Home Response surface methodology (RSM) and artificial neural network (ANN) approach to optimize the photocatalytic conversion of rice straw hydrolysis residue (RSHR) into vanillin and 4-hydroxybenzaldehyde
Article
Licensed
Unlicensed Requires Authentication

Response surface methodology (RSM) and artificial neural network (ANN) approach to optimize the photocatalytic conversion of rice straw hydrolysis residue (RSHR) into vanillin and 4-hydroxybenzaldehyde

  • Kaleem Ahmad , Himadri Roy Ghatak EMAIL logo and S. M. Ahuja
Published/Copyright: June 21, 2022
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Chemical Product and Process Modeling
From the journal Chemical Product and Process Modeling Volume 18 Issue 3

Abstract

Effective use of waste lignin is always a challenging task, technologies have been applied in the past to get value-added compounds from waste lignin. However, the existing technologies are not economical and efficient to produce the value-added chemicals. Alkali soluble lignin from rice straw hydrolysis residue (RSHR) is subjected to photocatalytic conversion into value-added compounds. Photocatalysis is one of the multifarious advanced oxidation processes (AOPs), carried out with TiO2 nanoparticles under a 125 W UV bulb. Gas chromatography mass spectroscopy (GCMS) confirmed the formation of vanillin and 4-hydroxybenzaldehyde. RSM and ANN techniques are adopted to optimize the process conditions for the maximization of the products. The response one (Y 1) vanillin (24.61 mg) and second response (Y 2) 4-hydroxybenzaldehyde (19.51 mg) is obtained at the optimal conditions as 7.0 h irradiation time, 2.763 g/L catalyst dose, 15 g/L lignin concentration, and 14.26 g/L NaOH dose for alkali treatment, suggested by face-centered central composite design (CCD). RSM and ANN models are statistically analyzed in terms of RMSE, R 2 and AAD. For RSM the R 2 0.9864 and 0.9787 while for ANN 0.9875 and 0.9847, closer to one warrant the good fitting of the models. Therefore, in terms of higher precision and predictive ability of both models the ANN model showed excellence for both responses as compared to the RSM model.

Keywords: 4-hydroxybenzaldehyde; artificial neural network (ANN); photocatalysis; rice straw hydrolysis residue (RSHR); RSM; vanillin
Corresponding author: Himadri Roy Ghatak, Department of Chemical Engineering, Sant Longowal Institute of Engineering and Technology, Longowal 148106, Punjab, India, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

1. Zhang, X, Tu, MB, Paice, MG. Routes to potential bioproducts from lignocellulosic biomass lignin and hemicelluloses. Bioenergy Res 2011;4:246–57. https://doi.org/10.1007/s12155-011-9147-1.Search in Google Scholar

2. Pouteau, C, Dole, P, Cathala, B, Averous, L, Boquillon, N. Antioxidant properties of lignin in polypropylene. Polym Degrad Stabil 2003;81:9–18. https://doi.org/10.1016/s0141-3910(03)00057-0.Search in Google Scholar

3. Medina, JD, Woiciechowski, A, Zandona Filho, A, Noseda, MD, Kaur, BS, Soccol, CR. Lignin preparation from oil palm empty fruit bunches by sequential acid/alkaline treatment - a biorefinery approach. Bioresour Technol 2015;194:172–8. https://doi.org/10.1016/j.biortech.2015.07.018.Search in Google Scholar PubMed

4. Isikgor, FH, Becer, CR. Lignocellulosic biomass: a sustainable platform for the production of bio-based chemicals and polymers. Polym Chem 2015;6:4497–559. https://doi.org/10.1039/c5py00263j.Search in Google Scholar

5. Malherbe, S, Cloete, TE. Lignocellulose biodegradation: fundamentals and applications. Rev Environ Sci Biotechnol 2002;1:105–14. https://doi.org/10.1023/a:1020858910646.10.1023/A:1020858910646Search in Google Scholar

