Artificial Neural Networks in Modeling of Dewaterability of Sewage Sludge

Mariusz Kowalczyk,Tomasz Kamizela

doi:10.3390/en14061552

Abstract

Mechanical dewatering is a key process in the management of sewage sludge. However, the drainage efficiency depends on a number of factors, from the type and dose of the conditioning agent to the parameters of the drainage device. The selection of appropriate methods and parameters of conditioning and dewatering of sewage sludge is the task of laboratory work. This work can be accelerated through the use of artificial neural network (ANNs). The paper discusses the possibilities of using ANNs in predicting the dewatering efficiency of physically conditioned sludge. The input variables were only four parameters characterizing the conditioning methods and the dewatering method by centrifugation. These were the dose of the sludge skeleton builders (cement, gypsum, fly ash, and liquid glass), sonication parameters (sonication amplitude and time), and relative centrifugal force. Dewatering efficiency parameters such as sludge hydration and separation factor were output variables. Due to the nature of the research problem, two nonlinear networks were selected: a multilayer perceptron and a radial neural network. Based on the results of the prediction of artificial neural networks, it was found that these networks can be used to forecast the effectiveness of municipal sludge dewatering. The prediction error did not exceed 1.0% of the real value. ANN can therefore be useful in optimizing the dewatering process. In the case of the conducted research, it was the optimization of the sludge dewatering efficiency as a function of the type and parameters of conditioning factors. Therefore, it is possible to predict the dewatering efficiency of sludge that has not been tested in the laboratory, for example, with the use of other doses of physical conditioner. However, the condition for correct prediction and optimization was the use of a large dataset in the neural network training process.

Highlights

Chemical substances such as aluminum sulfate, iron (III) chloride, iron (II) sulfate, and polyelectrolytes are usually added to sludge in order to improve the dewaterability.Currently, the most common method of pretreating sludge before the dewatering process is adding polyelectrolyte [1]
The separation factor was once again reduced and did not exceed 85%. This might have been caused by the dispersing effect of the ultrasounds and small particles of components which remain suspended during centrifugation and do not settle under the centrifugal force
Based on the laboratory analysis and mathematical modeling with the use of artificial neural networks, the following conclusions were formulated: The effective conditioning and dewatering of the sludge were influenced by the dose of the skeleton builders, the sonication parameters, and the value of the relative centrifugal force

Summary

Introduction

The most common method of pretreating sludge before the dewatering process is adding polyelectrolyte [1]. A number of alternative methods (chemical, physical, and biological) for sewage sludge conditioning are proposed in order to improve the dewatering efficiency [2]. Physical conditioning methods include the addition of skeleton builders of sludge floc such as gypsum and ash [3] and sludge sonication [4]. Zhao et al used the addition of gypsum in the amount of 60% of the total solids of conditioned sludge, which resulted in a 4.3% reduction in the sludge hydration after the dewatering process [5]. The use of the most optimum dose of coal dust, amounting to 274%

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Discussion

Conclusion