In the heart of Krasnoyarsk, Russia, a team of researchers led by Vladimir V. Bukhtoyarov from the Laboratory of Biofuel Compositions at Siberian Federal University has made a significant stride in the realm of vertical farming. Their work, published in *AgriEngineering* (which translates to *Agricultural Engineering*), introduces a hybrid digital twin approach that promises to revolutionize microclimate control in urban vertical farms. This innovation could have profound implications for the energy sector, particularly in optimizing energy use in controlled-environment agriculture.
Vertical farming, a burgeoning field in urban agriculture, relies heavily on precise environmental control to ensure optimal plant growth. However, managing the microclimate within these controlled environments has been a complex challenge. Bukhtoyarov and his team have tackled this issue head-on by integrating IoT sensor networks with physics-based modeling to create a hybrid digital twin. This digital twin simulates and controls the phytotron environment, a critical component in vertical farming.
The team’s approach involves a set of heat- and mass-balance equations that govern the dynamics of temperature, humidity, and transpiration. These equations were implemented and parameterized using a genetic algorithm (GA), an evolutionary optimization method. Real-time data collected over three intervals (72 h, 90 h, and 110 h) from LoRaWAN sensors (temperature, humidity, CO2) and Wi-Fi-connected power meters managed by Home Assistant were used to fine-tune the model.
The results are impressive. The optimized model achieved mean temperature deviations of ≤ 0.1 °C, relative humidity errors of ≤ 2%, and overall energy consumption accuracy of 99.5% compared to measured values. “The digital twin reliably tracked daily climate fluctuations and system energy use, confirming the accuracy of the hybrid approach,” Bukhtoyarov explained. This level of precision is a game-changer for vertical farming, where maintaining optimal growing conditions is crucial.
The implications for the energy sector are significant. By optimizing energy use in vertical farms, this technology can reduce operational costs and improve sustainability. “Our framework effectively integrates theoretical models with IoT-derived data to deliver precise environmental control and energy-use optimization,” Bukhtoyarov noted. This could pave the way for scalable digital twins in controlled-environment agriculture, making vertical farming more energy-efficient and economically viable.
The research lays the groundwork for future developments in the field. As vertical farming continues to grow, the need for precise environmental control and energy optimization will only increase. This hybrid digital twin approach offers a promising solution, one that could shape the future of urban agriculture and the energy sector alike.
In the words of Bukhtoyarov, “This is just the beginning. The potential for this technology is vast, and we are excited to see how it will be applied in the years to come.” With such promising results, the future of vertical farming looks brighter than ever.