6. Tian, M, Wen, J, MacDonald, D, Asmussen, RM, Chen, A. A novel approach for lignin modification and degradation. Electrochem Commun 2010;12:527–30. https://doi.org/10.1016/j.elecom.2010.01.035.Search in Google Scholar

7. Wei, Z, Spinney, R, Ke, R, Yang, Z, Ziao, R. Effect of pH on the sonochemical degradation of organic pollutants. Environ Chem Lett 2016;14:163–182. https://doi.org/10.1007/s10311-016-0557-3.Search in Google Scholar

8. Praveckova, M, Brennerova, MV, Holliger, C, De Alencastro, F, Rossi, P. Indirect evidence link PCB dehalogenation with geobacteraceae in anaerobic sediment-free microcosms. Front Microbiol 2016:7–933. https://doi.org/10.3389/fmicb.2016.00933.Search in Google Scholar PubMed PubMed Central

9. Shah, AL, Hasan, F, Hameed, A, Ahmad, S. Biological degradation of plastics: a comprehensive review. Biotechnol Adv 2008;26:246–65. https://doi.org/10.1016/j.biotechadv.2007.12.005.Search in Google Scholar PubMed

10. Ahmad, K, Ghatak, HR, Ahuja, SM. A review on photocatalytic remediation of environmental pollutants and H2 production through water splitting: a sustainable approach. Environ Technol Innovat 2020;19:1–28. https://doi.org/10.1016/j.eti.2020.100893.Search in Google Scholar

11. Nosaka, Y, Nosaka, A. Introduction to photocatalysis – from basic science to applications. Cambridge, UK: Royal Society of Chemistry; 2016.10.1039/9781839168918Search in Google Scholar

12. Rajamanickam, D, Shanthi, M. Photocatalytic degradation of an organic pollutant by zinc oxide–solar process. Arab J Chem 2016;9:1858–68. https://doi.org/10.1016/j.arabjc.2012.05.006.Search in Google Scholar

13. Kobayakawa, K, Yuichi, S, Shigeo, N, Akira, F. Photodecomposition of kraft lignin catalyzed by titanium dioxide. Bull Chem Soc Jpn 1989;62:3433–6. https://doi.org/10.1246/bcsj.62.3433.Search in Google Scholar

14. Li, H, Lei, Z, Liu, C, Zhang, Z, Lu, B. Photocatalytic degradation of lignin on synthesized Ag–AgCl/ZnO nanorods under solar light and preliminary trials for methane Fermentation. Bioresour Technol 2015;175:494–501. https://doi.org/10.1016/j.biortech.2014.10.143.Search in Google Scholar PubMed

15. Ksibi, M, Ben, S, Cherif, S, Elaloui, E, Houas, A, Elaloui, M. Photodegradation of lignin from black liquor using a UV/TiO2 system. J Photochem Photobiol Chem 2003;54:211–8. https://doi.org/10.1016/s1010-6030(02)00316-7.Search in Google Scholar

16. Ma, YS, Chang, CN, Chiang, YP, Sung, HF, Chao, AC. Photocatalytic degradation of lignin using Pt/TiO2 as the catalyst. Chemosphere 2008;71:998–1004. https://doi.org/10.1016/j.chemosphere.2007.10.061.Search in Google Scholar PubMed

17. Prado, R, Erdocia, X, Labidi, J. Effect of the photocatalytic activity of TiO2 on lignin depolymerization. Chemosphere 2013;91:1355–61. https://doi.org/10.1016/j.chemosphere.2013.02.008.Search in Google Scholar PubMed

18. Lekelefac, CA, Busse, N, Herrenbauer, M, Czermak, P. Photocatalytic based degradation processes of lignin derivatives. Int J Photoenergy 2015;2015:1–18. https://doi.org/10.1155/2015/137634.Search in Google Scholar

19. Cao, Y, Chen, SS, Zhang, S, Sikok, Y, Matsagar, BM, Wu, KC-W, et al.. Advances in lignin valorization towards bio-based chemicals and fuels: lignin biorefinery. Bioresour Technol 2019;291:121878. https://doi.org/10.1016/j.biortech.2019.121878.Search in Google Scholar PubMed

