CEST Proceedings are published under the ISSN 2944-9820.
Statistical prediction models for the odour emissions quantification in terms of odour concentration: Analysis and comparison
(Corresponding) Galang M., Ballesteros Jr. F., Zarra T., Naddeo V., Belgiorno V.
Topic:
Environmental odour, monitoring and control
Measuring odour concentration is a significant step to achieve efficient environmental odour management in continuous, objective and repeatable manner. To deal with this, researchers developed instrumental odour monitoring systems (IOMS) by applying odour monitoring models (OMM) for prediction. At...Read more
Application of Risk Analysis to landfills: the experience of Brescia (Italy)
(Corresponding) Vaccari M., Gibellini S., Arnaboldi G.
Topic:
Solid waste management
In the Italian authorization procedure for the construction of a landfill, the Risk Analysis is mandatory in case of request for derogation from the current legislation (D.M. September 27, 2010), in order to demonstrate that there is no risk for the environment. This contribution describes the...Read more
Material flow analysis applied to a waste treatment plant in Lombardy (Italy)
(Corresponding) Castro F., Fabbri M., Vaccari M., Cutaia L.
Topic:
Solid waste management
Material flow analysis (MFA) allows to quantify inputs, outputs and stocks of a system and to communicate results visually. It can be used for calculating process effectiveness, losses and for identifying critical points within a system, being useful for strategically intervening in a corporative...Read more
A Machine Learning Approach for the prediction of solid fuels consumption in Turkey
Celik N., (Corresponding) Konyalioglu A.
Topic:
Environmental data analysis and modelling
Solid fuels are very crucial energy sources as most of industries use them for obtaining heat, electricity and light. Furthermore, since solid fuels are scarce sources in Turkey, it is very important to forecast the consumption in order to effectively manage the energy policies and to conduct an...Read more
Keywords:
Solid Fuels, Environmental Data Analysis, Machine Learning
