Data base from a Review on Solar Dryer Technologies Thermal Energy Storage Automation and AI
Description
This dataset is a literature-based compilation derived from 81 scientific publications focused on solar drying technologies, hybrid solar dryers, photovoltaic-thermal (PV/T) systems, and IoT-enabled monitoring and control applications in agriculture. The dataset systematically extracts and organizes information on the technologies, sensors, control systems, communication architectures, actuators, measured variables, and performance indicators reported in each study. The primary objective of the dataset is to identify trends and relationships between sensor technologies, control devices, control algorithms, and the operational performance of solar drying systems. It provides a valuable resource for researchers working in smart agriculture, renewable energy systems, process automation, and Industry 4.0 applications. The dataset contains information on: Bibliographic data: authors, publication year, journal, citations, and study location. System characteristics: solar dryers, hybrid solar-electric dryers, PV-integrated dryers, greenhouse dryers, and photovoltaic-thermal systems. Sensors and instrumentation: DHT22, DS18B20, thermocouples, pyranometers, solarimeters, anemometers, humidity sensors, load cells, and pressure sensors. Control hardware: Arduino Uno, Arduino Mega 2560, ESP32, ESP8266, Raspberry Pi, PLCs, and microcontroller-based platforms. Communication technologies: Wi-Fi, LoRa, MQTT, Bluetooth, GSM/GPRS, WLAN, XBee, Node-RED, and IoT platforms. Control algorithms: PID, PI, PWM, Fuzzy Logic Controllers (FLC), Artificial Neural Networks (ANN), ANFIS, genetic algorithms, machine learning techniques, and optimization methods. Actuators: fans, blowers, heaters, relays, motors, valves, compressors, exhaust systems, and automated doors. Performance indicators: drying efficiency, thermal efficiency, exergy efficiency, energy consumption, drying time, moisture reduction, and product quality metrics. Common Measured Variables To use this data please cite: Caro Ruiz, Claudia and Rincon, Sonia Lucia and Escobar Ochoa, Mario and Cuervo, Sandra and Sabogal, Leidy, A Review on Recent Solar Dryer Technologies: Thermal Energy Storage, Automation and AI. Available at SSRN: https://ssrn.com/abstract=6630829 or http://dx.doi.org/10.2139/ssrn.6630829Caro Ruiz, Claudia and Rincon, Sonia Lucia and Escobar Ochoa, Mario and Cuervo, Sandra and Sabogal, Leidy, A Review on Recent Solar Dryer Technologies: Thermal Energy Storage, Automation and AI. Available at SSRN: https://ssrn.com/abstract=6630829 or http://dx.doi.org/10.2139/ssrn.6630829
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Institutions
- Universidad Nacional de ColombiaBogota D.C., Bogotá