Journal of Computer Electronic and Telecommunication

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

2025-02-03T01:58:40+00:00

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.

A Simple Modeling of MPPT-based ANN for Photovoltaic System

2025-06-18T02:04:09+00:00

This research describes a simple modeling technique for Maximum Power Point Tracking based on Artificial Neural Network (MPPT-based ANN) for photovoltaic (PV) systems. The proposed ANN model utilizes a feed-forward backpropagation architecture. The PV system was developed and tested in a simulation environment under uniform irradiation levels of 1000 W/m², 800 W/m², and 600 W/m², and rapidly varying irradiation changes. The simulation results demonstrate that the MPPT-based ANN accurately tracks the MPP, achieving stable power outputs of 98.36 W, 79 W, and 57.45 W, respectively. Although the system experiences initial transient oscillations during the tracking phase, it stabilizes within 80 milliseconds, showcasing rapid convergence and high steady-state accuracy. Under dynamic conditions, the MPPT-based ANN adapts effectively to fast-changing irradiation, restarting the algorithm to track and maintain the system at the updated MPP accurately. These results highlight the reliability, adaptability, and suitability of the MPPT-based ANN for real-time applications in dynamic environments. Nonetheless, further improvements to the ANN model are suggested to minimize transient oscillations and enhance overall performance.

Water Quality Control System In Goldfish Aquarium Using Fuzzy Method

2025-06-19T13:12:01+00:00

Considering the beauty and unique characteristics of goldfish as ornamental fish, keeping goldfish in an aquarium is a popular hobby among the community. However, some people face challenges in maintaining goldfish that must be controlled manually. In this work, the author proposes to create a water quality control system for goldfish aquariums using fuzzy logic. This system uses the E-201-C pH sensor to measure the water's pH level, the SEN-0189 turbidity sensor to detect water turbidity, an ultrasonic sensor to maintain water height, and the ESP32 as the microcontroller. In the pH control, a mini pump is used, which activates when the pH level is >9 to lower the water's pH to the set point. Meanwhile, for controlling water turbidity, two 12V DC pumps are used, where one pump functions to discharge turbid water and the other to fill with clean water. The data read by the sensor can be monitored through the OLED screen. Based on the test results, the water quality control system for the goldfish aquarium using the fuzzy method can function well. Meanwhile, the water draining process takes 5 minutes and 20 seconds, and the clean water filling takes about 15 minutes and 24 seconds.

Wi-Fi Enabled Remote Control Surveillance Vehicle: Design, Implementation, and Performance Analysis

2025-06-02T05:30:55+00:00

The rapid advancement of wireless technology has expanded the possibilities for remote-controlled systems, particularly in surveillance and safety applications. This study aims to develop a 4G-enabled remote surveillance vehicle using a Raspberry Pi 4 Model B microprocessor to achieve long-range control and real-time visual feedback. The vehicle integrates a Raspberry Pi 4 with two Electronic Speed Controllers (ESCs) connected to three gearbox motors for movement, along with an OV5647 camera module mounted on a 2-axis gimbal controlled by MG90S servos. The system is programmed in JavaScript using Node.js and Visual Studio Code, enabling a webserver for bidirectional communication between the vehicle and the controller. Key tests demonstrated a maximum operational range of 1.11 km, with the potential for further distance as connectivity permits. The vehicle exhibited an average battery life of 46 minutes and a latency of approximately 49 ms under stable 4G conditions. Additionally, it successfully traversed diverse terrains, including gravel and sand. The findings highlight the vehicle's capability for remote surveillance in hazardous or inaccessible environments, reducing human risk. Future enhancements could include integrating additional sensors for broader applications. This research underscores the feasibility of using cost-effective, off-the-shelf components to build a robust, long-range surveillance system

Power Factor Correction on 500W Inverter Microcontroller-Base Using Particle Swarm Optimization Method

2025-02-07T02:24:17+00:00

This research explores the design and evaluation of an inverter system incorporating the Particle Swarm Optimization (PSO) method to enhance power factor efficiency. The study investigates the inverter’s performance across resistive loads (40W and 100W lamps), inductive loads (40W fan and 200W blenders), and a combination of resistive-inductive loads, both with and without PSO-based Power Factor Correction (PFC). By optimizing the phase difference between voltage and current, the PSO algorithm aims to maintain a power factor close to the industry standard of 0.85 or higher. The findings indicate that resistive loads consistently sustain a power factor of 1.00, while inductive loads benefit significantly from PSO implementation. The 40W inductive fan, initially operating at 0.55, improved to 0.57 – 0.60, whereas the 200W inductive blender increased from 0.90 to 0.98. Similarly, mixed resistive-inductive loads showed an enhancement from 0.89 to 0.99, emphasizing PSO’s role in improving power efficiency. The study recorded a total power factor improvement of 0.36, with an average increase of 0.0144 per test case, confirming PSO’s effectiveness in reducing reactive power losses and optimizing energy conversion. These results highlight the potential of PSO-based control strategies in enhancing power quality, stabilizing inverter performance, and improving energy efficiency, particularly in applications where inductive loads are predominant. The research contributes to the development of intelligent inverter systems that offer greater reliability, cost-effectiveness, and energy savings for residential and industrial power applications.