Evolução do comportamento dos resíduos sólidos urbanos em aterro sanitário sob aspectos mecânicos.

Abstract

The operation of sanitary landfills is benefited when the degradation state of the residues is known, inside and in the different stages of its life cycle, since, with time, the mechanical and physical-chemical characteristics undergo modifications. The objective of this research was to relate the evolution of MSW behavior in different stages of grounding under mechanical aspects in Sanitary Landfill in Campina Grande (ASCG). The methodology included the collection and physical-chemical and mechanical characterization of the waste that arrive at the ASCG, from different municipalities in the states of Paraíba, Pernambuco and Rio Grande do Norte. The temporal behavior of the waste was also carried out through the analysis of the geotechnical and physical-chemical characteristics of the MSW that arrived at the ASCG, and also of the waste recently landfilled and with 1 and 2 years of landfill. The physical-chemical and geotechnical data obtained from grounded waste aged 0 years or more were collected prior to the making of this research by the Environmental Geotechnical Group (GGA). A model was developed using Artificial Neural Networks (ANN) to estimate the theoretical gravimetric composition of the MSW and the confrontation of this estimate with the real composition measured by the physical characterization. The results of the waste characterization show that in gravimetric terms the fraction of organic matter is higher when evaluating the waste that arrives at the ASCG, however, this percentage suffers a sudden decrease of 880% after grounding. The physical-chemical characterization revealed that the MSW, which arrive and recently landed in the ASCG, are similar, however, a rapid biodegradation occurs. Waste that has been landfilled for 1 and 2 years is equivalent in physical-chemical and mechanical aspects to that of old landfills. Multivariate statistical analyzes allowed, through Pearson's linear correlation and Principal Component Analysis, to verify that the residuals present parameters that form isolated groups that reflect positive and negative correlations between themselves. These correlations show the temporal behavior of the residues under different aspects and demonstrate that the ASCG provides a fast process of change in the physical characteristics of the residues. The ANN models proved to be an efficient and viable tool in predicting the gravimetric composition of the waste, which can be used by other municipalities.