Recently, a high-end equipment enterprise, in collaboration with a research team from the Chinese Academy of Sciences Institute of Process Engineering, has launched an intelligent laboratory reactor equipped with a digital twin system. By leveraging virtual-physical mapping and AI-based autonomous decision-making technologies, the device addresses long-standing industry challenges—such as delayed risk prediction and passive parameter adjustment—in traditional reactors handling high-pressure and flammable reactions. It provides comprehensive safety assurance for laboratory research in high-risk fields like pharmaceutical intermediates and energetic materials.

　　According to the lead researcher, traditional laboratory reactors rely on manual, intermittent monitoring of temperature and pressure parameters during hazardous reactions like nitration and hydrogenation. Emergency measures are often only activated after anomalies occur, posing significant safety risks. The newly developed intelligent reactor innovatively establishes a "six-dimensional digital mirror" system, integrating 328 micro-sensor nodes to real-time capture data such as temperature gradients and medium concentration. Using a Kalman filter algorithm, it achieves 0.1°C-level thermal field variation detection and reconstructs the entire reaction process in a virtual space.

　　In terms of core technological breakthroughs, the reactor’s AI control system draws on the multi-physics coupling simulation experience of Wanhua Chemical. It can automatically identify the optimal reaction curve based on raw material composition. The "external magnetic drive + shaftless sealing" design maintains the advantages of leak-free technology. Combined with the HHT-Hilbert time-frequency analysis algorithm, the system can detect potential failures such as micro-cracks in the stirring shaft with a 37-minute early warning. Leakage rates are controlled below 0.001 mL/h, forming a "dual leak-free" safety loop alongside the magnetic stirrer of interest.

　　In a simulated experiment of nitrobenzene hydrogenation to aniline, the reactor predicted the reaction exotherm peak via the digital twin system and autonomously adjusted cooling water flow, reducing by-product generation by 42% and increasing the target product conversion rate from 87.3% to 94.1%. In high-pressure polymerization tests, the device achieved coordinated control of rotational speed (50–1500 rpm) and pressure (0.1 MPa increments), with experimental data reproducibility reaching 99.5%.

　　“This device transforms the reactor from a 'passive operation tool' into an 'active safety guardian',” said Researcher Zhang Ming from the Chinese Academy of Sciences. Currently, the equipment has been adopted by three energetic materials laboratories. The next step involves integrating a horizontal federated learning framework to enable secure, multi-laboratory training on hazardous reaction data. An upgraded version adapted for sterile environments in biopharmaceuticals is expected within the year.