Processes

This study analyzes hydrodynamics and mass transfer in a packed-bed reactor (PBR) by comparing two representations of bed geometry. The first is a pseudo-homogeneous approach using effective parameters, such as a radial porosity distribution. The second is a heterogeneous approach with resolved particles in the CAD domain. Both models simulate single-phase flow and mass transfer of urea and NH₃ for an enzymatic reaction across a wide Reynolds number range $\left(5\le {\mathrm{Re}}_p\le 750\right)$. The pseudo-homogeneous model incorporated a detailed porosity distribution, derived from the heterogeneous model’s solids layout, which aligned well with literature, including classical correlations for radial porosity in packed beds. Additionally, hydrodynamic predictions were benchmarked against established pressure-drop correlations for confined packed beds, supporting the physical consistency of the particle-resolved framework. This non-uniform porosity informed local variations in permeability and dispersion coefficients. Velocity, pressure, and concentration fields from both approaches were compared to quantify predictive quality. Results indicate that a well-configured pseudo-homogeneous model can closely match heterogeneous model predictions, achieving similar accuracy in many flow regimes, with accumulated average relative errors below 8%. However, its performance varies with flow conditions. The optimal pseudo-homogeneous model (showing the highest predictive consistency with the particle-resolved simulations) was then used for transient simulations. These dynamic results support the preliminary sizing and conceptual design of a device for nutrient recovery from human urine for agricultural use, demonstrating the utility of simplified models for complex reactor design while acknowledging that full experimental validation under real urine-matrix conditions remains beyond the scope of the present study.

We propose Mamba - Physics-Informed Neural Network(Mamba-PINN), a novel data–physics integrated resilience assessment model, to evaluate the anti-typhoon disturbance ability of coastal sewage treatment systems in Zhuhai. The increasing frequency of extreme weather events poses significant challenges to urban infrastructure; yet, existing methods often fail to capture the complex spatio-temporal dynamics of typhoon impacts. Our approach combines a Mamba neural network for high-frequency monitoring data processing with Physics-Informed Neural Networks (PINN) to quantify process recovery dynamics under typhoon conditions. The Mamba network extracts critical storm impact features, while the PINN embeds fluid inertia and microbial activity inhibition mechanisms to model system responses. Furthermore, we introduce a disturbance–recovery resilience index to provide a quantitative measure of system robustness, enabling targeted adaptive transformations for coastal sewage plants. The proposed method addresses the limitations of purely data-driven or physics-based models by integrating both paradigms, offering a more comprehensive understanding of resilience mechanisms. Experimental results demonstrate the model’s effectiveness in capturing nonlinear interactions between typhoon disturbances and treatment process recovery. This work contributes to the sustainable development of coastal cities by providing a scientifically grounded framework for infrastructure adaptation under climate change.

High-voltage circuit breakers (HVCBs) are critical switching devices whose mechanical reliability directly affects power system safety and operational continuity. Accurate fault diagnosis remains challenging due to nonlinear vibration characteristics and the sensitivity of support vector machines (SVMs) to hyperparameter selection. To address this issue, a multi-strategy improved dung beetle optimization–support vector machine (MIDBO–SVM) framework is proposed for vibration-based mechanical fault diagnosis. Frequency-domain features are extracted from vibration signals using the fast Fourier transform to characterize fault-related spectral variations. A multi-strategy improved dung beetle optimization (MIDBO) algorithm incorporating chaotic initialization, adaptive search regulation, and mutation enhancement is developed to improve population diversity, global exploration, and convergence stability. The optimized MIDBO is used to determine the penalty and kernel parameters of the SVM, constructing a robust and well-generalized diagnostic model. Experimental results show that MIDBO–SVM achieves a diagnostic accuracy of 96.67%, outperforming conventional SVM (86.25%) and random forest (89.17%). The proposed method also demonstrates faster convergence and maintains accuracy above 86% under imbalanced sample conditions, confirming its robustness and generalization capability. These advantages contribute to more reliable mechanical condition assessment and improved maintenance decision support for HVCBs.