20. Kansal, SK, Singh, M, Sud, D. Optimization of process parameters for the photocatalytic degradation of 2, 4-dichlorophenol in aqueous solutions. Int J Chem React Eng 2009;7:1–24. https://doi.org/10.2202/1542-6580.1839.Search in Google Scholar

21. Rai, A, Mohanty, B, Bhargava, R. Supercritical extraction of sunflower oil: a central composite design for extraction variables. Food Chem 2016;192:647–59. https://doi.org/10.1016/j.foodchem.2015.07.070.Search in Google Scholar PubMed

22. Farouq, R, Abd-Elfatah, M, Ossman, ME. Response surface methodology for optimization of photocatalytic degradation of aqueous ammonia. J Water Supply Res Technol 2018;67:162–75. https://doi.org/10.2166/aqua.2018.121.Search in Google Scholar

23. Yang, H, Zhou, S, Liu, H, Yan, W, Yang, L, Yi, B. Photocatalytic degradation of carbofuran in TiO2 aqueous solution: kinetics using design of experiments and mechanism by HPLC/MS/MS. J Environ Sci 2013;25:1680–6. https://doi.org/10.1016/s1001-0742(12)60217-4.Search in Google Scholar PubMed

24. Khoshnamvand, N, Mostafapour, FK, Mohammadi, A, Faraji, M. Response surface methodology (RSM) modeling to improve removal of ciprofloxacin from aqueous solutions in photocatalytic process using copper oxide nanoparticles (CuO/UV). Amb Express 2018;8:48. https://doi.org/10.1186/s13568-018-0579-2.Search in Google Scholar PubMed PubMed Central

25. Quah, T, Machalek, D, Powell, KM. Comparing reinforcement learning methods for real-time optimization of a chemical process. Processes 2020;8:1497. https://doi.org/10.3390/pr8111497.Search in Google Scholar

26. Ameer, K, Chun, BS, Kwon, JH. Optimization of supercritical fluid extraction of steviol glycosides and total phenolic content from Stevia rebaudiana (Bertoni) leaves using response surface methodology and artificial neural network modeling. Ind Crop Prod 2017;109:672–85. https://doi.org/10.1016/j.indcrop.2017.09.023.Search in Google Scholar

27. Said, FM, Gan, JY, Sulaiman, J. Correlation between response surface methodology and artificial neural network in the prediction of bioactive compounds of unripe Musa acuminata peel. Eng Sci Technol Int J 2020;23:781–7. https://doi.org/10.1016/j.jestch.2019.12.005.Search in Google Scholar

28. Ahmad, K, Ghatak, HR, Ahuja, SM. Kinetics of producing vanillin and 4-hydroxy benzaldehyde from the hydrolysis residue of rice straw by photocatalysis. React Kinet Mech Catal 2020;131:383–95. https://doi.org/10.1007/s11144-020-01840-6.Search in Google Scholar

29. Henderson, M, Henderson, MA. A Surface Science Perspective on TiO2 Photocatalysis A surface science perspective on TiO2 photocatalysis. Surf Sci Rep 2016;66:185–297. https://doi.org/10.1016/j.surfrep.2011.01.001.Search in Google Scholar

30. Lee, KM, Hamid, SBA. Simple response surface methodology: investigation on advance photocatalytic oxidation of 4-Chlorophenoxyacetic acid using UV-Active ZnO photocatalyst. Materials 2015;8:339–54. https://doi.org/10.3390/ma8010339.Search in Google Scholar PubMed PubMed Central

31. Hinkelmann, K, Kempthorne, O. Design and analysis of experiments. Vol. 2: Advanced Experimental Design. Hoboken, NJ: Wiley; 2005, 1:497–531 pp.10.1002/0471709948Search in Google Scholar

