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Software sensors for monitoring of a solid waste composting process

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Abstract

Process identification for composting of tobacco solid waste in an aerobic, adiabatic batch reactor was carried out using neural network-based models which utilized the nonlinear finite impulse response and nonlinear autoregressive model with exogenous inputs identification methods. Two soft sensors were developed for the estimation of conversion. The neural networks were trained by the adaptive gradient method using cascade learning. The developed models showed that the neural networks could be applied as intelligent software sensors giving a possibility of continuous process monitoring. The models have a potential to be used for inferential control of composting process in batch reactors.

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Authors and Affiliations

  1. Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićev trg 19, HR-10 000, Zagreb, Croatia

    N. Bolf, N. Kopčić, F. Briški & Z. Gomzi

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  1. N. Bolf
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  2. N. Kopčić
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  3. F. Briški
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  4. Z. Gomzi
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Bolf, N., Kopčić, N., Briški, F. et al. Software sensors for monitoring of a solid waste composting process. Chem. Pap. 61, 98–102 (2007). https://doi.org/10.2478/s11696-007-0005-8

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  • DOI: https://doi.org/10.2478/s11696-007-0005-8

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