Advances in Computational Design

  • 제5권3호
  • /
  • Pages.323-347
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  • 2020
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  • 2383-8477(pISSN)
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  • 2466-0523(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Modeling methods used in bioenergy production processes: A review

  • Akroum, Hamza (Laboratoire d'Automatique Appliquee, Universite M'Hamed Bougara de Boumerdes) ;
  • Akroum-Amrouche, Dahbia (Departement de chimie, faculte des sciences, Universite M'Hamed Bougara de Boumerdes) ;
  • Aibeche, Abderrezak (Laboratoire d'Automatique Appliquee, Universite M'Hamed Bougara de Boumerdes)
  • 투고 : 2019.03.01
  • 심사 : 2020.03.05
  • 발행 : 2020.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

The enhancements of bioenergy production effectiveness require the comprehensively experimental study of several parameters affecting these bioprocesses. The interpretation of the obtained experimental results and the estimation of optimum yield are extremely complicated such as misinterpreting the results of an experiment. The use of mathematical modeling and statistical experimental designs can consistently supply the predictions of the potential yield and the identification of defining parameters and also the understanding of key relationships between factors and responses. This paper summarizes several mathematical models used to achieve an adequate overall and maximal production yield and rate, to screen, to optimize, to identify, to describe and to provide useful information for the effect of several factors on bioenergy production processes. The usefulness, the validity and, the feasibility of each strategy for studying and optimizing the bioenergy-producing processes were discussed and confirmed by the good correlation between predicted and measured values.

키워드

참고문헌

  1. Abbar, A.H. (2019), "Effect of electrolysis parameters on the specific surface area of nickel powder: Optimization using Box-Behnken design", Int. J. Electrochem. Sci., 14, 662-678. https://doi.org/10.20964/2019.01.60.
  2. Adepoju, T.F., Udoetuk, E.N., Olatunbosun, B.E., Mayen, I.A. and Babalola, R. (2018), "Evaluation of the effectiveness of the optimization procedure with methanolysis of waste oil as case study", South African J. Chem. Eng., 25, 169-177. https://doi.org/10.1016/j.sajce.2018.05.002.
  3. Adnan, N.A.A., Suhaimi, S.N., Abd-Aziz, S., Hassan, M.A. and Phang, L.Y. (2014), "Optimization of bioethanol production from glycerol by Escherichia coli SS1", Renewable Energy, 66, 625-633. https://doi.org/10.1016/j.renene.2013.12.032.
  4. Anbessa, T. T. and Karthikeyan, S. (2019), "Optimization and mathematical modeling of biodiesel production using homogenous catalyst from waste cooking oil", J. Eng. Adv. Technol., 9(1), 1733-1739. https://doi.org/10.35940/ijeat.F9005.109119.
  5. Ardabili, S. F., Najafi, B. and Shamshirband, S. (2019), "Fuzzy logic method for the prediction of cetane number using carbon number, double bounds, iodic, and saponification values of biodiesel fuels AQ3", Environ. Progress Sustainable Energy, 38, 584-599. https://doi.org/10.1002/ep.12960.
  6. Ardabili, S.F., Najafi, B., Shamshirband, S., Bidgoli, B. M., Deo, R.C. and Chau, K.W. (2018), "Computational intelligence approach for modeling hydrogen production: a review", Eng. Appl. Comput. Fluid Mech., 12(1), 438-458. https://doi.org/10.1080/19942060.2018.1452296.
  7. Athreya, S. and Venkatesh, Y.D. (2012), "Application of taguchi method for optimization of process parameters in: improving the surface roughness of lathe facing operation", Referred J. Eng. Sci., 1(3), 13-19.
  8. Barekati-Goudarzi, M., Boldor, D. and Nde D.B. (2016), "In-situ transesterification of seeds of invasive Chinese tallow trees (Triadica sebifera L.) in a microwave batch system (GREEN3) using hexane as co-solvent: Biodiesel production and process optimization", Bioresource Technol., 201, 97-104. https://doi.org/10.1016/j.biortech.2015.11.028.
  9. Behbahani, M., Moghaddam, M.R. A. and Arami, M. (2011), "Techno-economical evaluation of fluoride removal by electrocoagulation process: Optimization through response surface methodology", Desalination, 271, 209-218. https://doi.org/10.1016/j.desal.2010.12.033.
