was higher than that of regions with metal pipes. Based on the health risk assessment, exposure of humans to phthalates in drinking water was acceptable and did not pose carcinogenic effects. Further studies are recommended for adequate monitoring of phthalates in drinking water, food, and air in order to ensure human health.