王学重¹， 周梦¹， 毛晞玥¹， 刘艳²， 吴韬^1*， 周蕾¹

1.北京石油化工学院 生物医药与健康学院，恩泽生物质精细化工北京市重点实验室， 北京 102617；

2.晶格码（青岛）智能科技有限公司，山东 青岛 266000

引用格式：

王学重， 周梦， 毛晞玥， 等. 流化床造粒在线监测与过程模拟研究进展［J］. 中国粉体技术， 2027， 33（3）： 1-11.

Wang Xuezhong， Zhou Meng， Mao Xiyue， et al. Research progress on online monitoring and process simulation of fluidized bed granulation［J］. China Powder Science and Technology， 2027， 33（3）： 1-11.

DOI：10.13732/j.issn.1008-5548.2027.03.013

收稿日期： 2026-06-02， 修回日期： 2026-08-14， 上线日期： 2026-11-04。

基金项目： 国家自然科学基金项目，编号：52102208；北京市教育委员会科技计划一般项目，编号：KM202410017011；北京市科技新星计划，编号：Z211100002121024。

第一作者： 王学重（1963—），男，教授，博士，博士生导师，国家特聘计划专家，研究方向为制药工艺。E-mail：wangxuezhong@bipt.edu.cn。

通信作者： 吴韬（1990—），男，副教授，博士，硕士生导师，研究方向为制药工艺。E-mail：wutao@bipt.edu.cn。

摘要： 【目的】 为了高效制备高流动性、高溶解度及低粉尘的均质颗粒，并为流化床造粒过程构建智能闭环控制系统及工业放大提供理论支撑，开展流化床造粒关键质量属性的在线监测技术与过程模拟等研究，实现对造粒工艺过程及颗粒多尺度演变机制的系统认识。 【研究现状】 综述流化床造粒关键质量属性在线监测及过程模拟的研究进展，概括图像分析、近红外光谱等过程分析技术在颗粒含水率与粒径分布监测中的应用现状，总结群体粒数衡算模型、离散元法、计算流体力学以及耦合模型在解析造粒过程中颗粒多尺度演变机制方面的研究成果。 【结论与展望】 提出在线监测技术的未来发展趋势与多尺度模拟的优化方向；认为将过程分析技术与数值模拟模型深度融合，是实现流化床造粒过程智能闭环控制及工业放大的关键途径。

关键词： 流化床造粒； 在线检测技术； 图像分析； 过程模拟； 机器学习

Abstract

Significance Fluidized bed granulation （FBG） represents a cornerstone unit operation in solid dosage manufacturing， integrating powder mixing， liquid binder spraying， wet massing， and drying within a single multifunctional vessel. The primary objective of FBG is the efficient production of uniform and homogeneous granules characterized by enhanced flowability， high solubility， and minimal dust generation， which are critical downstream properties for processes such as tableting， compaction， and capsule filling. To facilitate the design of intelligent closed-loop control systems and provide robust theoretical foundations for industrial process scale-up， it is essential to conduct comprehensive research on the online monitoring of critical quality attributes （CQAs） and advanced process simulation. Such research aims to establish a systematic and multiscale understanding of both the macroscopic granulation process parameters and the microscopic particle evolution mechanisms. Deciphering these transient physical changes—ranging from microscale liquid bridge formation to macroscale particle aggregation—enables developers to maintain process stability and prevent batch failures. During scale-up， nonlinear thermodynamic variations and localized fluidization anomalies often occur， making empirical prediction models highly unreliable. Historically， FBG process optimization has relied heavily on empirical trial-and-error methodologies and offline characterization. However， traditional destructive manual sampling and subsequent offline testing introduce significant measurement delays and human errors， making real-time closed-loop adjustments impractical. Furthermore， to meet modern regulatory requirements， such as continuous quality verification， a shift toward science-based process understanding is necessary. To address these limitations， process analytical technology （PAT） and multiscale numerical models have emerged as complementary and non-invasive alternatives. Online PAT sensors provide non-destructive and continuous tracking of physical and chemical attributes during operation， while numerical simulations offer thermodynamic and mechanistic insights into the underlying gas-solid flow dynamics and particle growth mechanisms. The integration of PAT and process simulation represents a viable pathway for building intelligent FBG closed-loop control systems and facilitating efficient industrial scale-up.

