Water Monitoring and Control System in Krofta System with Fuzzy Logic Method

Yury Novian Ramadani; Ryan Yudha Aditya; Ii Munadhif; Isa Rachman; Eng Imam Sutrisno; Muhammad Khoirul Hasin

doi:10.52435/complete.v6i1.660

Authors

Yury Novian Ramadani Department of Automation Engineering, Shipbuilding Institute of Polytechnic Surabaya, Surabaya, Indonesia
Ryan Yudha Aditya Department of Automation Engineering, Shipbuilding Institute of Polytechnic Surabaya, Surabaya, Indonesia
Ii Munadhif Department of Automation Engineering, Shipbuilding Institute of Polytechnic Surabaya, Surabaya, Indonesia
Isa Rachman Department of Automation Engineering, Shipbuilding Institute of Polytechnic Surabaya, Surabaya, Indonesia
Eng Imam Sutrisno Department of Automation Engineering, Shipbuilding Institute of Polytechnic Surabaya, Surabaya, Indonesia
Muhammad Khoirul Hasin Department of Automation Engineering, Shipbuilding Institute of Polytechnic Surabaya, Surabaya, Indonesia

DOI:

https://doi.org/10.52435/complete.v6i1.660

Keywords:

internet of things, electro, esp32, fuzzy logic, sensor, mysql, MySQL

Abstract

Krofta is a water purification technology widely used in industries, particularly in paper and tissue manufacturing. In this study, a Fuzzy logic-based control method is applied to the input and output parameters of the system. The developed system utilizes a Sugeno Fuzzy system with three main inputs: TSS (Total Suspended Solids), pH, and temperature, and an output parameter in the form of PWM (Pulse Width Modulation) to control the booster pump for injecting chemicals to maintain water quality. The water purification process involves the injection of a chemical agent, specifically a fennopol solution, which is pumped by a booster pump. The booster pump is controlled by an AC Dimmer module driver based on the PWM output generated by the Fuzzy method. During this process, data from each parameter is recorded in real-time using a MySQL database and displayed via a web interface, with both components interconnected. Based on the research findings, the accuracy results for the sensors are as follows: the temperature sensor has an average error of 2.736%, the pH sensor has an average error of 1.742%, and the TSS sensor has an average error of 4.10%. For the PWM parameter, the system achieves highly accurate PWM values, effectively optimizing the tested water parameters. In conclusion, the Sugeno Fuzzy method demonstrates an average accuracy of 97.1% in monitoring and controlling the system to support decision-making processes.

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