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Allan N Soriano
School of Chemical Engineering and Chemistry, Mapúa Institute of Technology, Manila
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry

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Packed Bed Biosorption of Lead and Copper Ions Using Sugarcane Bagasse Norwin Dale F Duga; Pauline Edrickke A Imperial; Allan N Soriano; Aileen D Nieva
ASEAN Journal of Chemical Engineering Vol 16, No 1 (2016)
Publisher : Department of Chemical Engineering, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1119.124 KB) | DOI: 10.22146/ajche.49671

Abstract

Bagasse, a waste material from sugarcane has been studied as a biosorbent for removing heavy metals, Pb2+ and Cu2+, in a continuous system using a packed bed column. This study was undertaken to determine the influence of varying the bed height and flow rate on the breakthrough and saturation time. Thomas, Adams-Bohart and Yoon-Nelson models were used to assess the effects of varying parameters and both Thomas and Yoon-Nelson models were found to be satisfactory to describe the column data obtained in the experiment. Moreover, lead ions are adsorbed more efficiently with an adsorption capacity of 4.54 mg/g compared to copper ions with 3.98 mg/g at the most feasible parameters having a flow rate of 100 mL/min and a bed height of 30 cm
Prediction of Density of Binary Mixtures of Ionic Liquids with Alcohols (Methanol/Ethanol/1-Propanol) using Artificial Neural Network Karen Faith P. Ornedo Ramos; Carla Angela M. Muriel; Adonis P Adornado; Allan N Soriano; Vergel C Bungay
ASEAN Journal of Chemical Engineering Vol 15, No 2 (2015)
Publisher : Department of Chemical Engineering, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1642.479 KB) | DOI: 10.22146/ajche.49685

Abstract

Ionic liquids demonstrated successful potential applications in the industry most specifically as the new generation of solvents for catalysis and synthesis in chemical processes, thus knowledge of their physico-chemical properties is of great advantage. The present work presents a mathematical correlation that predicts density of binary mixtures of ionic liquids with various alcohols (ethanol/methanol/1-propanol). The artificial neural network algorithm was used to predict these properties based on the variations in temperature, mole fraction, number of carbon atoms in the cation, number of atoms in the anion, number of hydrogen atoms in the anion and number of carbon atoms in the alcohol. The data used for the calculations were taken from ILThermo Database. Total experimental data points of 1946 for the considered binaries were used to train the algorithm and to test the network obtained. The best neural network architecture determined was found to be 6-6-10-1 with a mean absolute error of 48.74 kg/m3. The resulting correlation satisfactorily represents the considered binary systems and can be used accurately for solvent related calculations requiring properties of these systems.
Co-Authors Adonis P Adornado Aileen D Nieva Carla Angela M. Muriel Karen Faith P. Ornedo Ramos Norwin Dale F Duga Pauline Edrickke A Imperial Vergel C Bungay