Renewable Energy Research

| Peer-Reviewed |

Bioconversion of Locally Made Cassava Wastewater for Bio-hydrogen Production and Its Statistical Analysis: A Case of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Received: 19 October 2016    Accepted: 29 October 2016    Published: 19 November 2016
Views:       Downloads:

Share This Article

Abstract

This study is centered on the production of bio-hydrogen from cassava wastewater and its statistical analysis. Analysis of the sample was carried out by determining the physiochemical properties of the cassava wastewater for its suitability for industrial application. A broth medium was prepared, substrate preparation and inoculum pretreatment were carried out and the medium was cultivated following standard method. To optimize the process condition, response surface methodology (RSM) and artificial neural network (ANN) were engaged. An experimental design was carried out using RSM, three variable factors such as fermentation time (X1) (days), pH effect (X2) and substrate concentration (X3) (mg/L) were considered and 17 experimental runs were created. Results showed the physicochemical properties of wastewater had an initial pH of 5.58 (low acidity), total diffuse solid (TDS) of 3.93 mg/l, chemical oxygen demand (COD) of 0.25 mg/l and biochemical oxygen demand (BOD) of 0.16 mg/l. The statistical analysis by RSM predicted bio-hydrogen yield (HY) of 4.011 ml at X1 = -1, X2 = -1 and X3 = -0.043 variable conditions, this was validated in triplicate experiments, and the average HY was 3.98 ml. Similarly, ANN statistical software predicted HY of 4.221 ml at X1 = - 1, X2 = -1 and X3 = -0.032 variable conditions, this was also validated in triplicate experiments, and the average HY was 4.002 ml. The coefficient of determination (R2) and R-Sq. (adj.) for RSM (99.98% and 99.96%) and ANN (99.993% and 99.986%) indicate that the model fitted well for the acceptable representation of the relationship among the variables under consideration. The results of this experiment established that the use of both RSM and ANN with appropriate experimental design can give the optimum yield of bio-hydrogen, even though, ANN predict better than RSM in terms of yield of bio-hydrogen.

DOI 10.11648/j.rer.20160101.11
Published in Renewable Energy Research (Volume 1, Issue 1, December 2016)
Page(s) 1-7
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Previous article
Keywords

Optimization, Response Surface Methodology, Artificial Neural Network, Physicochemical Properties, Bio-hydrogen Production, Coefficient of Determination

