Artificial neural networks for modeling ammonia emissions released from sewage sludge composting

Influence of maize straw content with sewage sludge on composting process

“…The sewage sludge in all chambers were aerated by the air flow of 3.5 dm 3 ·min -1 . Two types of chemo-electrical sensors MG-72 (scale 0-1000 ppm) from Alter S.A. firm have been used in the experiments [BONIECKI et al 2012]. The gauge Metex M3870D allowed to read the data subsequently.…”

Model of the sewage sludge-straw composting process integrating different heat generation capacities of mesophilic and thermophilic microorganisms

“…The complexity of this process is determined by the dynamic interactions between biological, chemical and physical mechanisms occurring within highly heterogeneous compost matrix (Fontenelle et al, 2001;Sole-Mauri et al, 2007). From bioengineering point of view, temperature, is one of the main factors, which has the greatest influence on the composting process and its final composition, followed by other strongly connected key parameters such as C:N ratio, oxygen concentration, pH, moisture content of the substrate, particle size and porosity (Boniecki et al, 2012;Bongochgetsaku and Ishida, 2008;Mengchun et al, 2010;Seng and Kaneko, 2011;Schaub and Leonard, 1996). Due to wide range of environmental variables, it is challenging to control composting process, thus, the framework integrating important elements of this biological system should be developed.…”

Neural image analysis for maturity classification of sewage sludge composted with maize straw

“…The use of maize straw as addition to the composted sewage sludge allows to improve its structure as it provides an adequate porosity much longer than the typical cereal straw. Due to improvement of the C:N ratio by introducing a large number of readily available organic carbon it is also visible a significant reduction of ammonia emissions from composted sewage sludge (Boniecki et al, 2012a).…”

“…In fact, in recent years there have been few studies where these tools were used for such investigations. Artificial neural networks were used for the prediction of ammonia emission from the composted biomass (Boniecki et al, 2012a), the prediction of heat loss and the classification of the degree of maturity on the basis of selected physical and microbiological parameters (Gao et al, 2007). They were also used for the optimisation of the composting process based on the achievement of appropriate courses of the curves of variance in pH, temperature and CO 2 concentration (Díaz et al, 2012).…”