Optimization of Thermal Decomposition Conditions of Bone to Achieve the Highest Percentage of Crystalline Phase in Bone Char using Gene Expression Programming and Artificial Neural Network

Document Type : Original Article

Authors

Department of Materials Science and Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract

Bone char (BC) is one of the most common adsorbent with extensive applications in the removal of pollutions. The adsorption capability of BC is proportional to the crystalline index, i.e, the atomic ratio of Ca/P. This study is an attempt to model the crystalline index of BC that by thermal decomposition of natural bone using artificial neural network (ANN) and genetic expression programming (GEP). In this regard, 100 various experimental data used to construct the ANN and GEP models, separately. Through the data collection step, heating rate, the type of precursor, calcination temperature, and residence time selected as the inputs for the preset output as Ca/P ratio. The results reveal that the minimum amount of Ca/P ratio are at the heating rate 10 °C/min, HNO3 1.6 M as activation agent, calcination temperature 1000 °C, and residence time 2 h. R squared indices is used to compare the performance of extracted models. Finally, the best ANN uses to investigate the effect of each practical variable by sensitivity analysis and revealed that the residence time is the most effective parameter on the crystalline index while acid activation is of secondary importance.

Keywords

References

1.     Alkurdi, S.S., Al-Juboori, R.A., Bundschuh, J., Bowtell, L. and McKnight, S., "Effect of pyrolysis conditions on bone char characterization and its ability for arsenic and fluoride removal", Environmental Pollution,  Vol. 262, (2020), 114221. https://doi.org/10.1016/j.envpol.2020.114221
2.     Leinweber, P., Hagemann, P., Kebelmann, L., Kebelmann, K. and Morshedizad, M., Bone char as a novel phosphorus fertilizer, In Phosphorus recovery and recycling. 2019, Springer. 419-432. https://doi.org/10.1007/978-981-10-8031-9_29
3.     Dotto, G.L., Salau, N.P.G., Piccin, J.S., Cadaval, T.R.S.A. and de Pinto, L.A.A., Adsorption kinetics in liquid phase: Modeling for discontinuous and continuous systems, In Adsorption processes for water treatment and purification. 2017, Springer. 53-76.
4.     de Melo, N.H., de Oliveira Ferreira, M.E., Neto, E.M.S., Martins, P.R. and Ostroski, I.C., "Evaluation of the adsorption process using activated bone char functionalized with magnetite nanoparticles", Environmental Nanotechnology, Monitoring & Management, Vol. 10, (2018), 427-434. https://doi.org/10.1016/j.enmm.2018.10.005
5.     Wang, M., Liu, Y., Yao, Y., Han, L. and Liu, X., "Comparative evaluation of bone chars derived from bovine parts: Physicochemical properties and copper sorption behavior", Science of The Total Environment,  Vol. 700, (2020), 134470. https://doi.org/10.1016/j.scitotenv.2019.134470
6.     Dalvand, H., Khayati, G.R., Darezereshki, E. and Irannejad, A., "A facile fabrication of nio nanoparticles from spent ni–cd batteries", Materials Letters,  Vol. 130, (2014), 54-56. https://doi.org/10.1016/j.matlet.2014.05.057
7.     Medellin-Castillo, N.A., Padilla-Ortega, E., Tovar-García, L.D., Leyva-Ramos, R., Ocampo-Pérez, R., Carrasco-Marín, F. and Berber-Mendoza, M.S.J.A., "Removal of fluoride from aqueous solution using acid and thermally treated bone char", Adsorption, Vol. 22, No. 7, (2016), 951-961. https://doi.org/10.1007/s10450-016-9802-0
8.     Zhao, J., Zhao, J., Chen, J., Wang, X., Han, Z. and Li, Y., "Rietveld refinement of hydroxyapatite, tricalcium phosphate and biphasic materials prepared by solution combustion method", Ceramics International,  Vol. 40, No. 2, (2014), 3379-3388. https://doi.org/10.1016/j.ceramint.2013.09.094
9.     Levinskas, G.J. and Neuman, W.F., "The solubility of bone mineral. I. Solubility studies of synthetic hydroxylapatite", The Journal of Physical Chemistry,  Vol. 59, No. 2, (1955), 164-168. https://doi.org/10.1021/j150524a017
