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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 presented a novel ANN that accurately predicts the steady-state performance of bioreactors for the bio-hydrogen producing processes. In this experiment, producing hydrogen from sugar refinery wastewater was studied in the integrative biological reactor (IBR). 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 the reactor. 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 followed: VLR> alkalinity > pH values> ORP in the IBR.