References
[1] Pradhan, N., Dipasquale, L., d'Ippolito, G., Panico, A., Lens, P. N. L., Esposito, G; Fontana, A. (2015). Hydrogen production by the Thermophilic Bacterium Thermotoga Neapolitana. International Journal of Molecular Sciences, 16 (6) 12578-12600.
[2] Lin, Y., Wang, D., Li, Q., and Xiao, M. (2011). Mesophilic batch anaerobic co-digestion of pulp and paper sludge and monosodium glutamate waste liquor for methane production in a bench-scale digester. Bioresource Technol., 102 (6), 3673–3678.
[3] Wang, L., Cheng, S., Li, Q., and Zengrang, X. (2013). Tourist Dining Behavior in Lhasa City. Resources Science, 35 (8), 848–857.
[4] Zhang et al., 2007. Zhang, R., El-Mashad, H. M., Hartman, K., Wang, F., Liu, G., Choate, C., and Gamble, P. (2007). Characterization of food waste as feedstock for anaerobic digestion. Bioresource Technol. 98 (10), 929–935.
[5] Arslan, C., Sattar, A., Ji, C., Sattar, S., Yousaf, K., and Hashim, S. (2015). Optimizing the impact of temperature on bio-hydrogen production from food waste and its derivatives under no pH control using statistical modeling. Biogeosciences, 12(1), 6503-6514.
[6] Winter, C. J. (2005). Into the hydrogen energy economy-milestones. International Journal of Hydrogen Energy. 30(2): 681-685.
[7] IEA. (2006). Hydrogen production and storage. Hydrogen Implementing Agreements.
[8] Chong, M., Sabaratnam, V., Shirai, Y., and Hassan, M. A. (2012). Bio-hydrogen production from biomass and industrial waste by dark fermentation. International Journal of Hydrogen Energy. 34 (5), 3277-3287.
[9] Benemann, J. (1996). Hydrogen biotechnology: Progress and prospects. Nature Biotechnology. 14(9):1101-1103.
[10] Han, S. K., and Shin, H. S. (2004). Bio-hydrogen production by anaerobic fermentation of food waste. International Journal of Hydrogen Energy. 29: 569–577.
[11] Pan, J., Zhang, R., El-Mashad, H. M., Sun, H., and Ying, Y. (2008). Effect of food to microorganism ratio on bio-hydrogen production from food waste via anaerobic fermentation. International Journal of Hydrogen Energy. 33, 6968–6975.
[12] Ren, N. Q., Li, J. Z., Li, B. K., Wang, Y., and Liu, S. R. (2006). Bio-hydrogen production from molasses by anaerobic fermentation with a pilot scale bioreactor system. International Journal of Hydrogen Energy. 31, 2147–2157.
[13] Fang, H. H. P., Li, C. L., Zhang, T. (2006). Acidophilic bio-hydrogen production from rice slurry. International Journal of Hydrogen Energy. 31: 683–692.
[14] Zhang, T., Liu, H., Fang, H. H. P. (2003). Bio-hydrogen production from starch in wastewater under thermophilic condition. Journal of Environmental Management. 69:149–156.
[15] Wang. A., Ren, N., Shi, Y., Lee, D. J. (2008). Bioaugmented hydrogen production from microcrystalline cellulose using co-culture– Clostridium acetobutyricum X9 and Ethanoligenens harbinense B49. International Journal Hydrogen Energy. 33, 912–7.
[16] Yokoi, H., Saitsu, A., Uchida, H., Hirose, J., Hayashi, S., Takasaki, Y. (2001). Microbial hydrogen production from sweet potato starch residue. Journal of Biosciences Bioengineering. 91, 58–63.
[17] O-Thong, S., Prasertsan, P., Intrasungkha, N., Dhamwichukorn, S., Birkeland, N. K. (2007). Improvement of bio-hydrogen production and treatment efficiency on palm oil mill effluent with nutrient supplementation at thermophilic condition using an anerobic sequencing batch reactor. Enzymatic Microb Technology. 41, 583–90.
[18] Kalil, S. J., Maugeri, F., Rodrigues, M. I. (2000). Response surface analysis and simulation as a tool for bioprocess design and optimization. Process Biochemistry. 35, 539-550.
[19] Kana, E. B., Oloke, J. K., Lateef, A., and Oyebanji, A. (2012). Comparison evaluation of artificial neural network coupled genetic algorithm and response surface methodology for modeling and optimization of citric acid production by Aspergillus Niger MCBN297. Chemical Engineering Transactions. 2(2), 14-23.
[20] Bari, M. N., Alam, M. Z., Muyibi, A. S., Jamal, P., Abdullah-Al-Mamun. (2009). Improvement of production of citric acid from oil palm empty fruit branches: Optimization of media by statistical experimental designs. Bioresource Technology. 100 (12), 3113-3120.
[21] Kilic, M., Bayraktar, E., Ates, S., Mehmetoglu, U. (2002). Investigation of extractive citric acid fermentation using response surface methodology. Process Biochemistry. 37 (6), 759-767.
[22] Ghaffari, A., Abdollahi, H., Khoshayand, M. R., Soltani, B., Dadgar, A., Rafiee-Tehrani, M. (2006). Performance comparison of neural network training algorithms in modeling of bimodal drug delivery. International Journal of Pharmaceutics 327: 126-138.
[23] Achanta, A. S., Kowalski, J. G., Rhodes, C. T. (1995). Artificial neural networks: implications for pharmaceutical sciences. Drug. Dev. Ind. Pharm. 21 (4), 119-155.
[24] Shinani, N., Kumbhar, B. K., and Kulshreshtha, M. (2006). Modeling of extrusion process using response surface methodology and artificial neural networks. Journal of Engineering Science and Technology, 8(1) 31-40.
[25] Ghorbani, H., Nikbakht, A. M., Tabatabaei, M., Mahdi, H., Mohammadi, P. (2011). Application of modeling techniques for prediction and optimization of biodiesel production processes. International Conference on Biotechnology and Environment Management. Pp: 65-78.
[26] Mourabet, M., El Rhilassi, A., Bennani-Ziatni, M., Taitai, A. (2014). Comparative study of artificial neural network and response surface methodology for modelling and optimization the adsorption capacity of fluoride onto apatitic tricalcium phosphate. Universal Journal of Applied Mathematics. 2(3), 84-91.
[27] Sehgal, A. K and Meenu. (2013). Application of artificial neural network and response surface methodology for achieving desired surface roughness in end milling process of ductile iron grade 80-55-06. International Journal of Computational Engineering and Management. 4(16), 54-56.
Author Information
  • Chemical/Petrochemical Engineering Department, Akwa Ibom State University, Ikot Akpaden, Mkpat Enin L.G.A., Nigeria

