A Smart Fuzzy System Applied to Reduce Odour Production from a Waste Landfill

Control and management of odours from landfills is becoming a relevant topic for both the public and the technical community, as numerous landfills are located nearby highly urbanised areas. The attention is focused on how odour emissions can be managed and mitigated to reduce the odour nuisance. Th...

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Bibliographic Details
Main Authors: G.F. Santonastaso, I. Bortone, S. Chianese, A. Di Nardo, M. Di Natale, D. Musmarra
Format: Article
Language:English
Published: AIDIC Servizi S.r.l. 2014-09-01
Series:Chemical Engineering Transactions
Online Access:https://www.cetjournal.it/index.php/cet/article/view/5257
Description
Summary:Control and management of odours from landfills is becoming a relevant topic for both the public and the technical community, as numerous landfills are located nearby highly urbanised areas. The attention is focused on how odour emissions can be managed and mitigated to reduce the odour nuisance. This paper proposes a Smart Fuzzy System (SFS) to mitigate the odour production of a solid waste landfill, called “Cava del Cane”, in North of Naples (Italy), as the area surrounding the landfill is densely populated, with the presence of hospitals, schools, and sports centres. The SFS proposed is based on a fuzzy approach that, via a certain number of input variables (i.e. the direction, the intensity and variation of wind), allows to obtain the mitigation actions to be applied in a landfill (e.g., waste covering, spraying of perfumed substances, placement of extraction wells to maintain waste under pressure, etc.) in order to reduce its odour emissions. The odour transport modelling was carried out by using CALPUFF model (U.S. Environmental Protection Agency (U.S.EPA)). CALPUFF allowed to assess the dispersion of odours in air as a function of the odour emission rates from the landfill and the weather conditions of the area. The input variables considered in the SFS were described via some “membership functions”, and the mitigation actions (output) were inferred by using a set of 16 fuzzy rules based on human expertise. The fuzzy system was optimized by using a Genetic Algorithm (GA) with some constraints to preserve the physical meaning of all parameters considered. The simulation results showed a good effectiveness of the SFS, as a significant reduction of threshold exceeds was obtained in the whole area.
ISSN:2283-9216