Optimizing Activated Carbon Production from Waste Cashew Nut Shell with Zinc Chloride: A Box-Behnken Design and Group Method of Data Handling (GMDH) Application

Abstract

In this study, Response surface methodology (RSM) and innovative Group Method of Data Handling (GMDH) approaches are applied to investigate the optimal process conditions of zinc chloride activated cashew nut production process. The effects of activation conditions (i.e. activation temperature, activation time, and impregnation ratio) on the achievable BET surface areas were studied with the aid of Box Behnken Design (BBD) and GMDH. Comparative analyses of RSM and GMDH-type neural models were further researched. During the process, the polynomial model equations developed were modified and fine-tuned to predict the highest BET surface area(s) using regression analysis and GMDH multi-layered iterative algorithm (MIA). Analysis of Variance (ANOVA) revealed that the significant factor(s) were impregnation ratio, impregnation ratio product, and the 2-way interactions (activation temperature and impregnation ratio) for ZnCl2 activated cashew nut shell. The best activation conditions for producing highest BET surface area of 504 m2.g-1 was activation temperature (873K), activation time (60 min), and impregnation ratio (1.50).The proposed GMDH-type BET model was ascertained to be the best model with average correlation coefficient (R) and root mean square error (RMSE) of 0.925 and 32.0 respectively. Sensitivity analysis conducted for GMDH-type neural network also revealed that the activation temperature and activation time with sensitivity values of 90.6% and 74.1% respectively were the most influential parameters in the basic (ZnCl2) activation process. The results of this study show that RSM and GMDH-type neural network could be applied as effective analytical tools for optimizing the ZCNS (zinc chloride-activated cashew nut shells) manufacturing process.

Keywords

• Cheaper Agro-Wastes Derived Porous Carbons
• Box Behnken Design (BBD)
• Basic Activation
• Group Method of Data Handling
• Process Optimisation

References

1. Adeigbe, O. O., Olasupo, F. O., Adewale, B. D., & Muyiwa, A. A. (2015). A review on cashew research and production in Nigeria in the last four decades. Scientific Research and Essays, 10(5), 196–209. https://doi.org/10.5897/SRE2014.5953
2. AJEMBA, R. O. (2014). Kinetics and equilibrium modeling of lead (II) and chromium (III) ions’ adsorption onto clay from Kono-bowe, Nigeria. Turkish Journal of Engineering and Environmental Sciences, 38(3), 455–479.
3. Akinbiyi, A. O. (2001). Removal of lead from aqueous solutions by adsorption using peat moss. Faculty of Graduate Studies and Research, University of Regina.
4. Anderson, M. J., & Whitcomb, P. J. (2016). RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments, Second Edition (2nd ed.). Productivity Press.
5. Ayoub, Mohammed. A., Almansour, Abdullah. O., & Hassan, Anas. M. (2020). A Novel Formula for Estimating Oil Compressibility Below Bubble Point Pressure Using Group Method of Data Handling: A Comparative Approach. Day 1 Tue, October 29, 2019, D012S002R001. https://doi.org/10.2118/196446-MS
6. Buasri, A., Sirikoom, P., Pattane, S., Buachum, O., & Loryuenyong, V. (2023). Process Optimization of Biodiesel from Used Cooking Oil in a Microwave Reactor: A Case of Machine Learning and Box–Behnken Design. ChemEngineering, 7(4), 65. https://doi.org/10.3390/chemengineering7040065
7. Chebii, S. J., Mukolwe, M. M., & Ong’or, B. I. (2022). River flow modelling for flood prediction using artificial neural network in ungauged Perkerra catchment, Baringo County, Kenya. Water Practice and Technology, 17(4), 914–929. https://doi.org/10.2166/wpt.2022.034
8. Dawei, H. (2012). Concise Environmental Engineering. Ventus Publishing ApS.

