Research Groups

  • Division of Mesoscience

    With the focus on mesosicence, this division is devoted to solving the common challenges at the frontiers of process engineering, by establishing the multi-scale theories, and developing efficient and accurate simulation and experimental methods for multiphase complex systems. The complex systems studied include: gas-solid system, gas-liquid system, granular matter, turbulence, etc.

    In 2020, the division conducted more than 60 projects from NSFC, CAS and industries, published 64 papers, and organized the 3rd Meeting of International Panel of Mesoscience (online). The main structure of two platforms for mesoscience and virtual process engineering constructed in Huairou Science City has been capped. 

    Breakthough IMesoscience research powers continuous upgrade of the process for clean gasoline production. According to the characteristics of MIP process such as the coexistence of multiple reaction zones, the new drag correlation based on the typical steady-state EMMS (Energy-Minimization Multi-Scale) model was built (Chem. Eng. Sci. 2020, 219, 115616) and the ANN (artificial neural network)-based machine learning was then adopted to obtain a high-dimensional drag model, thus providing the theoretical basis for further upsizing the MIP reactor and operation in multiple flow regimes.

    The monograph we published Diameter-Transformed Fluidized Bed: fundamentals and practice with Sinopec in 2019 in both Chinese (China Petrochemical Press) and English (Springer nature) received the first prize of excellent publication of China Petroleum and Chemical Industry Association in 2020. The achievement above including a generic approach for scale-up of gas-solid fluidized beds won the Natural Science Award (first class, 2020) from the Chinese Society of Particuology.

    Fig.1 Mesoscience research powers the process development for clean gasoline production

    Breakthough IIUpgrading the virtual factory R&D platform. A coarse-grain discrete simulation method, termed as EMMS-DPM, was developed. The accuracy is greatly improved by coupling with IBM, optimizing the boundary treatment and extending to compressible flows (Chem. Eng. J. 2020, 389: 124343; Phys. Fluids, 2020, 32: 103306). An in-situ, online visualization technology for the particle-fluid results was developed, so that the physical quantities and their evolution can be interactively observed for arbitrary part of the reactor.

    Based on the aforementioned method and technology, a virtual factory was established for the key process of a 100/130 Mt/a DMTO factory, which is useful for understanding the abnormal situation of the reactor, making monitoring and warning schemes, adjusting and optimizing the operating conditions and so on. Based on the long-time and accurate simulation capability provided by the platform, a set of optimization schemes for the DMTO reactor was proposed (Chem. Eng. J., 2020, 389: 124135).

    Fig.2 DMTO reactor fault detection and implementation of virtual factory

    Breakthough III: The energy consumption in gas-solid fluidization was investigated by using the particle-resolved direct numerical simulation (PR-DNS) method (CEJ, 2020, 401: 125999). When introducing acceleration into the statistics of the suspending and transporting velocity of Ust, it is observed that Ust tends to be minimized with time for different initial distributions. According to the correspondence between Ust and the energy consumption of Nst for suspending and transporting particles, the conclusion that Nst tends to be minimized with time is obtained for various initial distributions, which extends previous verifying work (CES, 2005, 60: 3091) based on the pseudo-particle modeling (PPM). This progress provides a more rigorous numerical algorithm for the in-depth study of the EMMS model and is also inspiring for the construction of new drag models.

    Fig.3 Verification of the stability condition of the EMMS model by PR-DNS


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