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Parameters optimization using an artificial neural networks and release characteristics of humic acids during lignite bioconversion


The bioconversion of coal at ambient conditions is a promising technology for coal processing. However, there are few examples of the optimization of processes for industrial-scale use. In this work, the optimization of process parameters affecting lignite bioconversion by an isolated fungus WF8 using an artificial neural network (ANN) combined with a genetic algorithm (GA) was carried out for modeling of humic acids (HAs) yield and parameters. Kinetic models were used to understand the release characteristics of HAs from the bioconversion of lignite. The results of the present work indicate that the optimal process parameters (OPP) are 29 °C, initial pH of 7, 180 rpm, 0.6 mmol·L−1 of CuSO4, 0.4 mmol L−1 of MnSO4, and 6.4 μmol·L−1 of veratryl alcohol (VA). The predicted experimental data obtained by ANN is similar to the actual and the significant correlation coefficient value (R2) of 0.99 indicates that ANN has good predictability. The actual yield of HAs are 5.17 mg·mL−1. During bioconversion, the fungus WF8 could loosen and attack the structure of lignite. The release of HAs produced by bioconversion of lignite under the OPP via diffusion and swelling is fit to zero-order model independent on concentration. This provides support for the industrial bioconversion of lignite.

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Alkaline metabolites


Artificial neural network


Genetic algorithm


Humic acids




Lignin peroxidase


Manganese peroxidase


Nitric acid-pretreated lignite


Optimal process parameters


Standard deviation


Veratryl alcohol


  1. Yin LQ (2004) Lignite resources and utilization outlook in China. Coal Sci Technol 32:12–14

    Google Scholar 

  2. Yuan HL, Yang JS, Wang FQ, Ying JY, Li BZ, Chen WX (2002) The prospect of microbial sustainable utilization of lignite. World Sci Technol R&D 24:13–18

    Google Scholar 

  3. Xiao L, Yao JH, Wan YZ, Tao XX, Liu JT (2010) Briquetting mechanism and parameters optimization of the lignite/biomass. Int J Min Sci Technol 39:352–356 ([in Chinese])

    CAS  Google Scholar 

  4. Zhao YM, Liu JT, Wei XY, Luo ZF, Chen QR, Song SL (2011) New progress in the processing and efficient utilization of coal. Mining Sci Technol 21:547–552

    CAS  Google Scholar 

  5. Dai HW, Xie KY (1999) Lignite processing & utilization. China Coal Industry Publishing House, Beijing

    Google Scholar 

  6. San XY, Gao ZF, Zhao SG (2005) Air pollution of sulfur dioxide coming from burning coal and feasible control method. Mining Safety Environ Protection 32:30–33

    Google Scholar 

  7. Xu XH, Chen CH, Qi HY (2000) Development of coal combustion pollution control for SO2 and NOx in China. Fuel Process Technol 62:153–160

    CAS  Article  Google Scholar 

  8. Ralph JP, Catcheside DEA (1994) Decolourisation and depolymerisation of solubilised lowrank coal by the white-rot basidiomycete Phanerochaete chrysosporium. Appl Microbiol Biotechnol 42:536–542

    CAS  Article  Google Scholar 

  9. Yuan HL, Yang JS, Chen WX (2006) Production of alkaline materials, surfactants and enzymes by Penicillium decumbens strain P6 in association with lignite degradation/solubilization. Fuel 85:1378–1382

    CAS  Article  Google Scholar 

  10. Elbeyli IY, Palantoken A, Piskin S, Kuzu H, Peksel A (2006) Liquefaction/solubilization of low-rank Turkish coals by white-rot fungus (Phanerochaete chrysosporium). Energy Sources Part A 28:1063–1073

    CAS  Article  Google Scholar 

  11. Selvi AV, Banerjee R, Ram LC, Singh G (2009) Biodepolymerization studies of low rank Indian coals. World J Microbiol Biotechnol 25:1713–1720

    CAS  Article  Google Scholar 

  12. Kandasamy S, Muniraj IK, Purushothaman N, Sekar A, Sharmila DJS, Kumarasamy R, Uthandi S (2016) High Level Secretion of Laccase (LccH) from a Newly Isolated White-Rot Basidiomycete, Hexagonia hirta MSF2. Front Microbiol 7:707

