Experimental and Artificial Neural Network Modeling of a Upflow Anaerobic Contactor (UAC) for Biogas Production from Vinasse

被引:18
作者
Dibaba, Ousman R. [1 ,2 ]
Lahiri, Sandip K. [3 ]
T'Jonck, Stephan [4 ]
Dutta, Abhishek [1 ]
机构
[1] Katholieke Univ Leuven, Fac Ind Ingn Wetenschappen, Campus Grp T Leuven,Andreas Vesaliusstr 13, B-3000 Leuven, Belgium
[2] Addis Ababa Sci & Technol Univ, Dept Chem Engn, Sch Chem & Mat Engn, Addis Ababa, Ethiopia
[3] Sci Design Co Inc, 49 Ind Ave, Little Ferry, NJ 07643 USA
[4] WaterLeau NV, Ctr Res & Dev, Nieuwstr 26, B-3150 Wespelaar, Belgium
关键词
anaerobic digestion; contactor; biogas; artificial neural network; differential evolution; WASTE-WATER-TREATMENT; EVOLUTIONARY OPTIMIZATION; DIFFERENTIAL EVOLUTION; DIGESTION; SLUDGE; PERFORMANCE; STILLAGE; ENERGY;
D O I
10.1515/ijcre-2016-0025
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
A pilot scale Upflow Anaerobic Contactor (UAC), based on upflow sludge blanket principle, was designed to treat vinasse waste obtained from beet molasses fermentation. An assessment of the anaerobic digestion of vinasse was carried out for the production of biogas as a source of energy. Average Organic loading rate (OLR) was around 7.5 gCOD/m(3)/day in steady state, increasing upto 8.1 gCOD/m(3)/day. The anaerobic digestion was conducted at mesophilic (30-37 degrees C) temperature and a stable operating condition was achieved after 81 days with average production of 65% methane which corresponded to a maximum biogas production of 85 l/day. The optimal performance of UAC was obtained at 87% COD removal, which corresponded to a hydraulic retention time of 16.67 days. The biogas production increased gradually with OLR, corresponding to a maximum 6.54 gCOD/m(3)/day (7.4% increase from initial target). A coupled Artificial Neural Network-Differential Evolution (ANN-DE) methodology was formulated to predict chemical oxygen demand (COD), total suspended solids (TSS) and volatile fatty acids (VFA) of the effluent along with the biogas production. The method incorporated a DE approach for the efficient tuning of ANN meta-parameters such as number of nodes in hidden layer, input and output activation function and learning rate. The model prediction indicated that it can learn the non-linear complex relationship between the parameters and able to predict the output of the contactor with reasonable accuracy. The utilization of the coupled ANN-DE model provided significant improvement to the study and helps to study the parametric effect of influential parameters on the reactor output.
引用
收藏
页码:1241 / 1254
页数:14
相关论文
共 49 条
  • [41] Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing
    Shin, SY
    Lee, IH
    Kim, D
    Zhang, BT
    [J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2005, 9 (02) : 143 - 158
  • [42] Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces
    Storn, R
    Price, K
    [J]. JOURNAL OF GLOBAL OPTIMIZATION, 1997, 11 (04) : 341 - 359
  • [43] Treatment of distillery vinasse in a high rate anaerobic reactor using low density polyethylene supports
    Thanikal, J. V.
    Torrijos, M.
    Habouzit, F.
    Moletta, R.
    [J]. WATER SCIENCE AND TECHNOLOGY, 2007, 56 (02) : 17 - 24
  • [44] Risks associated with the production of biogas in Europe
    Travnicek, Petr
    Kotek, Lubos
    [J]. PROCESS SAFETY PROGRESS, 2015, 34 (02) : 172 - 178
  • [45] EFFECTS OF ACETATE, PROPIONATE, AND BUTYRATE ON THE THERMOPHILIC ANAEROBIC DEGRADATION OF PROPIONATE BY METHANOGENIC SLUDGE AND DEFINED CULTURES
    VANLIER, JB
    GROLLE, KCF
    FRIJTERS, CTMJ
    STAMS, AJM
    LETTINGA, G
    [J]. APPLIED AND ENVIRONMENTAL MICROBIOLOGY, 1993, 59 (04) : 1003 - 1011
  • [46] Wei Wei, 2010, Proceedings 2010 International Conference on Computational and Information Sciences (ICCIS 2010), P1072, DOI 10.1109/ICCIS.2010.265
  • [47] Werbos P. J., 1994, The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting, V1
  • [48] Stillage characterization and anaerobic treatment of ethanol stillage from conventional and cellulosic feedstocks
    Wilkie, AC
    Riedesel, KJ
    Owens, JM
    [J]. BIOMASS & BIOENERGY, 2000, 19 (02) : 63 - 102
  • [49] An evolutionary algorithm with guided mutation for the maximum clique problem
    Zhang, QF
    Sun, JY
    Tsang, E
    [J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2005, 9 (02) : 192 - 200