Tomaž LEVSTEK and Miran LAKOTA
pp. 15-22

In the survey, we summarized some of the most important researches on assessment and forecasting components of biogas and the substrate in the process of anaerobic digestion using artificial neural networks (ANNs). Here we consider especially hydrogen sulfide, ammonia, hydrogen, methane in biogas and heavy metals in substrate. The results show high prediction accuracy and usefulness of the ANNs. The predicted removal efficiency of a biofilter for treating hydrogen sulphide with ANN was validated with determination coefficient of 0.92. A simulating model for the performance of a granule-based H2-producing was able to effectively describe the daily variations of the reactor performance, and to predict the steady-state reactor performance at various substrate concentrations using ANN network and genetic algorithm. The values of training determination coefficients for H2 concentration in the biogas was 0.966, H2 production rate 0.810, H2 yield 0.882. The ANN learned the relationship between input and output well. The ANN model to predict the methane production on the basis of operational parameters was validated with correlation coefficient of 0.87. A back propagation artificial neural network (BP-ANN) algorithm for the simultaneous spectrophotometric determination of the cobalt and nickel complexes show the good regression between actual values and prediction values for cobalt and nickel concentration.

Key words: artificial neural networks, anaerobic digestion, prediction, biogas

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