A fault-tolerant control strategy to estimate and compensate the temperature sensor bias in supermarket refrigeration systems.

The paper proposes a data-driven fault-tolerant control (FTC) strategy to construct and accommodate the bias on ambient temperature measurements in supermarket refrigeration systems. The bias, which is caused by direct or indirect exposure of the sensor to the sun, can have a significant impact on the refrigeration system's energy consumption. Based on analysis of the real data a comprehensive model of the bias is developed and then used to generate realistic scenarios for testing the proposed FTC method. The FTC method uses a feed forward Neural Network (NN) as a black box model. The model is trained by active injection of perturbation signals during the night operations. During the Monte-Carlo tests, the strategy was implemented in a Plug & Play manner, demonstrating that substantial energy savings can be achieved during summer periods.

Design and Manufacture of a Micro-Ejector and the Testing Stand for Investigation of Micro-Ejector Refrigeration Systems.

This paper describes the procedure of design and manufacture of a micro-ejector proposed for miniature ejection refrigeration systems. It describes the procedure of design, fabrication, and experimentation on supersonic micro-ejectors and makes the case for isobutane as a working fluid for such systems. It was demonstrated that it is possible to design and fabricate a micro-ejector with a cooling capacity of approximately 3 W. The discussed micro-ejector was driven by a heat source with temperature below 60 °C. The evaporation temperature was approximately 15 °C. For these operating parameters, the reported entrainment ratio was approximately 0.20. The difficulties in fabricating the micro-ejector due to its small dimensions are discussed in the paper. Additionally, the potential difficulties and solutions related to ensuring and maintaining stable operation of the testing stand are presented. The performance of the proposed system is demonstrated and discussed, including relations between mass entrainment ratio, compression ratio, cooling capacity, and temperature.

Real-time temperature anomaly detection in vaccine refrigeration systems using deep learning on a resource-constrained microcontroller.

Maintaining consistent and accurate temperature is critical for the safe and effective storage of vaccines. Traditional monitoring methods often lack real-time capabilities and may not be sensitive enough to detect subtle anomalies. This paper presents a novel deep learning-based system for real-time temperature fault detection in refrigeration systems used for vaccine storage. Our system utilizes a semi-supervised Convolutional Autoencoder (CAE) model deployed on a resource-constrained ESP32 microcontroller. The CAE is trained on real-world temperature sensor data to capture temporal patterns and reconstruct normal temperature profiles. Deviations from the reconstructed profiles are flagged as potential anomalies, enabling real-time fault detection. Evaluation using real-time data demonstrates an impressive 92% accuracy in identifying temperature faults. The system's low energy consumption (0.05 watts) and memory usage (1.2 MB) make it suitable for deployment in resource-constrained environments. This work paves the way for improved monitoring and fault detection in refrigeration systems, ultimately contributing to the reliable storage of life-saving vaccines.