    Article  Google Scholar 

  13. Majeau JA, Brar SK, Tyagi RD (2010) Laccases for removal of recalcitrant and emerging pollutants. Bioresour Technol 101:2331–2350

    CAS  Article  Google Scholar 

  14. Schneider WDH, Costa PC, Fontana RC, de Siqueira FG, Pinheiro Dillon AJ, Camassola M (2019) Upscale and characterization of lignin-modifying enzymes from Marasmiellus palmivorus VE111 in a bioreactor under parameter optimization and the effect of inducers. J Biotechnol 295:1–8

    Article  Google Scholar 

  15. Stajić M, Persky L, Friesem D, Hadar Y, Wasser SP, Nevo E, Vukojević J (2006) Effect of different carbon and nitrogen sources on laccase and peroxidases production by selected Pleurotus species. Enzyme Microb Tech 38:65–73

    Article  Google Scholar 

  16. Sabar MA, Ali MI, Fatima N, Malik AY, Jamal A, Farman M, Huang ZX, Urynowicz M (2019) Degradation of low rank coal by Rhizopus oryzae isolated from a Pakistani coal mine and its enhanced releases of organic substances. Fuel 253:257–265

    CAS  Article  Google Scholar 

  17. Elisashvili V, Kachlishvili E, Asatiani MD (2018) Efficient production of lignin-modifying enzymes and phenolics removal in submerged fermentation of olive mill by-products by white-rot basidiomycetes. Int Biodeter Biodegr 134:39–47

    CAS  Article  Google Scholar 

  18. Zhuo R, Yuan P, Yang Y, Zhang S, Ma FY, Zhang XY (2017) Induction of laccase by metal ions and aromatic compounds in Pleurotus ostreatus HAUCC 162 and decolorization of different synthetic dyes by the extracellular laccase. Biochem Eng J 117:62–72

    CAS  Article  Google Scholar 

  19. Gill PK, Arora DS (2003) Effect of culture conditions on manganese peroxidase production and activity by some white rot fungi. J Ind Microbiol Biotechnol 30:28–33

    CAS  Article  Google Scholar 

  20. Zhu CW, Bao GW, Huang S (2016) Optimization of laccase production in the white-rot fungusPleurotus ostreatus(ACCC 52857) induced through yeast extract and copper. Biotechnol Biotec Eq 30:270–276

    CAS  Article  Google Scholar 

  21. Songulashvili G, Spindler D, Jimenez Tobon GA, Jaspers C, Kerns G, Penninckx MJ (2015) Production of a high level of laccase by submerged fermentation at 120-L scale of Cerrena unicolor C-139 grown on wheat bran. C R Biol 338:121–125

    Article  Google Scholar 

  22. Makela MR, Lundell T, Hatakka A, Hilden K (2013) Effect of copper, nutrient nitrogen, and wood-supplement on the production of lignin-modifying enzymes by the white-rot fungus Phlebia radiata. Fungal Biol 117:62–70

    CAS  Article  Google Scholar 

  23. Qi XF, Ma WC, Dang YF, Su WJ, Liu LJ (2020) Optimization of the melt/crystal interface shape and oxygen concentration during the Czochralski silicon crystal growth process using an artificial neural network and a genetic algorithm. J Cryst Growth 548:125828

    CAS  Article  Google Scholar 

  24. Miraboutalebi SM, Kazemi P, Bahrami P (2016) Fatty Acid Methyl Ester (FAME) composition used for estimation of biodiesel cetane number employing random forest and artificial neural networks: a new approach. Fuel 166:143–151

    CAS  Article  Google Scholar 

  25. Mohammadi M, Lakestani M, Mohamed MH (2018) Intelligent parameter optimization of Savonius rotor using Artificial Neural Network and Genetic Algorithm. Energy 143:56–68

    Article  Google Scholar 

  26. Yuan HL, Yang JS, Wang FQ, Chen WX (2006) Degradation and Solubilization of Chinese Lignite by Penicillium sp. P61. Appl Biochem Microbiol 42:52–55

    CAS  Article  Google Scholar 

  27. Kwiatos N, Jędrzejczak-Krzepkowska M, Strzelecki B, Bielecki S (2018) Improvement of efficiency of brown coal biosolubilization by novel recombinant Fusarium oxysporum laccase. AMB Express 8:133

