An Application of Ultrasonic Waves in the Pretreatment of Biological Sludge in Urban Sewage and Proposing an Artificial Neural Network Predictive Model of Concentration

Author:

El Jery Atef1ORCID,Kosarirad Houman2ORCID,Taheri Nedasadat3ORCID,Bagheri Maryam4,Aldrdery Moutaz1,Elkhaleefa Abubakr1ORCID,Wang Chongqing5ORCID,Sammen Saad Sh.6

Affiliation:

1. Department of Chemical Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia

2. Durham School of Architectural Engineering and Construction, University of Nebraska-Lincoln, 122 NH, Lincoln, NE 68588, USA

3. School of Computing, University of Nebraska–Lincoln, Lincoln, NE 68588, USA

4. Department of Mechanical Engineering, University of Houston, Houston, TX 77004, USA

5. School of Chemical Engineering, Zhengzhou University, Zhengzhou 450001, China

6. Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah 10047, Iraq

Abstract

This research examines whether ultrasonic waves can enhance the hydrolysis, stability, and dewatering of activated sludge from raw urban wastewater. Sampling and physical examination of the activated sludge that was returned to the aeration pond were carried out using ultrasonic waves that were guided at frequencies of 30 and 50 kHz for periods of 0.5, 1, 3, 5, 10, 15, and 30 min. Various tests, including volatile suspended solids, inorganic solids, volatile solids, sludge resistant time, capillary suction time, total suspended solids, total solids, and volatile soluble solids, were carried out to advance further the processes of hydrolysis, stabilization, and dehydration of samples. According to the observations, the volatile soluble solids at a frequency of 30 kHz and t=15 min were raised by 72%. The capillary suction time of 30 and 50 kHz in 1 min demonstrated a drop of 29 and 22%, respectively. It is crucial to consider that, at 10 min and the frequency of 50 kHz, the greatest efficiency was found. The 30 kHz and 1 min yielded the optimum sludge dewatering conditions. Finally, artificial neural networks (ANN) are utilized to propose predictive models for concentration, and the results were also very accurate (MAE=1.37%). Regarding the computational costs, the ANN took approximately 5% of the time spent on experiments.

Funder

the Deanship of Scientific Research at King Khalid University

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

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