Biomass-derived carbons physically activated in one or two steps for CH4/CO2 separation

The present study aims at evaluating the suitability of producing activated carbons (ACs) derived from wheat straw by a one-step synthesis approach, as an alternative to more conventional two steps production processes (i.e., pyrolysis and subsequent activation). The performance of the produced ACs, in one or two steps, as sustainable and selective CO2 adsorbents for CH4/CO2 separation is compared. In addition, the influence of pyrolysis conditions on the properties of the resulting two-step ACs is carefully analyzed. We show that the biochar-based precursors of ACs presenting the best textural properties were obtained under mild conditions of maximum temperature and absolute pressure during pyrolysis. The one-step ACs were fully comparable —in terms of textural properties as well as CO2 uptake and selectivity— to those produced by the more conventional two-step synthesis process. In addition, results obtained from breakthrough curve simulations highlight that the best AC in terms of CH4 recovery under dynamic conditions was produced by a one-step activation. Therefore, the one-step process appears to be as an attractive route for the production of engineered carbon materials, which can lead to significant cost savings in large-scale production systems

Prediction Of Ternary Ion-exchange Equilibrium Using Artificial Neural Networks And Law Of Mass Action [aplicação De Redes Neurais Artificiais E Da Lei Da Ação Das Massas Na Predição De Equilíbrio De Sistemas Ternários De Troca-iônica]

The Law of Mass Action generally models the equilibrium data from ion exchange processes. This methodology is rigorous in terms of thermodynamics and takes into consideration the non-idealities in the solid and aqueous phases. However, the artificial neural networks may also be employed in the phase equilibrium modeling. In this study, both methodologies were tested to describe the ion exchange equilibrium in the binary systems SO 4 2--NO 3 -, SO 4 2--Cl -, NO 3-Cl - and in the ternary system SO 4 2--Cl --NO 3 -, by AMBERLITE IRA 400 resin as ion exchanger. Datasets used in current study were generated by the application of the Law of Mass Action in the binary systems. Results showed that in the equilibrium modeling of binary systems both methodologies had a similar performance. However, in the prediction of the ternary system equilibrium, the Artificial Neural Networks were not efficient. Networks were also trained with the inclusion of ternary experimental data. The Law of Mass Action in the equilibrium modeling of the ternary system was more efficient than Artificial Neural Networks in all cases.3415360Allen, R.M., Addison, P.A., Dechapunya, A.H., The characterization of binary and ternary ion exchange equilibria (1989) The Chemical Engineering Journal, 40 (3), pp. 151-158Boyer, W.D.A., Baird, M.H.I., Nirdosh, I., Ion exchange equilibria in binary and ternary systems (1999) The Canadian Journal of Chemical Engineering, 77 (1), pp. 92-98Canevesi, R.L.S., Junior, E.A.Z., Martins, T.D., Barella, R.A., Moreira, M.F.P., Silva, E.A., Modelagem do processo de troca iônica pela lei da ação das massas e redes neurais artificiais (2009) Estudos Tecnológicos, 5 (3), pp. 389-403Fagundes-Klen, M.R., Ferri, P., Martins, T.D., Tavares, C.R.G., Silva, E.A., Equilibrium study of the binary mixture of cadmium-zinc ions biosorption by the Sargassum filipendula species using adsorption isotherms models and neural network (2007) Biochemical Engineering Journal, 34 (2), pp. 136-146Jha, S.K., Madras, G., Neural network modeling of adsorption equilibria of mixtures in supercritical fluids (2005) Industrial and Engineering Chemistry Research, 44 (17), pp. 7038-7041Klassen, T., Martins, T.D., Cardozo-Filho, L., Silva, E.A., Modelagem do sistema de resfriamento por imersão de carcaças de frangos utilizando redes neurais artificiais (2009) Acta Scientiarum. Technology, 31 (2), pp. 201-205Mehablia, M.A., Shallcross, D.C., Stevens, G.W., Prediction of multicomponent ion exchange equilibria (1994) Chemical Engineering Science, 49 (14), pp. 2277-2286Nelder, J.A., Mead, R., A simplex method for function minimization (1965) The Computer Journal, 7 (4), pp. 308-313Prakash, N., Manikandan, S.A., Govindarajan, L., Vijayagopal, V., Prediction of biosorption efficiency for the removal of copper(II) using artificial neural networks (2008) Journal of Hazardous Materials, 152 (3), pp. 1268-1275Schmitz, J.E., Zemp, R.J., Mendes, M.J., Artificial neural networks for the solution of the phase stability problem (2006) Fluid Phase Equilibria, 245 (1), pp. 83-87Shallcross, D.C., Herrmann, C.C., McCoy, B.J., An improved model for the prediction of multicomponention exchange equilibria (1988) Chemical Engineering Science, 43 (2), pp. 279-288Silva, L.H.M., Neitzel, I., Lima, E.P., Resolução de um modelo de reator de leito fixo não adiabático com dispersão axial utilizando redes neurais artificiais (2003) Acta Scientiarum. Technology, 25 (1), pp. 39-44Smith, R.P., Woodburn, E.T., Prediction of multicomponent ion exchange equilibria for the ternary system SO4 -2-NO3--Cl- from data of binary systems (1978) AIChE Journal, 24 (4), pp. 577-587Souza, E.C.B., Ribeiro, S.R.A., Botelho, M.F., Krueger, C.P., Centeno, J.A.S., Geração de isolinhas, com dados obtidos por levantamento GPS/L1L2, mediante a técnica de Redes Neurais Artificiais (2006) Acta Scientiarum. Technology, 25 (2), pp. 205-212Tamura, H., Theorization on ion-exchange equilibria: Activity of species in 2-D phases (2004) Journal of Colloid and Interface Science, 279 (1), pp. 1-22Valverde, J.L., de Lucas, A., Gonzalez, M., Rodriguez, J.F., Equilibrium data for the exchange of Cu2+, Cd2+, and Zn2+ Ions for H+ on the cationic exchanger amberlite IR-120 (2002) Journal of Chemical and Engineering Data, 47 (3), pp. 613-617Vo, B.S., Shallcross, D.C., Multi-component ion exchange equilibria prediction (2003) Chemical Engineering Research and Design, 81 (10), pp. 1311-132