  10. Beltramo, T., Klocke, M. and Hitzmann, B. (2019), "Prediction of the biogas production using GA and ACO input features selection method for ANN model", Info. Process. Agriculture, 6(3), 349-356. https://doi.org/10.1016/j.inpa.2019.01.002.
  11. Beltramo, T., Ranzan, C., Hinrichs, J. and Hitzmann, B. (2016), "Artificial neural network prediction of the biogas flow rate optimised with an ant colony algorithm", Biosyst. Eng., 143, 68-78. https://doi.org/10.1016/j.biosystemseng.2016.01.006.
  12. Bertram, A.M., Zhang, Q. and S.C. (2016) "A novel particle swarm and genetic algorithm hybrid method for diesel engine performance optimization", J. Engine Res., 17(7), 732-747. https://doi.org/10.1177/1468087415611031.
  13. Betiku, E. and Ajala, S.O. (2014), "Modeling and optimization of Thevetia peruviana (yellow oleander) oil biodiesel synthesis via Musa paradisiacal (plantain) peels as heterogeneous base catalyst: A case of artificial neural network vs. response surface methodology", Industrial Crops Products, 53, 314-322. https://doi.org/10.1016/j.indcrop.2013.12.046.
  14. Betiku, E. and Taiwo, A.E. (2015), "Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate vis-a-vis response surface methodology and artificial neural network", Renew Energy., 74:87-94. https://doi.org/10.1016/j.renene.2014.07.054.
  15. Bharadwaj, A. V. S. L. S., Niju, S., Begum, K.M.M. and Anantharaman N. (2018), "Optimization and modeling of biodiesel production using fluorite as a heterogeneous catalyst", Energy Sources, Part A: Recovery, utilization, and environmental effects, 41(11), 1-17. https://doi.org/10.1080/15567036.2018.1549165.
  16. Bhattacharyya, S. (2012), Neural Networks: Evolution, Topologies, Learning algorithms and Applications, in Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies, IGI Global edition, USA.
  17. Bina, B., Amin, M. M., Pourzamani, H., Fatehizadeh, A., Ghasemian, M., Mahdavi, M. and Taheri, E. (2019), "Biohydrogen production from alkaline wastewater: The stoichiometric reactions, modeling, and electron equivalent", MethodsX, 6, 1496-1505. https://doi.org/10.1016/j.mex.2019.06.013.
  18. Boni, M.R., Sbaffoni, S., Tuccinardi, L. and Viotti, P. (2013), "Development and calibration of a model for biohydrogen production from organic waste", Waste Manage., 33, 1128-1135. https://doi.org/10.1016/j.wasman.2013.01.019.
  19. Booth, J. G. and Hobert, J. P. (1998), "Standard errors of prediction in generalized linear mixed models", J. American Statistical Assoc., 93(441), 262-272. https://doi.org/10.1080/01621459.1998.10474107
  20. Bouaid, A., Martinez, M. and Aracil, J., (2009), "Production of biodiesel from bioethanol and Brassica carinata oil: Oxidation stability study", Bioresource Technol., 100, 2234-2239. https://doi.org/110.1016/j.biortech.2008.10.045.
  21. Box, G.E.P. and Wilson, K.B. (1951), "On the Experimental Attainment of Optimum Conditions", J. Royal Statistical Society B, 13, 1-45, United Kingdom.
  22. Burati, J.L. and Weed, R.M. (2006), "Accuracy and precision of typical quality measures. transportation research record", J. Transport. Res. Board, 1946(1), 82-91. https://doi.org/10.1177/0361198106194600110
  23. Canabarro, N., Soares, J.F., Anchieta, C.G., Kelling, C.S. and Mazutti, M.A. (2013), "Thermochemical processes for biofuels production from biomass", Sustainable Chem. Processes, 1(1), 22. https://doi.org/10.1186/2043-7129-1-22.
  24. Castillo-Villar, K.K. (2014), "Metaheuristic algorithms applied to bioenergy supply chain problems: theory, review, challenges, and future", Energies, 7, 7640-7672. https://doi.org/10.3390/en7117640.
  25. Chaganti, S.R., Kim, D.H. and Lalman, J.A. and Shewa, W.A. (2012), "Statistical optimization of factors affecting biohydrogen production from xylose fermentation using inhibited mixed anaerobic cultures", J. Hydrogen Energy, 37, 11710 -11718. https://doi.org/10.1016/j.ijhydene.2012.05.036.
