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Vol.2 , No. 1, Publication Date: Feb. 15, 2015, Page: 1-8
, 3.5 million tons corresponding to oranges. Worldwide, only Brazil, China and the US have a higher annual production, with 21, 19, and 10 million tons, respectively. This pruned fraction of orange tree is an important lignocellulosic resource and consists of leaves, bark and young branches. The most obvious application is in combustion processes, but it is also feasible to use in conversion processes to products with high added value, including the production of cellulose pulp by alternative methods. The aim of this work is the systematic study of valorisation of orange pruning by applying an organosolv method (ethanol) for the production of cellulose pulps by assessing the effect of the most influential treatment variables on the characteristics of the pulps by means of factorial designs and statistical analysis of the results. The values estimated by the polynomial models reproduce the experimental results of the different dependent variables, with errors less than 22%, while by the neurofuzzy models the error are less than 20%. Although neurofuzzy models use a greater number of parameters than second order polynomial models, both types of models are equally effective for a typical experimental design of three variables of operation, with a total of 15 experiments.&picture=http://www.aascit.org/aascit/decorators/img/journal/924.jpg)
| [1] | Zoilo González, Chemical Engineering Department, Campus of Rabanales, Building Marie Curie (C-3), University of Córdoba, Córdoba, Spain. |
| [2] | Alberto Vega, Department of Physical Chemistry and Chemical Engineering, University of A Coruña, A Coruña, Spain. |
| [3] | Pablo Ligero, Department of Physical Chemistry and Chemical Engineering, University of A Coruña, A Coruña, Spain. |
| [4] | Alejandro Rodríguez, Chemical Engineering Department, Campus of Rabanales, Building Marie Curie (C-3), University of Córdoba, Córdoba, Spain. |
Spain is the country with the largest citrus production in Europe, with about 5 million tons per year (2007), 3.5 million tons corresponding to oranges. Worldwide, only Brazil, China and the US have a higher annual production, with 21, 19, and 10 million tons, respectively. This pruned fraction of orange tree is an important lignocellulosic resource and consists of leaves, bark and young branches. The most obvious application is in combustion processes, but it is also feasible to use in conversion processes to products with high added value, including the production of cellulose pulp by alternative methods. The aim of this work is the systematic study of valorisation of orange pruning by applying an organosolv method (ethanol) for the production of cellulose pulps by assessing the effect of the most influential treatment variables on the characteristics of the pulps by means of factorial designs and statistical analysis of the results. The values estimated by the polynomial models reproduce the experimental results of the different dependent variables, with errors less than 22%, while by the neurofuzzy models the error are less than 20%. Although neurofuzzy models use a greater number of parameters than second order polynomial models, both types of models are equally effective for a typical experimental design of three variables of operation, with a total of 15 experiments.
Keywords
Orange Tree Pruning, Ethanol, Cellulose, Hemicelluloses, Polynomial Models
Reference
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| [02] | Muurinen, Esa. 2000. Organosolv pulping. A review and distillation study related to peroxyacid pulping. Doctoral Thesis, Faculty of Technology, Oulu University |
| [03] | FAOSTAT, http://faostat3.fao.org/home/E Accesed November 2014 |
| [04] | Jang, J.S.R., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23 (3), 665–685 |
| [05] | Jiménez, L., Rodríguez, A. 2010. Valorization of Agricultural Residues by Fractionation of their Components, 4, 125-134. |
| [06] | Rodríguez, A., Sánchez, R., Ferrer, A., Requejo, A. 2011. Simulation of Hesperaloe funifera diethanolamine pulping by polynomial and neural fuzzy models, 89,648-656. |
| [07] | Works, G.A., 1989. Neural network basics. In: Proceeding AUTOFACT’89, pp. 1–9. |
| [08] | Zadeh, L.A., 1965. Fuzzy sets. Inform. Control 8, 338–353. |
