Centrifugal Separation Processes in Fluid Systems

Centrifugal separation harnesses the principle of sedimentation under an imposed rotational field. Rapid rotation generates forces hundreds to thousands of times greater than gravity, accelerating the migration of dispersed phases according to density differences. The method encompasses configurations from simple batch sedimentation centrifuges to large-scale continuous separators, including solid-bowl decanters and tubular centrifuges. In batch systems, samples rotate within fixed tubes; in continuous types, feed suspensions enter a rotating bowl and solids and liquids are extracted separately. Critical parameters include rotational speed, differential screw speed, residence time, flow geometry and feed rheology. Advances in computational fluid dynamics and hybrid mechanistic/data-driven models have enabled accurate prediction of separation performance, scale-up from laboratory to industrial scale and detailed analysis of phase interactions. Material function frameworks and real-time analytics, such as in situ spectroscopy and soft sensors, permit dynamic process control and quality monitoring. These developments support optimised energy consumption, enhanced throughput and tailored separation profiles across industries—from mineral dewatering and wastewater treatment to bioprocessing and nanoparticle fractionation. The versatility of centrifugal systems, combined with digital modelling and sensing technologies, drives innovation in resource recovery, chemical manufacturing and biomedical applications. As global demand for efficient and sustainable processes grows, centrifugal techniques remain at the forefront of high-throughput fluid separation solutions.

Research from Nature Portfolio

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Research from all publishers

Recent studies have advanced the modelling and control of decanter centrifuges through hybrid mechanistic and machine-learning approaches. One investigation developed a grey-box model that couples a numerical framework of sediment consolidation and transport with a neural network trained on experimental data, yielding improved predictive accuracy for solids capture and cake dryness under varying operational conditions. Computational fluid dynamics simulations have been used to resolve multi-phase flow and particle trajectories within decanter bowls, enabling virtual prototyping of geometry and operating parameters and reducing reliance on extensive pilot trials. In nanoparticle processing, tubular centrifuges have been augmented with UV/vis-based soft sensors for real-time monitoring of multi-component fractionation, translating spectroscopic signals into particle size and density distributions and facilitating adaptive process control. These developments illustrate the convergence of digital simulation, data analytics and advanced sensing to enhance the design, optimisation and automation of centrifugal separation across scales.

Centrifugal Separation Processes in Fluid Systems publication trend

The graph below shows the total number of articles in centrifugal separation processes in fluid systems across all publications each year (not limited to Nature Index journals).

Technical terms

Decanter centrifuge: A continuous solid–liquid separator featuring a rotating cylindrical-conical bowl and an internal screw conveyor to remove settled solids.

Tubular centrifuge: A rotor-based centrifuge in which feed is introduced into a long, narrow tube to achieve high centrifugal accelerations for fine particle fractionation.

Grey-box model: A hybrid modelling approach combining mechanistic physical equations with data-driven algorithms to capture complex process behaviour.

Soft sensor: A software-based estimator that infers unmeasured process variables from easily obtained signals using mathematical or statistical models.

Relative centrifugal force (RCF): The effective gravitational force experienced in a centrifuge, usually expressed as a multiple of Earth's gravity (g).

References

1. Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network. Minerals (2021).
2. A Resolved Simulation Approach to Investigate the Separation Behavior in Solid Bowl Centrifuges Using Material Functions. Separations (2022).
3. Soft Sensor Development for Real-Time Process Monitoring of Multidimensional Fractionation in Tubular Centrifuges. Nanomaterials (2021).
4. Resolved Simulation of the Clarification and Dewatering in Decanter Centrifuges. Processes (2023).