  26. Chen, W.H., Lee, K. T. and Ong H. C. (2019), "Biofuel and bioenergy technology", Energies, 12(2), 290. https://doi.org/10.3390/en12020290.
  27. Chollom, M.N., Rathilal, S., Swalaha, F.M., Bakare, B.F. and Tetteh, E. K. (2020), "Comparison of response surface methods for the optimization of an upflow anaerobic sludge blanket for the treatment of slaughterhouse wastewater", Environ. Eng. Res., 25(1) 114-122. https://doi.org/10.4491/eer.2018.366.
  28. Czitrom, V. (1999), "One-Factor-at-a-Time versus designed experiments", The American Statistician, 53(2), 126-131. https://doi.org/10.1080/00031305.1999.10474445.
  29. Deepanraj, B., Sivasubramanian, V. and Jayaraj, S. (2015), "Experimental and kinetic study on anaerobic digestion of food waste: The effect of total solids and pH", J. Renewable Sustainable Energy, 7, 063104. https://doi.org/10.1063/1.4935559 .
  30. Demirbas, M.F. (2009), "Biorefineries for biofuel upgrading: a critical review", Appl Energy, 86, S151-61. https://doi.org/10.1016/j.apenergy.2009.04.043.
  31. Ekpenyong, G., Antai1, S.P., Asitok, A.D. and Ekpo, B.O. (2017), "Plackett-Burman Design and Response Surface Optimization of Medium Trace Nutrients for Glycolipopeptide Biosurfactant Production Maurice", Iranian Biomedical J., 21(4), 249-260. https://doi.org/10.18869/acadpub.ibj.21.4.249.
  32. Fayyazi, E, Ghobadian., B, Najafi., G, Hosseinzadeh., B, Mamat., R. and Hosseinzadeh, J. (2015), "An ultrasound-assisted system for the optimization of biodiesel production from chicken fat oil using a genetic algorithm and response surface methodology", Ultrason Sonochem., 26, 312-320. https://doi.org/10.1016/j.ultsonch.2015.03.007.
  33. Ferreira, S.L.C., Bruns, R.E., Ferreira, H.S., Matos, G.D., David, J.M., Brandao, G.C., da Silva, E.G.P., Portugal, L.A., dos Reis, P.S., Souza a, A.S. and dos Santos, W.N.L. (2007), "Box-Behnken design: An alternative for the optimization of analytical methods", Analytica Chimica Acta, 597, 179-186. https://doi.org/10.1016/j.aca.2007.07.011.
  34. Ferreira, S.L.C., dos Santos, W.N.L., Quintella, Benicio. Neto C.M. and Bosque-Sendra J.M. (2004), "Doehlert matrix: A chemometric tool for analytical chemistry", Talanta, 63, 1061-1067. https://doi.org/10.1016/j.talanta.2004.01.015.
  35. Frey, D. D., Engelhardt, F. and Greitzer, E.M. (2003) "A role for 'one-factor-at-a-time' experimentation in parameter design", Res. Eng. Design, 14, 65-74. https://doi.org/10.1007/s00163-002-0026-9.
  36. Gadhe, A., Sonawane, S.S. and Varma, M.N. (2014a), "Kinetic analysis of biohydrogen production from complex dairy wastewater under optimized condition", J. Hydrogen Energy, 39, 1306-1314. https://doi.org/10.1016/j.ijhydene.2013.11.022.
  37. Gadhe, A., Sonawane, S.S. and Varma, M.N. (2014b), "Ultrasonic pretreatment for an enhancement of biohydrogen production from complex food waste", J. Hydrogen Energy, 39, 7721-7729. https://doi.org/10.1016/j.ijhydene.2014.03.105.
  38. Garlapati, V.K., Vundavilli, P.R. and Banerjee, R. (2017), "Optimization of flavour ester production through artificial bee colony algorithm: ABC optimization approach for flavour ester production", 4th IEEE ICIIP 2017 Fourth International Conference on Image Information Processing, Shimla, India, December. https://doi.org/10.1109/iciip.2017.8313694.
  39. Guerrero, A.B. and Munoz, E. (2018), "Life cycle assessment of second generation ethanol derived from banana agricultural waste: environmental impacts and energy balance", J. Clean Prod., 174,710-717. https://doi.org/10.1016/j.jclepro.2017.10.298.
  40. Gunst, R.F. and Robert, L. (2009), "Fractional factorial design", Computational Statistics, 1(2), 234-244.
  41. Hanrahan, G. (2011), Artificial Neural networks in Biological and Environmental Analysis, Analytical Chemistry Series, 182, Duke University, Los Angeles, California.
