In this study, we developed a pipeline transport model to investigate the influence of particle sedimentation factors on slurry transportation through pipelines. The particle tracking module of the software was used to simulate the transport process, and the influences on the sedimentation rate were analyzed considering the slurry concentration, particle size, and flow velocity. The established model exhibited small calculation errors. In addition, the results revealed that the proposed model is reliable for calculating the degree of influence of various factors on particle sedimentation. The effect of the particle sedimentation rate on the pipeline slurry was explored considering the particle size, slurry concentration, and flow velocity. The sedimentation rate was positively related to particle size and adversely related to the slurry concentration and flow velocity. Indeed, study on the sedimentation rate requires considering a reasonable range of particle sizes, preparing a slurry with an appropriate concentration, and adjusting an appropriate flow velocity. Numerical simulations were performed using the filling data as the background for a sample mining area. The experimental results showed optimal slurry concentration and particle size of 60% and 25.25 µm, respectively.

As the demand for deep mineral resource extraction intensifies, optimizing pipeline transportation systems in backfill mining has become increasingly critical. Thus, reducing energy loss while ensuring the filling effect becomes crucial for improving process efficiency. Owing to variations among mines, accurately calculating pipeline resistance loss remains challenging, resulting in significant inaccuracies. The rapid development of Industry 4.0 provides intelligent and data-driven optimization ideas for this challenge. This study introduces a novel pipeline resistance loss prediction framework integrating generative artificial intelligence with a TransKAN model. This study employs generative artificial intelligence to produce physically constrained augmented data, integrates the KAN network’s B-spline basis functions for nonlinear feature extraction, and incorporates the Transformer architecture to capture spatio-temporal correlations in pipeline pressure sequences, enabling precise resistance loss calculation. The experimental data collected from pipeline pressure sensors provides empirical validation for the model. Compared with traditional mathematical formulas, BP neural networks, SVMs, and random forests, the proposed model demonstrates superior performance, achieving an R² value of 0.9644, an RMSE of 0.7126, and an MAE of 0.4703.

Sintering, as a critical process in iron and steelmaking, has a direct impact on the quality of sintered ore as well as the output and quality of blast furnace ironmaking. The burn-through point (BTP) is a key state parameter for assessing sinter quality, yet it remains difficult to accurately predict in actual production. Given the characteristics of the sintering process—such as multivariable dynamics, large time delays, nonlinearity, and strong coupling—this paper develops an intelligent BTP prediction model based on a hybrid attention FNet architecture.Its diffusion-enhanced preprocessing module can simultaneously collect various sensor time-series data (such as temperature, pressure, etc.) from the windbox, as well as process parameters like ignition temperature and bed thickness.By utilizing an iterative denoising mechanism, it mitigates noise in the windbox data and accomplishes predictive modeling and reconstruction of missing or anomalous sensor data. By integrating an FNet feature fusion network into a hybrid frequency-domain and spatial-domain attention module, the proposed model reduces computational complexity and achieves a 30% improvement in predictive performance over CNN-LSTM models. With an overall data fitting degree of 0.948, the model effectively addresses the scarcity of key abnormal operating condition samples in industrial scenarios and significantly enhances robustness and generalization capability under extreme operating conditions.

In green mine construction and bulk solid-waste utilization, pipeline transport energy efficiency and stope support stability of unclassified cemented tailings backfill exhibit a critical cross-scale mechanical trade-off. However, key process parameters are traditionally determined by empirical heuristics, lacking automated multi-objective quantitative evaluation. To address scale-bar interference and complex boundary degradation in industrial CFD velocity contour maps, this study proposes a physically constitutive-constrained HSV-K-GVF-Snake graded active contour model. Utilizing Bingham three-phase flow priors, the algorithm automatically extracts the cross-sectional flow-core area ratio as a macroscopic geometric proxy for fluid shear-dissipation trends, while three-dimensional micro-porosity is quantitatively resolved via industrial Computed Tomography (CT) reconstruction. Tested across 100 complex contour maps, the proposed segmentation algorithm achieves an Intersection over Union (IoU) of 85.61%, a Dice coefficient of 92.2%, a root mean square error (RMSE) of 0.118, and an average processing time of 1.2908 s per image, reducing GVF iteration steps by over 60% compared with unguided models. Multi-factor analysis across 120 simulated configurations indicated that slurry concentration was the dominant factor affecting the flow-core ratio and the CT-derived porosity indicator. Under the adopted CQI formulation and investigated parameter combinations, the condition with a pipe diameter of 180 mm, a slurry mass concentration of 76%, and a piping aspect ratio of 5 achieved the highest compromise score between the proxy-based transport-efficiency indicator and the porosity-based compactness indicator.