  • Chemical/Petrochemical Engineering Department, Akwa Ibom State University, Ikot Akpaden, Mkpat Enin L.G.A., Nigeria

  • Chemical/Petrochemical Engineering Department, Akwa Ibom State University, Ikot Akpaden, Mkpat Enin L.G.A., Nigeria

Cite This Article
  • APA Style

    Adepoju Tunde Folorunsho, Eyibio Uduak Promise, Ukpong Anwana Abel. (2016). Bioconversion of Locally Made Cassava Wastewater for Bio-hydrogen Production and Its Statistical Analysis: A Case of Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Renewable Energy Research, 1(1), 1-7. https://doi.org/10.11648/j.rer.20160101.11

    Copy | Download

    ACS Style

    Adepoju Tunde Folorunsho; Eyibio Uduak Promise; Ukpong Anwana Abel. Bioconversion of Locally Made Cassava Wastewater for Bio-hydrogen Production and Its Statistical Analysis: A Case of Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Renew. Energy Res. 2016, 1(1), 1-7. doi: 10.11648/j.rer.20160101.11

    Copy | Download

    AMA Style

    Adepoju Tunde Folorunsho, Eyibio Uduak Promise, Ukpong Anwana Abel. Bioconversion of Locally Made Cassava Wastewater for Bio-hydrogen Production and Its Statistical Analysis: A Case of Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Renew Energy Res. 2016;1(1):1-7. doi: 10.11648/j.rer.20160101.11