32. Ciric, A, Krajnc, B, Heath, D, Ogrinc, N. Response surface methodology and artificial neural network approach for the optimization of ultrasound-assisted extraction of polyphenols from garlic. Food Chem Toxicol 2019;135:110976. https://doi.org/10.1016/j.fct.2019.110976.Search in Google Scholar PubMed

33. Okuda, K, Umetsu, M, Takami, S, Adschiri, T. Disassembly of lignin and chemical recovery—rapid depolymerization of lignin without char formation in water–phenol mixtures. Fuel Process Technol 2004;85:803–13. https://doi.org/10.1016/j.fuproc.2003.11.027.Search in Google Scholar

34. Singh, S, Ghatak, HR. Vanillin formation by electrooxidation of lignin on stainless steel anode: kinetics and byproducts. J Wood Chem Technol 2017;37:407–22. https://doi.org/10.1080/02773813.2017.1310899.Search in Google Scholar

35. Lalman, JA, Shewa, WA. Microbial fuel cell for generating electricity, and process for producing feedstock chemicals. US20160064758A1; 2016.Search in Google Scholar

36. Doong, RA, Chen, CH, Maithreepala, RA, Chang, SM. The influence of pH and cadmium sulfide on the photocatalytic degradation of 2-chlorophenol in titanium dioxide suspensions. Water Res 2001;35:2873–80. https://doi.org/10.1016/s0043-1354(00)00580-7.Search in Google Scholar PubMed

37. Liu, X, Duan, X, Wei, W, Wang, S, Ni, BJ. Photocatalytic conversion of lignocellulosic biomass to valuable products. Green Chem 2019;21–16:4266–89. https://doi.org/10.1039/c9gc01728c.Search in Google Scholar

38. Dahm, A, Lucia, LA. Titanium dioxide catalyzed photodegradation of lignin in industrial effluents. Ind Eng Chem Res 2004;43:7996–8000. https://doi.org/10.1021/ie0498302.Search in Google Scholar

39. Kansal, SK, Singh, M, Sud, D. Studies on TiO2/ZnO photocatalysed degradation of lignin. J Hazard Mater 2008;153:412–7. https://doi.org/10.1016/j.jhazmat.2007.08.091.Search in Google Scholar PubMed

40. Tanaka, K, Calanag, RCR, Hisanaga, T. Photocatalyzed degradation of lignin on TiO2. J Mol Catal Chem 1999;138:287–94. https://doi.org/10.1016/s1381-1169(98)00161-7.Search in Google Scholar

41. Neppolian, B, Choi, HC, Shankar, MV, Arabindoo, B, Murugesan, V. Semiconductor-assisted photodegradation of textile dye, reactive red 2 by ZnO in aqueous solution. In: Proceedings of international symposium on environmental pollution control and waste management (EPCOWM’2002), Tunis; 2002:647–53 p.Search in Google Scholar

42. Miyata, Y, Miyazaki, K. Solventless delignification of wood flour with TiO2/poly (ethylene oxide) photocatalyst system. J Polym Environ 2013;21:115–21. https://doi.org/10.1007/s10924-012-0465-y.Search in Google Scholar

43. Hofstadler, K, Bauer, R, Novalic, S, Heisier, SG. New reactor design for photocatalytic wastewater treatment with TiO2 immobilized on fused-silica glass fibres: photo mineralisation of 4-Chlorophenol. Environ Sci Technol 1994;28:670–4. https://doi.org/10.1021/es00053a021.Search in Google Scholar PubMed

44. Villasenor, J, Mansilla, HD. Effect of temperature on kraft black liquor degradation by ZnO-photoassisted catalysis. J Photochem Photobiol, A 1996;93:205–9. https://doi.org/10.1016/1010-6030(95)04179-6.Search in Google Scholar

45. Parkhey, P, Ram, AK, Diwan, B, Eswari, JS, Gupta, P. Artificial neural network and response surface methodology: a comparative analysis for optimizing rice straw pretreatment and saccharification. Prep Biochem Biotechnol 2020;768–80. https://doi.org/10.1080/10826068.2020.1737816.Search in Google Scholar PubMed

