This paper proposes an embedded Internet of Things (IoT) system for bioreactor sensor integration, aimed at optimizing temperature and turbidity control during cell cultivation. Utilizing an ESP32 development board, the system makes advances on previous iterations by incorporating superior analog-to-digital conversion capabilities, dual-core processing, and integrated Wi-Fi and Bluetooth connectivity. The key components include a DS18B20 digital temperature sensor, a TS-300B turbidity sensor, and a Peltier module for temperature regulation. Through real-time monitoring and data transmission to cloud platforms, the system facilitates advanced process control and optimization. The experimental results on yeast cultures demonstrate the system’s effectiveness at maintaining optimal growth, highlighting its potential to enhance bioprocessing techniques. The proposed solution underscores the practical applications of the IoT in bioreactor environments, offering insights into the improved efficiency and reliability of culture cultivation processes.

The present paper investigates the optimal control of the photosynthetic growth process in an artificial light photobioreactor operated in batch mode, the objective being to find an optimal incident light intensity for which the consumption of light energy, for any amount of newly formed biomass, is minimal. By using a simple and reliable model for the photosynthetic growth of microalgae of microalgae, predictions can be made on the quantity of produced biomass and on the amount of light consumed, whose ratio gives the biomass yield on light energy. This variable is unimodal on the allowed range of incident light intensities and has been used as objective function. An improved objective function is proposed by using the specific growth rate and a weighing factor that allows obtaining the desired amount of biomass while the light energy consumption is optimal. A closed-loop control structure has been designed based on the developed optimization algorithm. The optimal controller has been validated in simulation, comparing different lengths of the optimization horizon and the sampling period. It was found that a bigger sampling period, for the cases where there is no online information on the biomass concentration, does not significantly affect the productivity. The optimization algorithm can be used either online or offline, being useful for various experimental setups.

Efficient and safe charging of lithium-ion batteries is essential for maximizing their lifespan and performance. This paper presents the design and implementation of a microcontroller-based Li-ion battery charger that employs real-time monitoring, adaptive charging strategies, and built-in safety mechanisms. The system integrates a CC/CV charging approach with automatic current regulation, overcharge protection, and reverse polarity detection. A current sensor module ensures continuous monitoring, while an LCD interface provides real-time feedback on charging parameters. Experimental validation was conducted using multiple Li-ion cells in various conditions, like new, aged, and deeply discharged, to evaluate charging behavior and safety under different scenarios. The system successfully regulated current and voltage, managed preconditioning for low-voltage cells, and transitioned smoothly between charging phases. A key contribution of this work is the development of a low-cost, microcontroller-based platform that enables flexible implementation and testing of diverse charging strategies. Its open-source architecture and modular design make it highly suitable for research, educational use, and experimental development in battery management systems. Future enhancements may include the integration of adaptive algorithms based on internal resistance and temperature, enabling smarter and more efficient charging.

An important element in communications systems is the router. The paper proposes a remote temperature monitoring system for it. This was implemented based on Arduino Uno development platform. In addition, an Android user-friendly interface has been created; it can be installed on a smart terminal (phone or tablet) and its role is to display the router temperature and to notify the user if it has exceeded a maximum set value. There are two methods of notifications: one is using different colors for the displayed temperatures and the other using an alert. The temperature is measured with a sensor and the data is then processed by the microcontroller and sent to the Bluetooth module using serial interface. Also, the measured values can be seen on an LCD display. The hardware, software designs and the simulation and experimental results of the proposed system are presented.

This paper presents the development and experimental validation of a low-cost IoT-enabled turbidity monitoring platform intended for laboratory-scale bioprocess applications. The proposed system was designed as a modular turbidity acquisition subsystem that can be integrated into broader bioreactor automation platforms. The hardware architecture is based on an ESP8266 microcontroller, a TS-300B optical turbidity sensor, a resistive voltage divider for analog signal conditioning, an OLED display for local visualization, and a Google Sheets-based cloud logging solution. A blank-based relative Turbidity Index was defined in order to compensate for optical configuration and environmental variations. The embedded firmware implements multi-sample averaging, blank calibration, serial command control, local display updates, CSV logging, and optional cloud transmission through HTTP requests. The calibration procedure was performed using serial dilutions of a yeast suspension, and the obtained data were fitted using a nonlinear power-law model and a log-log representation. An additional comparison with OD600 reference measurements showed a monotonic relationship between the proposed Turbidity Index and conventional optical-density measurements. The system was further validated through a yeast-based monitoring experiment performed under consistent optical conditions. The results showed the capability of the platform to acquire, process, visualize, and store turbidity-related data over an extended interval. The proposed platform provides a practical, affordable, and reproducible solution for turbidity monitoring and IoT-based data acquisition in small-scale bioprocess applications.