  42. Hasegawa, F., Yokoyama, S. and Imou, K. (2010), "Methanol or ethanol produced from woody biomass: Which is more advantageous?", Bioresource Technol., 101, S109-S111. https://doi.org/10.1016/j.biortech.2009.05.008.
  43. Hossain, S.M.Z. (2019), "A Tutorial review on biochemical conversion of microalgae biomass into biofuel.", Chem. Eng. Technol., https://doi.org/10.1002/ceat.201800605.
  44. Huang, D., Zhou, H. and Lin, L. (2012), "Biodiesel: An alternative to conventional fuel", Energy procedia, 16, 1874-1885. https://doi.org/10.1016/j.egypro.2012.01.287.
  45. Ishola, N.B., Okeleye, A.A., Osunleke, A.S. and Betiku, E. (2019), "Process modeling and optimization of sorrel biodiesel synthesis using barium hydroxide as a base heterogeneous catalyst: appraisal of response surface methodology, neural network and neuro-fuzzy system", Neural Comput. Appl., 31, 4929-4943. https://doi.org/10.1007/s00521-018-03989-7.
  46. Jachner, S., Boogaart, K. G.v.d. and Petzoldt, T. (2007), "Statistical Methods for the Qualitative Assessment of Dynamic Models with Time Delay (R Package qualV)", J. Statistical Software, 22(8), 1-30. https://doi.org/10.18637/jss.v022.i08.
  47. Jha, P., Kana, E. and Schmidt, S. (2017), "Can artificial neural network and response surface methodology reliably predict hydrogen production and COD removal in an UASB bioreactor?", J. Hydrogen Energy, 42(30), 18875-18883. https://doi.org/10.1016/j.ijhydene.2017.06.063.
  48. Kadi, M.A., Akkouche, N., Awad, S., Loubar, K. and Tazerout, M. (2019), "Kinetic study of transesterification using particle swarm optimization method", Heliyon, 5(8), e02146. https://doi.org/10.1016/j.heliyon.2019.e02146 .
  49. Kaparaju, P., Serrano, M., Thomsen, A.B., Kongjan, P. and Angelidaki, I. (2009), "Bioethanol, biohydrogen, and biogas production from wheat straw in a biorefinery concept", Bioresour Technol., 100(9), 2562-2568. https://doi.org/10.1016/j.biortech.2008.11.011.
  50. Karaboga, D. and Basturk, B. (2007), "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", J. Global Optimization, 39(3), 459-471. https://doi.org/10.1007/s10898-007-9149-x.
  51. Karthic, P., Joseph, S., Arun, N., Kumaravel, S. (2013), "Optimization of biohydrogen production by Enterobacter species using artificial neural network and response surface methodology", J. Renewable Sustainable Energy, 5(3), 033104, 1-13. https://doi.org/ 10.1063/1.4803746.
  52. Kato, T. (2016), Prediction of Photovoltaic Power Generation Output and Network Operation, in Integration of Distributed Energy Resources in Power Systems, 77-108, Academic Press, Japan.
  53. Kumar, P., Barrett, D.M., Delwiche, M. J. and Stroeve, P. (2009), "Methods for Pretreatment of Lignocellulosic Biomass for Efficient Hydrolysis and Biofuel Production", Ind. Eng. Chem. Res., 48(8), 3713-3729. https://doi.org/10.1021/ie801542g.
  54. Lardon, L., Helias, A., Sialve, B., Steyer, J.P. and Bernar, O. (2009), "Life-Cycle assessment of biodiesel production from microalgae", Environ. Sci. Technol., 43(17), 6475-6481. https://doi.org/10.1021/es900705j .
  55. Li, D. and Liu, S. (2019), Water Quality Evaluation in Water Quality Monitoring and Management: Basis, Technology and Case Studies, 113-159, Academic Press, London. https://doi.org/10.1016/B978-0-12-811330-1.00004-1.
  56. Li, P., Xu, L., Mou, Y., Shan, T., Mao, Z., Lu, S., Peng, Y. and Zhou, L. (2012), "Medium optimization for exopolysaccharide production in liquid culture of endophytic ungus berkleasmium sp. dzf12", Int. J. Mol. Sci., 13(9), 11411-11426. https://doi.org/10.3390/ijms130911411.
