Abstract
Abstract The optimisation of the flocculation process during fibre cement production is a new key issue for the fibre cement industry. Many companies face difficulties in optimising the flocculant dosage in real time, which leads to product strength losses. This paper shows the feasibility of using artificial neural networks (ANNs) to establish correlations between flocculation data, in-line measured in a Hatschek machine by a focused beam reflectance measurement (FBRM) sensor, and mechanical properties of final composites. The results show a clear relationship between the mechanical properties of fibre cement composites and the flocculation process and that these are determined in real time. Three ANNs have been created to predict breaking load for 48 h and 7 days and bending strength for 7 days, to obtain good correlations between the predicted and the real values.
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