Progress Optical and spectroscopic techniques currently represent the predominant approaches for FBG online monitoring. In the domain of PAT， image analysis methods have been successfully implemented for real-time particle size distribution （PSD） measurement， achieving high spatiotemporal resolutions that capture transient growth， aggregation， and breakage phenomena. Meanwhile， near-infrared spectroscopy （NIRS） has been extensively adopted for the simultaneous and non-destructive monitoring of granule moisture content and active pharmaceutical ingredient （API） homogeneity. Despite these advances， mainstream single-point inline sensors—including focused beam reflectance measurement （FBRM） and spatial filter velocimetry （SFV）—frequently face limitations related to spatial localization. When positioned in low-shear or stagnant regions of the fluidized bed， these probes fail to acquire spatially representative data， which can compromise the overall reliability of real-time monitoring results. Parallel to experimental monitoring， FBG numerical modeling has transitioned from macroscopic empirical correlations to multiscale phenomenological simulations. The population balance model （PBM）， computational fluid dynamics （CFD）， and discrete element method （DEM） serve as three core computational frameworks. While early PBMs model macroscopic PSD evolution via specialized growth， aggregation， and breakage kernels， they assume spatial homogeneity and perform poorly in highly dynamic fluidization regimes. To address this spatial limitation， coupled CFD-DEM simulations have been widely applied to analyze particle residence time distributions and resolve detailed spatial flow patterns. Subsequent research has led to integrated CFD-DEM-PBM frameworks that couple microscale liquid bridge physics with macroscale multiphase hydrodynamic behaviors. These models explicitly capture localized binder distribution and interparticle liquid bridge forces to accurately predict granule growth kinetics. Additionally， coarse-grained particle methodologies have been explored to reduce the high computational costs of full-scale CFD-DEM simulations， thereby improving the feasibility of simulating larger industrial-scale equipment. On the application front， PAT-based feedback control systems have demonstrated the ability to adaptively adjust binder spray rates based on inline moisture and PSD measurements， maintaining consistent granule attributes despite process disturbances.

Conclusions and Prospects Over the past decade， substantial progress has been achieved in FBG online monitoring and multiscale simulation， enabling more accurate CQA tracking and detailed modeling of complex gas-solid-liquid interactions. Nonetheless， several critical challenges continue to restrict large-scale industrial deployment. Most existing numerical frameworks rely on idealized spherical particle assumptions and simplified liquid bridge physics， which limit their predictive accuracy when applied to irregular and real-world granules. Furthermore， the high computational overhead associated with coupled multiscale CFD-DEM simulations currently prevents their direct integration into real-time model predictive control （MPC） systems. To overcome these limitations and advance the field， several promising research directions are proposed. First， the development of physics-informed machine learning （PIML） and optimized coarse-grained DEM techniques is necessary to reduce simulation times and accelerate computations without compromising physical fidelity. Second， a deeper and more seamless integration of online PAT tools and real-time numerical models is required to establish robust and adaptive digital twins of industrial FBG systems. By synchronizing virtual models with physical sensors， these digital twins can dynamically predict process drift， facilitate intelligent closed-loop control， and support reliable industrial scale-up. Finally， extending intelligent closed-loop control frameworks from traditional batch operations to continuous granulation lines represents a crucial step toward facilitating the modernization， automation， and digital transformation of pharmaceutical solid dosage manufacturing.

Keywords： fluidized bed granulation； online detection technology； image analysis； process simulation； machine learning

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