Geometrical optimization of a filtering conical hydrocyclone for thickening highly viscous and pseudoplastic suspensions.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Geometrical optimization of a filtering conical hydrocyclone for thickening highly viscous and pseudoplastic suspensions.
Συγγραφείς:	Morimoto, Murilo Guimarães, Barrozo, Marcos Antonio de Souza, Vieira, Luiz Gustavo Martins
Πηγή:	Chemical Engineering Communications; 2026, Vol. 213 Issue 8, p1547-1560, 14p
Θεματικοί όροι:	Differential evolution, Pseudoplastic fluids, Suspensions (Chemistry), Separation (Technology), Machine separators, Non-Newtonian fluids, Mathematical optimization, Coagulation
Περίληψη:	Filtering conical hydrocyclones, featuring a porous cone section, represent an innovative approach to solid-liquid separation that offers enhanced performance over conventional designs. This study investigates the systematic optimization of key geometric parameters (inlet diameter, DI; overflow diameter, DO; total length, L; and cone angle, θ) for thickening operations involving highly viscous, pseudoplastic suspensions representative of drilling fluids. A Central Composite Design was employed to map the effects of these variables on water split ratio and overall efficiency. Although the filtrate stream represented only 0.29% of the inlet stream, the presence of the filtering wall fundamentally alters the entire velocity field within the hydrocyclone. Experimental data were used to develop predictive models that served as objective functions for a Differential Evolution optimization algorithm. The optimized hydrocyclone (DI = 7.8 mm, DO = 10.5 mm, L = 141 mm, θ = 11.2°) achieved a water split ratio of 14.43% and demonstrated a 16.41% improvement in overall efficiency compared to the best-performing configuration from the experimental design. The study reveals fundamental design trade-offs and provides practical guidelines for optimizing filtering hydrocyclones for challenging separation duties involving non-Newtonian fluids. [ABSTRACT FROM AUTHOR]
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Βάση Δεδομένων:	Complementary Index

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