10.   Cheung, C.W., Chan, C.K., Porter, J.F. and McKay, G., "Combined diffusion model for the sorption of cadmium, copper, and zinc ions onto bone char", Environmental Science & Technology,  Vol. 35, No. 7, (2001), 1511-1522. https://doi.org/10.1021/es0012725
11.   Medellin-Castillo, N.A., Leyva-Ramos, R., Ocampo-Perez, R., Garcia de la Cruz, R.F., Aragon-Pina, A., Martinez-Rosales, J.M., Guerrero-Coronado, R.M. and Fuentes-Rubio, L., "Adsorption of fluoride from water solution on bone char", Industrial & Engineering Chemistry Research,  Vol. 46, No. 26, (2007), 9205-9212. https://doi.org/10.1021/ie070023n
12.   Warren, G., Robinson, J. and Someus, E., "Dissolution of phosphorus from animal bone char in 12 soils", Nutrient Cycling in Agroecosystems,  Vol. 84, No. 2, (2009), 167-178. https://doi.org/10.1007/s10705-008-9235-6
13.   Siebers, N. and Leinweber, P., "Bone char: A clean and renewable phosphorus fertilizer with cadmium immobilization capability", Journal of Environmental Quality,  Vol. 42, No. 2, (2013), 405-411. https://doi.org/10.2134/jeq2012.0363
14.   Medellin-Castillo, N., Leyva-Ramos, R., Padilla-Ortega, E., Perez, R.O., Flores-Cano, J. and Berber-Mendoza, M., "Adsorption capacity of bone char for removing fluoride from water solution. Role of hydroxyapatite content, adsorption mechanism and competing anions", Journal of industrial and Engineering Chemistry,  Vol. 20, No. 6, (2014), 4014-4021. https://doi.org/10.1016/j.jiec.2013.12.105
15.   Rojas-Mayorga, C.K., Silvestre-Albero, J., Aguayo-Villarreal, I.A., Mendoza-Castillo, D.I. and Bonilla-Petriciolet, A., "A new synthesis route for bone chars using co2 atmosphere and their application as fluoride adsorbents", Microporous and Mesoporous Materials,  Vol. 209, (2015), 38-44. https://doi.org/10.1016/j.micromeso.2014.09.002
16.   Zwetsloot, M.J., Lehmann, J. and Solomon, D., "Recycling slaughterhouse waste into fertilizer: How do pyrolysis temperature and biomass additions affect phosphorus availability and chemistry?", Journal of the Science of Food and Agriculture,  Vol. 95, No. 2, (2015), 281-288. https://doi.org/10.1002/jsfa.6716
17.   Zwetsloot, M.J., Lehmann, J., Bauerle, T., Vanek, S., Hestrin, R. and Nigussie, A., "Phosphorus availability from bone char in a p-fixing soil influenced by root-mycorrhizae-biochar interactions", Plant and Soil,  Vol. 408, No. 1-2, (2016), 95-105. https://doi.org/10.1007/s11104-016-2905-2
18.   Morshedizad, M., Zimmer, D. and Leinweber, P., "Effect of bone chars on phosphorus‐cadmium‐interactions as evaluated by three extraction procedures", Journal of Plant Nutrition and Soil Science,  Vol. 179, No. 3, (2016), 388-398. https://doi.org/10.1002/jpln.201500604
19.   Flores-Cano, J.V., Leyva-Ramos, R., Carrasco-Marin, F., Aragón-Piña, A., Salazar-Rabago, J.J. and Leyva-Ramos, S., "Adsorption mechanism of chromium (iii) from water solution on bone char: Effect of operating conditions", Adsorption,  Vol. 22, No. 3, (2016), 297-308. https://doi.org/10.1007/s10450-016-9771-3
20.   Iriarte-Velasco, U., Sierra, I., Zudaire, L. and Ayastuy, J.L., "Preparation of a porous biochar from the acid activation of pork bones", Food and Bioproducts Processing,  Vol. 98, No., (2016), 341-353. https://doi.org/10.1016/j.fbp.2016.03.003
21.   Robinson, J.S., Baumann, K., Hu, Y., Hagemann, P., Kebelmann, L. and Leinweber, P., "Phosphorus transformations in plant-based and bio-waste materials induced by pyrolysis", Ambio,  Vol. 47, No. 1, (2018), 73-82. https://doi.org/10.1007/s13280-017-0990-y
22.   Jia, P., Tan, H., Liu, K. and Gao, W., "Synthesis and photocatalytic performance of zno/bone char composite", Materials, Vol. 11, No. 10, (2018), 1981. https://doi.org/10.3390/ma11101981
23.   Koza, J.R. and Koza, J.R., "Genetic programming: on the programming of computers by means of natural selection" (Vol. 1), MIT press, 1992.