  57. Luftig, J.T. and Jordan, V.S. (1998), Design of Experiments in Quality Engineering, McGraw-Hill, NY, USA.
  58. Mahanty, B., Zafar, M. and Park, H.S. (2013), "Characterization of codigestion of industrial sludges for biogas production by artificial neural network and statistical regression models", Environ Technol., 34, 2145-2153. https://doi.org/10.1080/09593330.2013.819022.
  59. Massart, D.L., Vandeginste, B.G.M., Buydens, L.M.C., de Jong, S., Lewi, P.J. and Smeyers-Verbeke, J. (1997), Handbook of Chemometrics and Qualimetrics, Part A, 20A, Elsevier, Amsterdam.
  60. Minon-Fuentes, R. and Aguilar-Juarez, O. (2020). "Hydrogen production from coffee pulp by dark fermentation", Water Scie. Technol., 80(9), 1692-1701. https://doi.org/10.2166/wst.2019.416.
  61. Mohamed, M.S., Tan, J.S., Mohamad, R., Mokhtar, M.N. and Ariff A.B (2013), "Comparative analyses of response surface methodology and artificial neural network on medium optimization for Tetraselmis sp. FTC209 grown under mixotrophic condition", Sci World J., 2013, 1-14. https://doi.org/10.1155/2013/948940 .
  62. Motulsky, H. and Christopoulos, A. (2003), Fitting Models to Biological Data using Linear and Nonlinear Regression : A Practical Guide to Curve Fitting, GraphPad Software, Inc San Diego CA, USA.
  63. Nagata, Y. and Chu, K.H. (2003), "Optimization of a fermentation medium using neural networks and genetic algorithms", Biotechnol Lett., 25, 1837-1842. https://doi.org/10.1023/A:1026225526558.
  64. Nassef, A. M., Sayed, E. T., Rezka, H., Abdelkareem, M. A., Rodriguez, C. and Olabi, A.G. (2019), "Fuzzy-modeling with Particle Swarm Optimization for enhancing the production of biodiesel from Microalga", Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 41(17), 2094-2103. https://doi.org/10.1080/15567036.2018.1549171.
  65. Oiwoh, O., Ayodele, B.V., Amenaghawon, N.A. and Okieimen, C.O. (2018), "Optimization of bioethanol production from simultaneous saccharification and fermentation of pineapple peels using Saccharomyces cerevisiae", J. Appl. Sci. Environ. Manage., 22(1) 54-59. https://doi.org/10.4314/jasem.v21i7.5.
  66. Onumaegbu, C., Alaswad, A., Rodriguez., C. and Olabi, A. (2018), "Optimization of pre-treatment process parameters to generate biodiesel from Microalga", Energies, 11(4), 806. https://doi.org/10.3390/en11040806.
  67. Paintsil, A., Armah, F.A. and Yanful, E.K. (2016), "Assessment of the transesterification stage of biodiesel, production i: application of a plackett-burman design to select the process variables", Waste Biomass Valor, 8(2), 473-481. https://doi.org/10.1007/s12649-016-9583-4.
  68. Pan, C.M., Fan, Y.T., Xing, Y., Hou, H.W. and Zhang, M.L. (2008), "Statistical optimization of process parameters on biohydrogen production from glucose by Clostridium sp. Fanp2", Bioresource Technol., 99, 3146-3154. https://doi.org/10.1016/j.biortech.2007.05.055.
  69. Pandey, R. K., Chand, K. and Tewari, L. (2018), "Solid state fermentation and crude cellulase based bioconversion of potential bamboo biomass to reducing sugar for bioenergy production", J. Food Agriculture, 98(12), 4411-4419. https://doi.org/10.1002/jsfa.8963.
  70. Patterson, T., Esteves, S., Dinsdale, R., Guwy, A. and Maddy, J. (2013), "Life cycle assessment of biohydrogen and biomethane production and utilization as a vehicle fuel", Bioresource Technol., 131, 235-245. https://doi.org/10.1016/j.biortech.2012.12.109.
  71. Perera, F. (2017), "Pollution from fossil-fuel combustion is the leading environmental threat to global pediatric health and equity: solutions exist", J. Environ. Res. Public Health, 15(1), 16. https://doi.org/10.3390/ijerph15010016 .
  72. Perez-Sarinana, B.Y., Diaz-Gonzalez, A., Leon-Rodriguez, A.D., Saldana-Trinidad, S., Perez-Luna, Y.D.C., Guerrero-Fajardo, C.A.O. and Sebastian, P.J. (2019), "Methane production from coffee crop residues", Rom Biotechnol Lett., 24(4), 669-675. https://doi.org/10.25083/rbl/24.4/669.675.