    Article  Google Scholar 

  28. Sekhohola LM, Igbinigie EE, Cowan AK (2013) Biological degradation and solubilisation of coal. Biodegradation 24:305–318

    CAS  Article  Google Scholar 

  29. Yuan H, Cai Y, Zhou X (1999) Breeding of lignite degrading fungi and analysis of the degraded products. Chin J Appl Environ Biol 5(Suppl.):21–24 ([in Chinese])

    CAS  Google Scholar 

  30. Naqvi SR, Ali I, Nasir S, Ali Ammar Taqvi S, Atabani AE, Chen W-H (2020) Assessment of agro-industrial residues for bioenergy potential by investigating thermo-kinetic behavior in a slow pyrolysis process. Fuel 278:118259

    CAS  Article  Google Scholar 

  31. Ali I, Tariq R, Naqvi SR, Khoja AH, Mehran MT, Naqvi M, Gao N (2021) Kinetic and thermodynamic analyses of dried oily sludge pyrolysis. J Energy Inst 95:30–40

    CAS  Article  Google Scholar 

  32. Hameed Z, Naqvi SR, Naqvi M, Ali I, Taqvi SAA, Gao N, Hussain SA, Hussain S (2020) A comprehensive review on thermal coconversion of biomass, sludge, coal, and their blends using thermogravimetric analysis. J Chem 2020:1–23

    Article  Google Scholar 

  33. Yao JH, Wei XY, Xiao L, Ji HM, Zong ZM, Liu FJ (2015) Fractional extraction and biodepolymerization of shengli lignite. Energ Fuel 29:2014–2021

    CAS  Article  Google Scholar 

  34. Unagolla JM, Jayasuriya AC (2018) Drug transport mechanisms and in vitro release kinetics of vancomycin encapsulated chitosan-alginate polyelectrolyte microparticles as a controlled drug delivery system. Eur J Pharm Sci 114:199–209

    CAS  Article  Google Scholar 

  35. Cong ZT, Shi YB, Wang Y, Wang YH, Niu J, Chen NN, Xue HY (2018) A novel controlled drug delivery system based on alginate hydrogel/chitosan micelle composites. Int J Biol Macromol 107(Pt A):855–864

    CAS  Article  Google Scholar 

  36. Das B, Dutta S, Nayak AK, Nanda U (2014) Nanda, Zinc alginate-carboxymethyl cashew gum microbeads for prolonged drug release: development and optimization. Int J Biol Macromol 70:506–515

    CAS  Article  Google Scholar 

  37. Chenthamarakshan A, Parambayil N, Miziriya N, Soumya PS, Lakshmi MS, Ramgopal A, Dileep A, Nambisan P (2017) Optimization of laccase production from Marasmiellus palmivorus LA1 by Taguchi method of Design of experiments. BMC Biotechnol 17:12

    Article  Google Scholar 

  38. Ahamed A, Vermette P (2009) Effect of culture medium composition on Trichoderma reesei’s morphology and cellulase production. Bioresour Technol 100:5979–5987

    CAS  Article  Google Scholar 

  39. Jain KK, Kumar A, Shankar A, Pandey D, Chaudhary B, Sharma KK (2020) De novo transcriptome assembly and protein profiling of copper-induced lignocellulolytic fungus Ganoderma lucidum MDU-7 reveals genes involved in lignocellulose degradation and terpenoid biosynthetic pathways. Genomics 112:184–198

    CAS  Article  Google Scholar 

  40. Garg A, Jain S (2020) Process parameter optimization of biodiesel production from algal oil by response surface methodology and artificial neural networks. Fuel 277:118254

    CAS  Article  Google Scholar 

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This work was supported by the Fundamental Research Funds for the Central Universities (2019XKQYMS60).

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Correspondence to Lei Xiao.

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Yao, JH., Xu, BC., Zhuo, DY. et al. Parameters optimization using an artificial neural networks and release characteristics of humic acids during lignite bioconversion. Bioprocess Biosyst Eng 45, 1223–1235 (2022).

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  • Lignite
  • Bioconversion
  • Humic acids
  • Artificial neural network
  • Release characteristics