9. Demirbas, E., Kobya, M., & Konukman, A. E. S. (2008). Error analysis of equilibrium studies for the almond shell activated carbon adsorption of Cr(VI) from aqueous solutions. Journal of Hazardous Materials, 154(1–3), 787–794. https://doi.org/10.1016/j.jhazmat.2007.10.094
10. Elibol, M. (2002). Response surface methodological approach for inclusion of perfluorocarbon in actinorhodin fermentation medium. Process Biochemistry, 38(5), 667–673. https://doi.org/10.1016/S0032-9592(02)00171-1
11. Essa, M. H., Zahrani, M. A. A., & Nesaratnam, S. (2013). Optimisation of activated carbon production from date pits. International Journal of Environmental Engineering, 5(3), 325. https://doi.org/10.1504/IJEE.2013.054708
12. Farlow, S. J. (1981). The GMDH Algorithm of Ivakhnenko. The American Statistician, 35(4), 210–215. https://doi.org/10.1080/00031305.1981.10479358
13. Gokcesu, K., & Gokcesu, H. (2023). A Note On Nonlinear Regression Under L2 Loss. https://doi.org/10.48550/ARXIV.2303.17745
14. Kehinde Ademola, Adesanya, Mutiat Oluwaseyi Agboola-Adedoja, Adejoke Adebusola Adelusi, Qudus Adebayo Ogunwolu, Chinweike Abednego Ugwu, Modinat Adewunmi Alli, & Ayodele Oladipo Akinpelu. (2021). Opportunities in Nigerian cashew nut value chain. World Journal of Advanced Research and Reviews, 9(1), 168–174. https://doi.org/10.30574/wjarr.2021.9.1.0008
15. Kulkarni, S. J., & Kaware, J. (2014). Removal of phenol from effluent in fixed bed: A review. International Journal of Engineering Research and General Science, 2(5), 35–38.
16. Li, R. Y. M., Fong, S., & Chong, K. W. S. (2017). Forecasting the REITs and stock indices: Group Method of Data Handling Neural Network approach. Pacific Rim Property Research Journal, 23(2), 123–160. https://doi.org/10.1080/14445921.2016.1225149
17. Madala, H. R., & Ivakhnenko, A. G. (1994). Inductive learning algorithms for complex systems modeling. CRC Press.
18. Malik, D. S., Jain, C. K., & Yadav, A. K. (2017). Removal of heavy metals from emerging cellulosic low-cost adsorbents: A review. Applied Water Science, 7(5), 2113–2136. https://doi.org/10.1007/s13201-016-0401-8
19. Mathew Nkurlu, B., Shen, C., Asante-Okyere, S., Mulashani, A. K., Chungu, J., & Wang, L. (2020). Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data. Energies, 13(3), 551. https://doi.org/10.3390/en13030551
20. Montgomery, D. C. (2017). Design and analysis of experiments (Ninth edition). Wiley.
21. Nwabanne, J. T., & Okoye, A. C. (2013). Treatment of synthetic and battery industry wastewater by electrocoagulation. Der Chemica Sinica, 4(6), 32–39.
22. Okewale, A., Igbokwe, P., & Adesina, O. (2015). Optimization of the adsorptive dehydration of ethanol–water system. Optimization, 39.
23. Okiy, K. V. (2006). Scale-up of an electrochemical reactor for the removal of copper ions from wastewater [B. Sc. Thesis]. University of Lagos.
24. Onu, C. E., Nwabanne, J. T., Ohale, P. E., & Asadu, C. O. (2021). Comparative analysis of RSM, ANN and ANFIS and the mechanistic modeling in eriochrome black-T dye adsorption using modified clay. South African Journal of Chemical Engineering, 36, 24–42. https://doi.org/10.1016/j.sajce.2020.12.003
25. Özer, A., Gürbüz, G., Çalimli, A., & Körbahti, B. K. (2009). Biosorption of copper(II) ions on Enteromorpha prolifera: Application of response surface methodology (RSM). Chemical Engineering Journal, 146(3), 377–387. https://doi.org/10.1016/j.cej.2008.06.041
26. Palani, S., Liong, S.-Y., Tkalich, P., & Palanichamy, J. (2009). Development of a neural network model for dissolved oxygen in seawater. Indian Journal of Geo-Marine Sciences, 38(2), 151–159.
27. Peavy, H. S., Rowe, D. R., & Tchobanoglous, G. (1985). Environmental engineering. McGraw-Hill.
28. S, M. S., M, E. A. A., & Chidambaram, R. (2015). Isotherm Modelling, Kinetic Study and Optimization of Batch Parameters Using Response Surface Methodology for Effective Removal of Cr(VI) Using Fungal Biomass. PLOS ONE, 10(3), e0116884. https://doi.org/10.1371/journal.pone.0116884
29. Senthil Kumar, P., Ramalingam, S., Abhinaya, R. V., Kirupha, S. D., Murugesan, A., & Sivanesan, S. (2012). Adsorption of Metal Ions onto the Chemically Modified Agricultural Waste. CLEAN – Soil, Air, Water, 40(2), 188–197. https://doi.org/10.1002/clen.201100118
30. Singh, R., Gautam, N., Mishra, A., & Gupta, R. (2011). Heavy metals and living systems: An overview. Indian Journal of Pharmacology, 43(3), 246. https://doi.org/10.4103/0253-7613.81505
31. Srivastava, S., Agrawal, S. B., & Mondal, M. K. (2015). A review on progress of heavy metal removal using adsorbents of microbial and plant origin. Environmental Science and Pollution Research, 22(20), 15386–15415. https://doi.org/10.1007/s11356-015-5278-9
32. Stavropoulos, G. G., & Zabaniotou, A. A. (2009). Minimizing activated carbons production cost. Fuel Processing Technology, 90(7–8), 952–957. https://doi.org/10.1016/j.fuproc.2009.04.002
33. Subramaniam, R., & Kumar Ponnusamy, S. (2015). Novel adsorbent from agricultural waste (cashew NUT shell) for methylene blue dye removal: Optimization by response surface methodology. Water Resources and Industry, 11, 64–70. https://doi.org/10.1016/j.wri.2015.07.002
34. Van Dyk, L. D. (2000). The production of granular activated carbon from agricultural waste products [MSc Thesis, Stellenbosch University]. https://scholar.sun.ac.za/items/388fda93-974e-4e4d-9562-0e78cc0f8c31
35. Voss, M. S. (2002). The Group Method for Cartesian Programming: A New Methodology for Complex Adaptive Functional Networks [PhD Thesis, Marquette University]. https://epublications.marquette.edu/cgi/viewcontent.cgi?article=2797&context=dissertations_mu
36. Wang, Z., Wu, Q., Zhang, J., Zhang, H., Feng, J., Dong, S., & Sun, J. (2019). In situ polymerization of magnetic graphene oxide-diaminopyridine composite for the effective adsorption of Pb(II) and application in battery industry wastewater treatment. Environmental Science and Pollution Research, 26(32), 33427–33439. https://doi.org/10.1007/s11356-019-06511-1
37. Xu, Z., Cai, J., & Pan, B. (2013). Mathematically modeling fixed-bed adsorption in aqueous systems. Journal of Zhejiang University SCIENCE A, 14(3), 155–176. https://doi.org/10.1631/jzus.A1300029
38. Yi, Q. (2009). Point Sources of Pollution: Local Effects and their Control—Volume II. EOLSS Publications. https://books.google.com.tr/books?id=dq2kCwAAQBAJ