  73. Phukoetphim, N., Salakkam, A., Laopaiboon, P. and Laopaiboon, L. (2017), "Kinetic models for batch ethanol production from sweet sorghum juice under normal and high gravity fermentations: Logistic and modified Gompertz models", J. Biotechnol., 243, 69-75. https://doi.org/10.1016/j.jbiotec.2016.12.012.
  74. Pittman, J. K., Dean, A. P. and Osundeko, O. (2011), "The potential of sustainable algal biofuel production using wastewater resources", Bioresource Technol., 102, 17-25. https://doi.org/10.1016/j.biortech.2010.06.035.
  75. Plackett, R.L. and Burman, J.P. (1946), "The Design of optimum multifactorial experiments", Biometrika, 33(4), 305-325. https://doi.org/10.1093/biomet/33.4.305.
  76. Prakasham, R.S., Sathish, T. and Brahmaiah, P. (2011), "Imperative role of neural networks coupled genetic algorithm on optimization of biohydrogen yield", Int J. Hydrog. Energy, 36 4), 4332-4339. https://doi.org/10.1016/j.ijhydene.2011.01.031.
  77. Razali, N.A.A.M., Ibrahim ID, M. F., Bahrin, E.K. and Abd-Aziz, S. (2018), "Optimisation of simultaneous saccharification and fermentation (ssf) for biobutanol production using pretreated oil palm empty fruit bunch", Molecules, 23, 1944. https://doi.org/10.3390/molecules23081944.
  78. Rostami, S., Samani, B. H. and Saeidi, K. (2016) "Optimization of biodiesel production from prunus scoparia using artificial bee colony algorithm", JREE, 3(1), 52-58. https://doi.org/10.30501/jree.2016.70078.
  79. Sathasivam, S., Hamadneh, N. and Choon, O.H. (2011), "Comparing neural networks: hopfield network and rbf network", Appl. Math. Sci., 5(69), 3439-3452.
  80. Sathish, S. and Vivekanandan, S. (2016), "Parametric optimization for floating drum anaerobic bio-digester using response surface methodology and artificial neural network", Alexandria Eng. J., 55, 3297-3307. https://doi.org/10.1016/j.aej.2016.08.010.
  81. Sebayang, A.H., Masjuki, H.H., Ong, H.C., Dharma, S., Silitonga, A.S., Kusumo, F. and Milano, J. (2017), "Optimization of bioethanol production from sorghum grains using artificial neural networks integrated with ant colony", Industrial Crops Products, 97, 146-155. http://dx.doi.org/10.1016/j.indcrop.2016.11.064.
  82. Selvakumar, P., Kavitha, S. and Sivashanmugam, P. (2018), "Optimization of process parameters for efficient bioconversion of thermo-chemo pretreated manihot esculenta Crantz YTP1 stem to ethanol", Waste Biomass Valorization, https://doi.org/10.1007/s12649-018-0244-7.
  83. Sevinc, P., Gunduz, U., Eroglu, I. and Yucel, M. (2012), "Kinetic analysis of photosynthetic growth, hydrogen production and dual substrate utilization by Rhodobacter capsulatus", J. Hydrogen Energy, 37(21), 16430-16436. http://dx.doi.org/10.1016/j.ijhydene.2012.02.176.
  84. Sewsynker-Sukai, Y., Faloye, F. and Kana, E.B.G. (2017), "Artificial neural networks: an efficient tool for modelling and optimization of biofuel production (a mini review)", Biotechnol. Biotechnol. Equipment, 31(2), 221-235. https://doi.org/10.1080/13102818.2016.1269616.
  85. Shalaby, E.A. (2015), "A Review of Selected Non-Edible Biomass Sources as Feedstock for Biodiesel Production", Biofuels - Status and Perspective, chapter 1, 1-20, InTech, London, United Kingdom.
  86. Silitonga, A., Mahlia, T., Shamsuddin, A., Ong, H., Milano, J., Kusumo, F. and Rahman, S. (2019), "Optimization of cerbera manghas biodiesel production using artificial neural networks integrated with ant colony optimization", Energies, 12(20), 3811. https://doi.org/10.3390/en12203811.
