Modeling the adsorption of benzeneacetic acid on CaO2 nanoparticles using artificial neural network

AbstractThe present work reported a method for removal of benzeneacetic acid from water solution using CaO2 nanoparticle as adsorbent and modeling the adsorption process using artificial neural network (ANN). CaO2 nanoparticles were synthesized by a chemical precipitation technique. The characterization and confirmation of nanoparticles have been done by using different techniques such as X-ray powder diffraction (XRD), high resolution field emission scanning electron microscope (HR-FESEM),transmittance electron microscopy (TEM) and high-resolution TEM (HRTEM) analysis. ANN model was developed by using elite-ANN software. The network was trained using experimental data at optimum temperature and time with different CaO2 nanoparticle dosage (0.002–0.05 g) and initial benzeneacetic acid concentration (0.03–0.099 mol/L). Root mean square error (RMS) of 3.432, average percentage error (APE) of 5.813 and coefficient of determination (R2) of 0.989 were found for prediction and modeling of benzeneacetic acid removal. The trained artificial neural network is employed to predict the output of the given set of input parameters. The single-stage batch adsorber design of the adsorption of benzeneacetic acid onto CaO2 nanoparticles has been studied with well fitted Langmuir isotherm equation which is homogeneous and has monolayer sorption capacity

Stoichiometric and Spectroscopic Study of Reactive Extraction of Phenylacetic Acid with Tri-n-Butyl Phosphate

Phenylacetic acid is widely used in the pharmaceutical industry for production of antibiotics. The recovery of phenylacetic acid from dilute aqueous waste with tri-n-butyl phosphate in methyl isobutyl ketone and petroleum ether has been attempted, and the results are presented in terms of distribution coefficient, extraction efficiency, apparent equilibrium constant, and loading ratio. The mechanism of reactive extraction was analyzed and the stoichiometric ratio of phenylacetic acid to tri-n-butyl phosphate in methyl isobutyl ketone and petroleum ether was found to be 1:0.5 and 1:1.2. Mass action law was used to represent the reactive extraction equilibrium for phenylacetic acid−tri-n-butyl phosphate−diluents which satisfied much in the present study. FTIR spectroscopy was studied for confirmation of the formation of a complex between acid and extractant. Further relative basicity approach has been extended to represent the experimental results. The model is best suited to experimental results

Evaluation of Distribution of Succinic Acid between Binary Phase System with Biodiesel + N,N-Dioctyloctan-1-amine

The present study is aimed at using one of the most promising methods called reactive extraction to extract succinic acid from aqueous solution by using N,N-dioctyloctan-1-amine in biodiesel as diluent made from sunflower oil, rice bran oil, sesame oil, and karanji oil. The results of extraction studies with the diluents (physical) showed their inability to recover any acid by themselves. In reactive extraction, the organic phase extracting power solely depends on tri-n-octylamine. The ranges of the distribution coefficient are found as 7.62–18.12 for sunflower oil biodiesel, 8.33–17.45 for rice bran oil biodiesel, 7.0–17.67 for sesame oil biodiesel, and 9.85–21.36 for karanji oil biodiesel. The ranges of the loading ratio are 0.1–3.0 for sunflower oil biodiesel, 0.1–2.9 for rice bran oil biodiesel, 0.2–2.9 for sesame oil biodiesel, and 0.1–2.9 for karanji oil biodiesel. The karanji and sunflower oil showed higher values of distribution coefficient (KD) over rice bran oil and sesame oil which might be due to presence of both C20 and special fatty acids. The results show that biogenous diluents along with N,N-dioctyloctan-1-amine as extractant form a nontoxic and viable option for the extraction of succinic acid in the binary phase system