The issue of pure water is currently one of the most critical concerns facing the global population. Wastewater treatment technologies are seen as one of the solutions to these water shortages. At present, despite the advancements in water treatment technology, there are still drawbacks in terms of energy consumption, the recovery ratio, and control. In order to solve the problems mentioned above, it is also very important to develop a control system method suitable for the water treatment process. In this work, the development and implementation of a fuzzy logic approach to control industrial wastewater treatment technology using an ion-exchange resin are presented. Initially, ion-exchange resin technology was developed in the laboratory as a pilot project and tested to purify wastewater at the Kungrad Soda Plant in Uzbekistan. According to technical instructions, the hardness of purified water should not exceed 3 mEq/L, the total dissolved solids (TDS) should not exceed 40 ppm, and the pH should remain below 7.5. Based on these data, the membership functions (MFs) of the parameters were formed, and the model of controlling the process through the fuzzy logic controller was developed. The developed fuzzy logic controller model was compared with the traditional controller (PID). The energy consumption ranged from 2 to 2.5 kJ/m³, and the settling time was 40 s when the process was controlled by a PID controller. By implementing the developed fuzzy logic controller in the process, it is possible to decrease the energy consumption by 1.8–2.3 kJ/m³ and the settling time by 15 s.

Water scarcity is currently one of the world’s major issues. Water treatment technologies are a solution to the water crisis problems. In this study, the outcomes of pre-pilot scale testing of solar-powered Ion-exchange resin technology is presented. The tests were carried out using industrial wastewater at the Kungrad Soda Plant located in Kungrad, Uzbekistan. From the plant, about 1500 m³/day of waste water containing total dissolved solids (TDS) is discharged into the environment. In order to reduce the negative impact on the environment and to reuse waste water, the factory proposed conducting water purification tests from unwanted ions (Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, dissolved CO₂). During the test of the technology, water with a TDS of about 2000 ppm was passed through the ion-exchange resin and clean water of around 30 ppm was obtained (purify up to 98–99%). However, according to the requirement of the plant, a certain amount of daily water is purified and added to the total water, and no more than 1600 ppm water is produced and sent for reuse. Experiments have been successfully carried out on a pre-pilot scale using this technology.

In this work, a mathematical model of industrial wastewater treatment technology was formed. First, wastewater treatment technologies were analyzed and the technology of ion-exchange resin treatment was selected. A water purification device using ion-exchange resins was made, and tested on the Kungrad soda plant and the results were obtained. According to these results, it was determined that the cleaning coefficient of the device is up to 98-99%. In this article, a mathematical model of a wastewater treatment device using ion-exchange resins was developed using the stoichiometric matrix method. After the mathematical model of the process was found, a graph of the concentration change over time was constructed using the MATLAB program. With the help of the obtained results, we can monitor the changes in the concentration of each component participating in the process, and through this, we can optimally control the process.

This study explores the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for controlling wastewater treatment processes using ion-exchange resins. It addresses the critical challenges of water scarcity and pollution by enhancing the regulation of water hardness (H) and Total Dissolved Solids (TDS). Using a pilot laboratory device and experimental data from the mixed wastewater of the Kungrad Soda Plant in Uzbekistan, an ANFIS model was developed in MATLAB to automate process control. The model leverages water hardness and TDS as input parameters to regulate the water flow rate by servo valve opening degree, ensuring precise and efficient treatment. Compared to fuzzy logic models, ANFIS achieves a 3–4 second reduction in settling time and significantly lowers error rates. This enhanced control not only optimizes water hardness and TDS regulation but also ensures greater consistency and efficiency. Experimental results indicate that adopting ANFIS for real-time parameter adjustments can reduce energy consumption by up to 1.3%. These findings highlight the potential of ANFIS as a superior solution for controlling dynamic and nonlinear wastewater treatment processes.

Ion-exchange-based wastewater treatment processes exhibit nonlinear and time-varying dynamics, making the control of total dissolved solids (TDS) and water hardness a complex task. Conventional Proportional–Integral–Derivative (PID) controllers often show limited performance under such conditions due to fixed tuning parameters and linear assumptions. To address these limitations, this study presents a comparative evaluation of traditional and intelligent control strategies for regulating TDS and water hardness through influent flow control. A classical PID controller is compared with fuzzy logic and Adaptive neuro-fuzzy inference system (ANFIS) controllers using a unified MATLAB/Simulink simulation framework. The control performance is evaluated based on dynamic response characteristics, including rise time, settling time, and overshoot. For TDS control, the PID controller exhibits a rise time of 15.9 s and a settling time of 50.9 s, while the fuzzy logic controller improves the response with a rise time of 13.6 s and settling time of 44.1 s. The ANFIS controller achieves the fastest response, with a rise time of 8.31 s and a settling time of 27.1 s. Similar trends are observed for water hardness control, where the PID controller shows a rise time of 17.0 s and settling time of 55.8 s, the fuzzy logic controller reduces these values to 12.3 s and 40.4 s, respectively, and the ANFIS controller further improves performance with a rise time of 9.23 s and settling time of 30.3 s. The overshoot values for all controllers remain comparable, within the range of approximately 4.4–5.0%. The results clearly demonstrate that intelligent control strategies, particularly ANFIS, provide significantly faster convergence and improved dynamic performance compared to conventional PID control. The reduced settling time implies lower control effort and decreased energy consumption, highlighting the potential of intelligent controllers for efficient and reliable industrial wastewater treatment applications.