46. Das, A, Golder, AK, Das, C. Enhanced extraction of rebaudioside-a experimental, response surface optimization and prediction using artificial neural network. Ind Crop Prod 2015;65:415–21. https://doi.org/10.1016/j.indcrop.2014.11.006.Search in Google Scholar

47. Kashyap, P, Riar, CS, Jindal, N. Optimization of ultrasound-assisted extraction of polyphenols from Meghalayan cherry fruit (Prunus nepalensis) using response surface methodology (RSM) and artificial neural network (ANN) approach. J Food Meas Char 2020;15:119–33. https://doi.org/10.1007/s11694-020-00611-0.Search in Google Scholar

48. Garg, A, Kaur, G, Sangal, VK, Bajpai, PK, Upadhyay, S. Optimization methodology based on neural networks and box-Behnken design applied to photocatalysis of acid red 114 dye. Environ Eng Res 2020;25:753–62. https://doi.org/10.4491/eer.2019.246.Search in Google Scholar
Received: 2022-01-04
Accepted: 2022-05-27
Published Online: 2022-06-21

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Two-stage adsorber optimization of NaOH-prewashed oil palm empty fruit bunch activated carbon for methylene blue removal
  4. Response surface methodology (RSM) and artificial neural network (ANN) approach to optimize the photocatalytic conversion of rice straw hydrolysis residue (RSHR) into vanillin and 4-hydroxybenzaldehyde
  5. Computational investigation of erosion wear in the eco-friendly disposal of the fly ash through 90° horizontal bend of different radius ratios
  6. Optimal sequencing of conventional distillation column train for multicomponent separation system by evolutionary algorithm
  7. Enhanced design of PID controller and noise filter for second order stable and unstable processes with time delay
  8. Removal of glycerol from biodiesel using multi-stage microfiltration membrane system: industrial scale process simulation
  9. Multi-objective optimization of a fluid catalytic cracking unit using response surface methodology
  10. Effect of pipe rotation on heat transfer to laminar non-Newtonian nanofluid flowing through a pipe: a CFD analysis
  11. Statistical modeling and optimization of the bleachability of regenerated spent bleaching earth using response surface methodology and artificial neural networks with genetic algorithm
  12. Short Communication
  13. A comparative study: conventional and modified serpentine micromixers
https://doi.org/10.1515/cppm-2022-0003
Keywords for this article
4-hydroxybenzaldehyde; artificial neural network (ANN); photocatalysis; rice straw hydrolysis residue (RSHR); RSM; vanillin

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Two-stage adsorber optimization of NaOH-prewashed oil palm empty fruit bunch activated carbon for methylene blue removal
  4. Response surface methodology (RSM) and artificial neural network (ANN) approach to optimize the photocatalytic conversion of rice straw hydrolysis residue (RSHR) into vanillin and 4-hydroxybenzaldehyde
  5. Computational investigation of erosion wear in the eco-friendly disposal of the fly ash through 90° horizontal bend of different radius ratios
  6. Optimal sequencing of conventional distillation column train for multicomponent separation system by evolutionary algorithm
  7. Enhanced design of PID controller and noise filter for second order stable and unstable processes with time delay
  8. Removal of glycerol from biodiesel using multi-stage microfiltration membrane system: industrial scale process simulation
  9. Multi-objective optimization of a fluid catalytic cracking unit using response surface methodology
  10. Effect of pipe rotation on heat transfer to laminar non-Newtonian nanofluid flowing through a pipe: a CFD analysis
  11. Statistical modeling and optimization of the bleachability of regenerated spent bleaching earth using response surface methodology and artificial neural networks with genetic algorithm
  12. Short Communication
  13. A comparative study: conventional and modified serpentine micromixers
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 30.11.2025 from https://www.degruyterbrill.com/document/doi/10.1515/cppm-2022-0003/html
Scroll to top button