Journal of Science and Innovative Development ISSN 2181-4317

DEVELOPMENT OF A CONTROL MODEL OF WASTEWATER TREATMENT PROCESS BASED ON A FUZZY INFERENCE SYSTEM

Eshbobayev Jaloliddin Abdurazzaqovich April 13, 2024 DOI: https://doi.org/10.36522/ILM-FAN/7-2-2024-5cf1c

Abstract

<p>Proper process control practice can be regarded as one of effective solutions to such problems as improving wastewater treatment technologies, reducing energy consumption as well as lowering the cost of water and increasing its quality. As a result of this work, wastewater treatment technology using ionexchange resins has been developed, and the device - tested in the process of treating the wastewater mixture at the Kungrad soda factory. According to the results of the tests taken place at the factory, the total dissolved solids in the waste water decreased from 1885 mg/l to 27.3 mg/l. Overall hardness reduced from 9.3 to 0.27. The pH went down from 9.5 to 7.5. However, the energy consumption parameters went up to 2.5 from 1.8 kWh for purifying 1 m3 of wastewater, that needs to be reduced. This article reviews development of a model for controlling the process of wastewater treatment using ion-exchange resins based on fuzzy logic. The main parameters are the total dissolved solids in the water, the pH indicator, and the total hardness of the water. Based on the experimental findings retrieved from the device, a simulation model of control on these parameters based on fuzzy logic was built using the Matlab software. Findings show that the developed model enables control on concentrations of total salts in water with purity up to 99&thinsp;&thinsp;%, as well as reduction of settling time to 20 seconds. Minimization of energy consumption from an average of 1.8-2.5 kWh to 1.2-1.8 kWh was achieved by reducing the adjustment time.</p>

Cite this article
Eshbobayev Jaloliddin Abdurazzaqovich (2024). DEVELOPMENT OF A CONTROL MODEL OF WASTEWATER TREATMENT PROCESS BASED ON A FUZZY INFERENCE SYSTEM. Journal of Science and Innovative Development. https://doi.org/https://doi.org/10.36522/ILM-FAN/7-2-2024-5cf1c