Influence and Simulation Model of Operational Parameters on Hydrogen Bio-production Through Anaerobic Microorganism Fermentation Using Two Kinds of Wastes

The bio-hydrogen producing process has complex interactions; thus, constructing a detailed mechanistic model and proper control architecture is difficult. Artificial neural networks (ANNs) are capable of inferring the complex relationships between input and output process variables without a detailed characterization of the mechanisms governing the process. This work presents a novel ANN that accurately predicts the steady-state performance of bioreactors for the bio-hydrogen producing processes. In this experiment, producing hydrogen from kitchen wastes and sugar refinery wastewater was studied in two kinds of bioreactors. And a simulation model of operational parameters was also established based on theory of back propagation neural network (BPNN). The effects of operational parameters on bio-hydrogen production bioreactors were considered. The results showed that simulation model well fitted the laboratory data, and could well simulate the production of hydrogen in these two reactors. Also it showed that volume loading rate(VLR), pH, oxidation reduction potential(ORP) and alkalinity could influence the fermentation characteristics and hydrogen yield of the anaerobic activated sludge. And the weight of the influence factors was as follows: VLR>pH values> ORP> alkalinity in continuous stirred tank reactor (CSTR),and VLR> alkalinity > pH values> ORP in the integrative biological reactor (IBR)..

[1]  Samir Kumar Khanal,et al.  Kinetic study of biological hydrogen production by anaerobic fermentation , 2006 .

[2]  Hang-Sik Shin,et al.  Hydrogen production from food waste in anaerobic mesophilic and thermophilic acidogenesis , 2004 .

[3]  Clifford Lau,et al.  Neural Networks: Theoretical Foundations and Analysis , 1991 .

[4]  Hang-Sik Shin,et al.  Biohydrogen production by anaerobic fermentation of food waste , 2004 .

[5]  N. Ren,et al.  Assessing optimal fermentation type for bio-hydrogen production in continuous-flow acidogenic reactors. , 2007, Bioresource technology.

[6]  Jie Ding,et al.  Simultaneous biohydrogen production and starch wastewater treatment in an acidogenic expanded granular sludge bed reactor by mixed culture for long-term operation , 2008 .

[7]  Haijun Yang,et al.  Continuous bio-hydrogen production from citric acid wastewater via facultative anaerobic bacteria , 2006 .

[8]  D J Choi,et al.  A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process. , 2001, Water research.

[9]  Maged M. Hamed,et al.  Prediction of wastewater treatment plant performance using artificial neural networks , 2004, Environ. Model. Softw..

[10]  M. Gevrey,et al.  Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .

[11]  Zhaobo Chen,et al.  A novel application of TPAD-MBR system to the pilot treatment of chemical synthesis-based pharmaceutical wastewater. , 2008, Water research.

[12]  Alice E. Smith,et al.  Prediction of the gas /liquid volumetric mass transfer coefficients in surface-aeration and gas-inducing reactors using neural networks , 2003 .

[13]  Duu-Jong Lee,et al.  Modeling denitrifying sulfide removal process using artificial neural networks. , 2009, Journal of hazardous materials.

[14]  Awwa,et al.  Standard Methods for the examination of water and wastewater , 1999 .

[15]  Alberto Ferrer,et al.  Comparison of different predictive models for nutrient estimation in a sequencing batch reactor for wastewater treatment , 2006 .

[16]  Ruey-Fang Yu,et al.  Applying real-time control to enhance the performance of nitrogen removal in the continuous-flow SBR system , 1998 .

[17]  Yuzuru Takamura,et al.  Development of a compact stacked flatbed reactor with immobilized high-density bacteria for hydrogen production , 2008 .

[18]  Cumali Kinaci,et al.  Modeling of submerged membrane bioreactor treating cheese whey wastewater by artificial neural network. , 2006, Journal of biotechnology.