The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process

Hubert Byliński,Andrzej Sobecki,Jacek Gębicki

doi:10.3390/su11164407

Hubert Byliński, Andrzej Sobecki + Show 1 more

Open Access

https://doi.org/10.3390/su11164407

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Journal: Sustainability	Publication Date: Aug 15, 2019
Citations: 23	License type: CC BY 4.0

Affiliation: Gdańsk University of Technology

Abstract

This paper presents the application of artificial neural networks and decision trees for the prediction of odor properties of post-fermentation sludge from a biological-mechanical wastewater treatment plant. The input parameters were concentrations of popular compounds present in the sludge, such as toluene, p-xylene, and p-cresol, and process parameters including the concentration of volatile fatty acids, pH, and alkalinity in the fermentation sludge. The analyses revealed that the implementation of artificial neural networks allowed the prediction of the values of odor intensity and the hedonic tone of the post-fermentation sludge at the level of 30% mean absolute percentage error. Application of the decision tree made it possible to determine what input parameters the fermentation feed should have in order to arrive at the post-fermentation sludge with an odor intensity <2 and hedonic tone >−1. It was shown that the aforementioned phenomenon was influenced by the following factors: concentration of p-xylene, pH, concentration of volatile fatty acids, and concentration of p-cresol.

Highlights

The presence of odorous compounds in ambient air is a serious problem, especially for the residents of areas directly adjacent to municipal plants, such as wastewater treatment ones [1,2]
A literature survey showed that the chosen compounds can be regarded as the most frequently identified volatile aromatic compounds released from wastewater sludge, which have a substantial influence on the perceived odor connected with sludge processing [32,33,34]
This paper has presented the application of artificial neural networks and decision trees to the prediction of odor properties of post-fermentation sludge from two biological-mechanical treatment plants located in the north of Poland

Summary

Introduction

The presence of odorous compounds in ambient air is a serious problem, especially for the residents of areas directly adjacent to municipal plants, such as wastewater treatment ones [1,2]. Taking into account the unit processes in wastewater treatment plants, it was found that the operations connected with sludge processing constituted the main source of odorous substance emissions [4,5]. In order to provide safety, the introduction of sludge to soil must be preceded by its dewatering, stabilization, and composting. These operations are aimed at a reduction of the risk of the uncontrolled release of hazardous chemical substances to soil and ground water [10,11]. A decrease in their emission during thermal processing can be achieved, among other things, via the co-incineration of sludge with other energetic materials (coal, crude oil) [14]

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