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Software sensors for monitoring of a solid waste composting process

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

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



Received: 3 April 2006  Revised: 1 October 2006  Accepted: 8 October 2006

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.

Keywords: software sensor - neural network - modeling - solid waste composting - aerobic bioreactor

Full paper is available at

DOI: 10.2478/s11696-007-0005-8


Chemical Papers 61 (2) 98–102 (2007)

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