    Copy | Download

  • @article{10.11648/j.rer.20160101.11,
      author = {Adepoju Tunde Folorunsho and Eyibio Uduak Promise and Ukpong Anwana Abel},
      title = {Bioconversion of Locally Made Cassava Wastewater for Bio-hydrogen Production and Its Statistical Analysis: A Case of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)},
      journal = {Renewable Energy Research},
      volume = {1},
      number = {1},
      pages = {1-7},
      doi = {10.11648/j.rer.20160101.11},
      url = {https://doi.org/10.11648/j.rer.20160101.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.rer.20160101.11},
      abstract = {This study is centered on the production of bio-hydrogen from cassava wastewater and its statistical analysis. Analysis of the sample was carried out by determining the physiochemical properties of the cassava wastewater for its suitability for industrial application. A broth medium was prepared, substrate preparation and inoculum pretreatment were carried out and the medium was cultivated following standard method. To optimize the process condition, response surface methodology (RSM) and artificial neural network (ANN) were engaged. An experimental design was carried out using RSM, three variable factors such as fermentation time (X1) (days), pH effect (X2) and substrate concentration (X3) (mg/L) were considered and 17 experimental runs were created. Results showed the physicochemical properties of wastewater had an initial pH of 5.58 (low acidity), total diffuse solid (TDS) of 3.93 mg/l, chemical oxygen demand (COD) of 0.25 mg/l and biochemical oxygen demand (BOD) of 0.16 mg/l. The statistical analysis by RSM predicted bio-hydrogen yield (HY) of 4.011 ml at X1 = -1, X2 = -1 and X3 = -0.043 variable conditions, this was validated in triplicate experiments, and the average HY was 3.98 ml. Similarly, ANN statistical software predicted HY of 4.221 ml at X1 = - 1, X2 = -1 and X3 = -0.032 variable conditions, this was also validated in triplicate experiments, and the average HY was 4.002 ml. The coefficient of determination (R2) and R-Sq. (adj.) for RSM (99.98% and 99.96%) and ANN (99.993% and 99.986%) indicate that the model fitted well for the acceptable representation of the relationship among the variables under consideration. The results of this experiment established that the use of both RSM and ANN with appropriate experimental design can give the optimum yield of bio-hydrogen, even though, ANN predict better than RSM in terms of yield of bio-hydrogen.},
     year = {2016}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Bioconversion of Locally Made Cassava Wastewater for Bio-hydrogen Production and Its Statistical Analysis: A Case of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)
    AU  - Adepoju Tunde Folorunsho
    AU  - Eyibio Uduak Promise
    AU  - Ukpong Anwana Abel
    Y1  - 2016/11/19
    PY  - 2016
    N1  - https://doi.org/10.11648/j.rer.20160101.11
    DO  - 10.11648/j.rer.20160101.11
    T2  - Renewable Energy Research
    JF  - Renewable Energy Research
    JO  - Renewable Energy Research
    SP  - 1
    EP  - 7
    PB  - Science Publishing Group
    UR  - https://doi.org/10.11648/j.rer.20160101.11
    AB  - This study is centered on the production of bio-hydrogen from cassava wastewater and its statistical analysis. Analysis of the sample was carried out by determining the physiochemical properties of the cassava wastewater for its suitability for industrial application. A broth medium was prepared, substrate preparation and inoculum pretreatment were carried out and the medium was cultivated following standard method. To optimize the process condition, response surface methodology (RSM) and artificial neural network (ANN) were engaged. An experimental design was carried out using RSM, three variable factors such as fermentation time (X1) (days), pH effect (X2) and substrate concentration (X3) (mg/L) were considered and 17 experimental runs were created. Results showed the physicochemical properties of wastewater had an initial pH of 5.58 (low acidity), total diffuse solid (TDS) of 3.93 mg/l, chemical oxygen demand (COD) of 0.25 mg/l and biochemical oxygen demand (BOD) of 0.16 mg/l. The statistical analysis by RSM predicted bio-hydrogen yield (HY) of 4.011 ml at X1 = -1, X2 = -1 and X3 = -0.043 variable conditions, this was validated in triplicate experiments, and the average HY was 3.98 ml. Similarly, ANN statistical software predicted HY of 4.221 ml at X1 = - 1, X2 = -1 and X3 = -0.032 variable conditions, this was also validated in triplicate experiments, and the average HY was 4.002 ml. The coefficient of determination (R2) and R-Sq. (adj.) for RSM (99.98% and 99.96%) and ANN (99.993% and 99.986%) indicate that the model fitted well for the acceptable representation of the relationship among the variables under consideration. The results of this experiment established that the use of both RSM and ANN with appropriate experimental design can give the optimum yield of bio-hydrogen, even though, ANN predict better than RSM in terms of yield of bio-hydrogen.
    VL  - 1
    IS  - 1
    ER  - 

    Copy | Download

  • Sections