Gastro-resistant Controlled Release Of Otc Encapsulated In Alginate/chitosan Matrix Coated With Acryl-eze® Mp In Fluidized Bed

A gastro-resistant system of acryl-EZE® MP coated alginate/chitosan microparticles was developed to improve the controlled release of oxytetracycline (OTC). Microparticles were obtained by complex coacervation and, thereafter, were coated using fluidized polymer dispersion with acryl-EZE® MP solution. OTC distribution inside the microparticles was determined by multiphoton confocal microscopy, demonstrating the efficiency of encapsulation process. In vitro OTC release kinetic was performed in order to obtain the release profile in gastric and intestinal simulated fluids. A fast initial release, or burst effect, was observed with uncoated microparticles loaded with OTC in gastric conditions. When a 50% mass increase in acryl-EZE® MP coating was achieved, OTC release in acidic medium was greatly reduced, resulting in the expected gastro-resistant effect. Different mathematical models were applied to describe the drug diffusion across the polymer matrix. The Logistic model was the best tool to interpret the experimental data in most of the systems studied. © 2014 Wiley Periodicals, Inc.13112Maroni, A., Curto, M.D.D.C., Zema, L., Foppoli, A., Andrea Gazzaniga, A., (2013) Int. J. Pharm, 457, p. 372Ferrari, P.C., Souza, F.M., Giorgetti, L., Oliveira, G.F., Chaud, M.V., Ferraz, H.G., Evangelista, R.C., (2012) Carbohydr. Polym., 87, p. 2526Depypere, F., Oostveldt, P.V., Pieters, J.G., Dewettinck, K., (2009) Eur. J. Pharm. Biopharm., 73, p. 179Severino, P., Oliveira, G.G.G., Ferraz, H.G., Souto, E.B., Santana, M.H.A., (2012) J. Pharm. Anal., 2, p. 188Miyadai, N., Higashi, K., Moribe, K., Yamamoto, K., (2012) Adv. Powder Technol., 23, p. 40Rujivipat, S., Bodmeier, R., (2012) Eur. J. Pharm. Biopharm., 81, p. 223Albanez, R., Nitz, M., Taranto, O.P., (2013) Adv. Powder Technol., 24, p. 659Pina, M.E., Sousa, A.T., Brojo, A.P., (1996) Int. J. Pharm., 133, p. 139Priese, F., Wolf, B., (2013) Powder Technol., 241, p. 149Reddy, J.R.K., Gnanaprakash, K., Badarinath, A.V., Chetty, C.M.S., (2009) J. Pharm. Sci. Res., 1, p. 131Shukla, R.K., Tiwari, A., (2012) Carbohydr. Polym., 88, p. 399Mi, F.L., Wong, T.B., Shyu, S.S., (1997) J. Microencapsul., 14, p. 577Cruz, M.C.P., Ravagnani, S.P., Brogna, F.M.S., Campana, S.P., Trivinõ, G.C., Lisboa, A.C.L., Mei, L.H.I., (2004) Biotechnol. Appl. Biochem., 40, p. 243González-Rodríguez, M.L., Holgado, M.A., Sánchez- Lafuente, C., Rabasco, A.M., Fini, A., (2002) Int. J. Pharm., 232, p. 225Elgindy, N., Elkhodairy, K., Molokhia, A., Elzoghby, A., (2011) Int. J. Pharm., 411, p. 113Zhang, Y., Wei, W., Lv, P., Wang, L., Ma, G., (2011) Eur. J. Pharm. Biopharm., 77, p. 11Huang, X., Brazel, C.S., (2001) J. Controlled Release., 73, p. 121Park, T.G., Cohen, S., Langer, R., (1992) Pharm. Res., 9, p. 37Capece, M., Dave, R., (2011) Powder Technol., 211, p. 199Neumann, A.W., Good, R.J., Techniques of measuring contact angles (1979) Surface and Colloid Science, pp. 31-91. , InGood, R. J. Stromberg, R.R. Eds.;Plenum Press: New YorkCosta, P., Lobo, J.M.S., (2001) Eur. J. Pharm. Sci., 13, p. 123Nelder, J.A., Mead, R., (1965) Comput. J., 7, p. 308Wallrabe, H., Periasamy, A., (2005) Curr. Opin. Biotechnol., 16, p. 19Geldart, D., (1973) Powder Technol., 7, p. 285Massarani, G., (2002) Fluidodinâmica em Sistemas Particulados, , 2nd ed.Rio de Janeiro, E- Papers Serviços Editoriais Ltd Chapter 1 (in Portuguese)Hsu, S.-T., Yao, Y.L., (2013) J. Appl. Polym. Sci, 130, p. 4147Korsmeyer, R.W., Gurny, R., Doelker, E.M., Buri, P., Peppas, N.A., (1983) Int. J. Pharm., 15, p. 2