  87. Singh, G., Mohapatra, S.K., Ragit, S.S. and Kundu, K. (2018), "Optimization of biodiesel production from grape seed oil using Taguchi's orthogonal array", Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 40(18), 2144-2153. https://doi.org/10.1080/15567036.2018.1495778
  88. Siu-Woon Ng, E. (2011) "MSE < Variance? A pitfall in calculating the mean square error", Model Assisted Statistics Appl., 6, 369-371. https://doi.org/10.3233/MAS-2011-0195.
  89. Sivamani, S. and Baskar, R. (2015), "Optimization of Bioethanol Production from Cassava Peel Using Statistical Experimental Design", Environ. Progress Sustainable Energy, 34(2), 567-574. https://doi.org/10.1002/ep.11984.
  90. Sivamani, S., Selvakumar, S., Rajendran, K. and Muthusamy, S. (2019), "Artificial neural network-genetic algorithm based optimization of biodiesel production from Simarouba glauca", Biofuels, 10(3), 393-401. https://doi.org/10.1080/17597269.2018.1432267. https://doi.org/10.1080/17597269.2018.1432267.
  91. Smyth, G.K. (2002), "Nonlinear Regression", Encyclopedia of Environmetrics, John Wiley Sons, Ltd, Chichester, 3, 1405-1411. https://doi.org/10.1002/9780470057339.van017.
  92. Suen, C.y., Das, A. and Midha, C.K. (2013), "Optimal fractional factorial designs and their construction", J. Statistical Planning Inference, 143, 1828-1834. https://doi.org/10.1016/j.jspi.2013.05.004.
  93. Sun, Q., Xiao, W., Xi, D., Shi, J., Yan, X. and Zhou, Z. (2010), "Statistical optimization of biohydrogen production from sucrose by a co-culture of Clostridium acidisoli and Rhodobacter sphaeroides", J. Hydrogen Energy, 35, 4076-4084. https://doi.org/10.1016/j.ijhydene.2010.01.145.
  94. Talebian-Kiakalaieh, A., Amin, N. A. S., Zarei, A. and Noshadi, I. (2013), "Transesterification of waste cooking oil by heteropoly acid (HPA) catalyst: Optimization and kinetic model", Appl. Energy, 102, 283- 292. https://doi.org/10.1016/j.apenergy.2012.07.018.
  95. Tarley, C.R.T., Silveira, G., Lopes dos Santos, W. N., Matos, G. D., Paranhos da Silva, E. G., Bezerra, M. A., Miro, M. and Ferreira S.L.C. (2009), "Chemometric tools in electroanalytical chemistry: Methods for optimization based on factorial design and response surface methodology", Microchem. J., 92, 58-67. https://doi.org/10.1016/j.microc.2009.02.002.
  96. Thao Vi, L.V., Salakkam, A. and Reungsang, A. (2017), "Optimization of key factors affecting bio-hydrogen production from sweet potato starch", Energy Procedia, 138, 973-978. https://doi.org/10.1016/j.egypro.2017.10.092.
  97. Thoai, D. N., Tongurai, C., Prasertsit, K. and Kumar, A. (2018) "Predictive Capability Evaluation of RSM and ANN in Modeling and Optimization of Biodiesel Production from Palm (Elaeisguineensis) Oil", J. Appl. Eng. Res., 13(10), 7529-7540.
  98. Tonini, D., Hamelin, L., Alvarado-Morales, M. and Astrup, T.F. (2016), "GHG emission factors for bioelectricity, biomethane, and bioethanol quantified for 24 biomass substrates with consequential life-cycle assessment", Bioresour, Technol., 208,123-133. https://doi.org/10.1016/j.biortech.2016.02.052.
  99. Usmanbaha, N., Jariyaboon, R., Reungsang, A., Kongjan, P. and Chu, C.Y. (2019), "Optimization of batch dark fermentation of chlorella sp. using mixed-cultures for simultaneous hydrogen and butyric acid production", Energies, 12(13), 2529. https://doi.org/10.3390/en12132529.
  100. Van der Stelt, M.J.C., Gerhauser, H., Kiel, J.H.A. and Ptasinski, K.J. (2011), "A Biomass upgrading by torrefaction for the production of biofuels", A Rev. Biomass Bioenergy, 35, 3748-3762. https://doi.org/10.1016/j.biombioe.2011.06.023.
  101. Wang, J. and Wan W. (2011), "Combined effects of temperature and pH on biohydrogen production by anaerobic digested sludge", Biomass Bioenergy, 35, 3896-3901. https://doi.org/10.1016/j.biombioe.2011.06.016.
