Research Information System
POWDER TECHNOLOGY, vol.467, pp.1-17, 2026 (SCI-Expanded)
Slurry transport underpins mining, dredging, and wastewater operations. The prediction of the critical flow velocity, defined as the minimum line speed that prevents bed formation and keeps solids fully suspended, is essential to avert deposition and blockages, limit increases in pressure drop and avoidable energy use, and reduce erosive wear along the pipe. Classical empirical correlations regarding this velocity, while historically useful, have often fallen short when confronted with the nonlinear interactions and heterogeneous mixtures characteristic of these flows. This study addresses this limitation by proposing a novel equation that has been arrived at using a nonlinear regression method based on experimental data. The efficiency of the proposed equation is thoroughly compared to several empirical correlations available in the literature and various machine learning methods, such as Gene Expression Programming (GEP), Random Forest (RF), and Multiple Regression Analysis (MRA), along with wavelet combinations. According to our findings, the proposed nonlinear regression model is superior to classical methods in terms of the coefficient of determination (R2 = 0.91) and lower error metrics (AAPE = 12.87 %, RMSE = 0.277). Moreover, it appears competitive and sometimes better in prediction ability as compared to suggested machine learning methods. By carefully examining a real-world problem in engineering, this study shows practical insights for the design and operation of slurry transport systems.