24.   de Castilho, V.C., El Debs, M.K. and do Carmo Nicoletti, M., "Using a modified genetic algorithm to minimize the production costs for slabs of precast prestressed concrete joists", Engineering Applications of Artificial Intelligence, Vol. 20, No. 4, (2007), 519-530. https://doi.org/10.1016/j.engappai.2006.09.003
25.   Ferreira, C., "Gene expression programming: A new adaptive algorithm for solving problems", Complex Systems, Vol. 13, No. 2, (2001), 87-129. arXiv:cs/0102027
26.   Cheng, M.-Y., Firdausi, P.M. and Prayogo, D.J.E.A.o.A.I., "High-performance concrete compressive strength prediction using genetic weighted pyramid operation tree (gwpot)", Engineering Applications of Artificial Intelligence, Vol. 29, No., (2014), 104-113. https://doi.org/10.1016/j.engappai.2013.11.014
27.   Ferreira, C., "Gene expression programming: Mathematical modeling by an artificial intelligence, Springer,  Vol. 21,  (2006).
28.   Ong, C.S., Huang, J.J. and Tzeng, G.H., "Building credit scoring models using genetic programming". Expert Systems with Applications, Vol. 29, No. 1, (2005), 41-47. https://doi.org/10.1016/j.eswa.2005.01.003
29.   Mollahasani, A., Alavi, A.H., Gandomi, A.H.J.C. and Geotechnics, "Empirical modeling of plate load test moduli of soil via gene expression programming",  Vol. 38, No. 2, (2011), 281-286. https://doi.org/10.1016/j.compgeo.2010.11.008
30.   Hosseini, M., Khayati, G.R., Mahdavi, M. and Danesh-Manesh, H., "Modeling and optimization of roll-bonding parameters for bond strength of ti/cu/ti clad composites by artificial neural networks and genetic algorithm", International Journal of Engineering, Transactions C: Aspects,  Vol. 30, No. 12, (2017), 1885-1893. https://doi.org/10.5829/ije.2017.30.12c.10
31.   Si, H.Z., Wang, T., Zhang, K.J., De Hu, Z., Fan, B.T.J.B. and Chemistry, M., "Qsar study of 1, 4-dihydropyridine calcium channel antagonists based on gene expression programming", Bioorganic & Medicinal Chemistry, Vol. 14, No. 14, (2006), 4834-4841. https://doi.org/10.1016/j.bmc.2006.03.019
32.   Guessasma, S., Montavon, G. and Coddet, C., "Neural computation to predict in-flight particle characteristic dependences from processing parameters in the aps process", Journal of Thermal Spray Technology,  Vol. 13, No. 4, (2004), 570-585. https://doi.org/10.1361/10599630419391
33.   Austin, N., Kumar, P.S. and Kanthavelkumaran, N.,, "Artificial neural network involved in the action of optimum mixed refrigerant (domestic refrigerator)", International Journal of Engineering-Transactions A: Basics, Vol., No., (2013), 1235-1242. https://doi.org/10.5829/idosi.ije.2013.26.10a.13
34.   Pradeep, J., Srinivasan, E. and Himavathi, S., "Neural network based recognition system integrating feature extraction and classification for english handwritten", International Journal of Engineering, TransactionsB: Applications, Vol. 25, No. 2, (2012), 99-106. https://doi.org/10.5829/idosi.ije.2012.25.02b.03
35.   Khanmohammadi, S., "Neural network modelling of optimal robot movement using branch and bound tree", International Journal of Engineering,  Vol. 7, No. 2, (1994), 95-110. Retrieved from: http://www.ije.ir/article_71101_2db276aad3a417e1836645f7e4b24984.pdf
36.   Mahdavi, M. and Khayati, G.R., "Artificial neural network based prediction hardness of al2024-multiwall carbon nanotube composite prepared by mechanical alloying", International Journal of Engineering, Transactions C: Aspects, Vol. 29, No. 12, (2016), 1726-1733. https://doi.org/10.5829/idosi.ije.2016.29.12c.11
37.   Neshat, E. and Saray, R.K., "An optimized chemical kinetic mechanism for hcci combustion of prfs using multi-zone model and genetic algorithm", Energy Conversion and Management,  Vol. 92, No., (2015), 172-183. https://doi.org/10.1016/j.enconman.2014.11.057
 
 
International Journal of Engineering
Volume 34, Issue 1
TRANSACTIONS A: Basics
January 2021
Pages 184-194

Files

Cited by

Share

How to cite

Statistics