  102. Wang, J. and Wan, W. (2009a), "Experimental design methods for fermentative hydrogen production: a review". J. Hydrogen Energy, 34, 235-44, https://doi.org/10.1016/j.ijhydene.2008.10.010.
  103. Wang, J. and Wan, W. (2009b), "Application of desirability function based on neural network for optimizing biohydrogen production process", J. Hydrogen Energy, 34, 1253-1259. https://doi.org/10.1016/j.ijhydene.2008.11.055.
  104. Wang, J. and Wan, W. (2009c), "Optimization of fermentative hydrogen production process using genetic algorithm based on neural network and response surface methodology", J. Hydrogen Energy, 34(1), 255-261. https://doi.org/10.1016/j.ijhydene.2008.10.010.
  105. Wang, J.L. and Wan, W. (2008), "The effect of substrate concentration on biohydrogen production by using kinetic models", Sci. China Ser. B Chem., 51(11), 1110-1117. https://doi.org/10.1007/s11426-008-0104-6.
  106. Wang, K.S., Chen, J.H., Huang, Y.H. and Huang, S.L. (2013), "Integrated Taguchi method and response surface methodology to confirm hydrogen production by anaerobic fermentation of cow manure", J. Hydrogen. Energy, 38, 45-53. https://doi.org/10.1016/j.ijhydene.2012.03.155.
  107. Weuster-Botz, D. (2000), "Experimental design for fermentation media development: Statistical design or global random search?", J. Biosci. Bioeng., 90(5), 473-83. https://doi.org/10.1016/s1389-1723(01)80027-x.
  108. Whiteman, J. and Kana, E.G. (2014), "Comparative assessment of the artificial neural network and response surface modeling efficiencies for biohydrogen production on sugar cane molasses", BioEnergy Research, 7(1), 295-305. https://doi.org/10.1007/s12155-013-9375-7.
  109. Wirth, R., Lakatos, G., Maroti, G., Bagi, Z., Minarovics, J., Nagy, K., Kondorosi, E., Rakhely, G. and Kovacs, K.L. (2015), "Exploitation of algal-bacterial associations in a two-stage biohydrogen and biogas generation process", Biotechnol. Biofuels, 8(59), 1-14. https://doi.org/10.1186/s13068-015-0243-x .
  110. Wolf, C., McLoone, S., & Bongards, M. (2008), "Biogas plant optimization using genetic algorithms and particle swarm optimization", IET Irish Signals and Systems Conference (ISSC 2008), NUI, Galway, Ireland, June. https://doi.org/10.1049/cp:20080670.
  111. Xingyong, L., Yubao, C., Xu, Z., Juhua, W., Wenhua, Z., Shijie, L., Jing, L. amd Xingling, Z. (2019), "Optimization of Operating Parameters in C8-C16 iso-Alkanes Selectivity with Response Surface Methodology", J. Biobased Mater. Bioenergy, 13(1), 11-18(8). https://doi.org/10.1166/jbmb.2019.1815.
  112. Yono, B., Syaichurrozi, I. and Sumardiono, S. (2014), "Kinetic Model of Biogas Yield Production from Vinasse at Various Initial pH: Comparison between Modified Gompertz Model and First Order Kinetic Model", Res. J. Appl. Sci., Eng. Technol., 7(13), 2798-2805. http://dx.doi.org/10.19026/rjaset.7.602.
  113. Zhu, H., Yang, J. and Xiaowei, C. (2019), "Application of Modified Gompertz Model to Study on Biogas production from middle temperature co-digestion of pig manure and dead pigs", 4th International Conference on Advances in Energy and Environment Research ICAEER 2019, E3S Web of Conferences 118 (03022), 1-7, Shanghai, China, August. https://doi.org/10.1051/e3sconf/201911803022.
  114. Zolgharneina, J., Shahmoradia, A. and Ghasemi, J.B.J. (2013), "Comparative study of Box-Behnken, central composite, and Doehlert matrix for multivariate optimization of Pb (II) adsorption onto Robinia tree leaves", Chemometrics, 27(1-2), 12-20. https://doi.org/10.1002/cem.2487.
  115. Zwietering, M.H., Jongenburger, I., Rombouts, F.M. and van't Riet, K. (1990), "Modelling of the bacterial growth curve", Appl Environ Microbiol, 56(6),1875-1881. https://doi.org/10.1128/aem.56.6.1875-1881.1990