Li Xiao long¹， Sun Jipeng²， Wu Feng¹

Abstract

Objective Duringthe wet granulation process of sulfur， there is a significant nonlinear correlation between the degree of particle formation， the quality of the particle size distribution， and the key operating condition parameters. Factors such as melt temperature， cooling medium properties， and flow conditions interact in a complex way， directly influencing granule morphology and size uniformity. To increase the proportion of particles within the desired size range in the final product， it is essential to systematically optimize these operating parameters. Such optimization not only improves product quality and consistency but also enhances process efficiency and stability， thereby providing a more reliable basis for industrial-scale production.

Methods The research employed computational fluid dynamics （CFD） to simulate and analyze the wet granulation process of sulfur， enabling a systematic and in-depth investigation of key physical phenomena such as droplet formation， breakup， cooling， and solidification under a wide range of operating conditions. By establishing a comprehensive multiphase flow and heat transfer model， the study accurately captured the complex interactions between molten sulfur and the cooling medium， including interfacial dynamics， momentum exchange， and phase change behavior. These simulations provided valuable insights into the underlying mechanisms governing particle size distribution， morphology evolution， and granulation efficiency， thereby laying a solid foundation for subsequent data-driven modeling. Building upon the high-quality simulation dataset， a backpropagation （BP） neural network model was introduced to quantitatively characterize the nonlinear relationships between critical process parameters—including liquid sulfur pressure， temperature， and cooling conditions—and key granulation performance indicators. The neural network was systematically trained， tested， and validated using representative data samples to ensure high prediction accuracy， robustness， and generalization capability across diverse operating scenarios. Once trained， the model rapidly predicted granulation outcomes over a broad parameter space and efficiently identified optimal parameter combinations， significantly reducing the need for time-consuming and computationally expensive numerical simulations. Furthermore， the integration of CFD with machine learning established a hybrid modeling framework that not only enhanced computational efficiency but also improved the reliability and applicability of process optimization. This approach effectively overcame the limitations of traditional trial-and-error methods and provided a powerful， scalable tool for guiding process design， optimization， and control. Ultimately， it offered strong support for industrial application and had broad potential for extension to other complex multiphase systems， contributing to the intelligent development and optimization of advanced manufacturing processes.

Results and Discussion The pressure of liquid sulfur played a dominant role in regulating its kinetic energy during the wet granulation process， directly affecting droplet breakup， impact intensity， and the resulting particle size distribution and morphology. Increasing pressure enhanced fragmentation and dispersion， promoting finer and more uniform particles.However， excessive pressure could lead to over-fragmentation and irregular shapes， while insufficient pressure resulted in poor breakup and larger granules. In contrast， the temperature of liquid sulfur and the cooling water temperature influenced particle formation through secondary mechanisms. The former altered viscosity and surface tension， thereby affecting droplet formation and deformation， while the latter governed heat transfer and solidification behavior， influencing crystallization， internal structure， and surface characteristics. Based on systematic analysis and multi-parameter optimization， the optimal operating conditions were determined to be a liquid sulfur pressure of 700 kPa， a liquid sulfur thermodynamic temperature of 403.15 K， and a cooling water thermodynamic temperature of 333.15 K. Simulation and validation results confirmed that this parameter combination achieved a favorable balance between particle size uniformity and structural stability， demonstrating strong reliability and reproducibility， and providing a solid theoretical basis for improving product quality and advancing sulfur wet granulation technology.

Conclusion Among the three key factors—liquid sulfur pressure， liquid sulfur temperature， and cooling water temperature—liquid sulfur pressure emerges as the core dominant parameter due to its significant regulatory effect on the kinetic energy of the molten sulfur. By directly influencing the velocity， impact intensity， and breakup behavior of sulfur droplets， pressure plays a decisive role in determining particle morphology， size distribution， and overall granulation quality. In contrast， liquid sulfur temperature and cooling water temperature primarily affect the process through secondary mechanisms， such as modifying thermophysical properties and controlling heat transfer and solidification rates. Building on this understanding， the BP neural network-based process optimization method developed in this study demonstrates strong application potential. By establishing a data-driven mapping between operating parameters and granulation performance， this approach effectively reduces the uncertainty and inefficiency associated with the traditional trial-and-error method， while also overcoming the computational burden of direct optimization based on high-cost numerical simulations. Furthermore， the proposed methodology provides a scalable and efficient framework for parameter optimization， offering valuable guidance for process control and design. It is expected to be extended to a wide range of material processing and advanced manufacturing fields， thereby promoting the intelligent upgrading and digital transformation of related industrial processes.

Keywords： computational fluid dynamics； sulfur wet granulation； artificial neural network； equipment optimization

Get Citation:Li Xiaolong， Sun Jipeng， Wu Feng. Intelligent optimization of sulfur wet granulation equipment based on CFD simulation［J］. China Powder Science and Technology， 2026， 32（6）： 1-13.

Received：2026-03-16， Revised： 2026-06-22，Online： 2026-08-03。

Funding： This researchwas supported by the National Natural Science Foundation of China (Grant No.22478317).

CLC No.：X743；TQ125.1+1；TB4

Type Code：A

Serial No.：1008-5548（2026）06-0001-13