The study, published in the Journal of Agriculture and Food Research, focused on suction-type pneumatic conveying, the vacuum-driven method most widely used in modern recirculating aquaculture systems to move feed from storage silos to fish tanks. While positive-pressure blowing systems have been studied extensively, suction conveying has received far less scientific attention, even though it dominates the aquaculture sector. The researchers, Hasan Ghafori and Sadegh Ataei, set out to close that gap with an unusually comprehensive experimental campaign covering twelve commercial pellet types spanning three species: rainbow trout, common carp, and sturgeon, each at four growth stages from pre-starter to fattening.

The physical test rig was a six-meter-long transparent Plexiglas pipeline with a 60-millimeter internal diameter, engineered for a conveying capacity of one ton per hour. A calibrated feeder valve dispensed pellets at mass flow rates ranging from 0.2 to 0.8 kilograms per second, while a variable-speed centrifugal blower pushed air velocities from 10 to 25 meters per second. Because the pipe was transparent, the team could watch the flow patterns directly, but they also backed up their eyes with instrumentation: digital manometers measured pressure drop at three points along the line, a high-precision differential pressure transmitter recorded instantaneous pressure fluctuations at the pipe midpoint, and a two-stage sieving procedure based on the ASABE S269.4 standard quantified mechanical damage by weighing the fines generated during each run.

One of the study’s most significant contributions is a quantitative method for classifying flow regimes, replacing the traditional reliance on visual observation with an objective statistical indicator. The researchers used the coefficient of variation of pressure fluctuations, calculated from 30-second recordings at 3 Hz, to draw hard numerical boundaries between four distinct flow regimes. When the coefficient stayed below 5 percent, the flow was classified as dilute phase, meaning pellets were fully suspended and moving uniformly. Values between 5 and 11 percent marked a transition zone, 11 to 16 percent signaled an unstable zone with a stationary layer at the pipe bottom and strand flow above, and values above 16 percent indicated dense phase, where plug-type flow and frequent particle collisions produce intense pressure fluctuations. These thresholds, validated against both visual observations and simulations, turn regime identification from an art into a reproducible measurement.

The experiments revealed a characteristic U-shaped relationship between air velocity and pressure drop for every pellet type. At low velocities in the dense phase, pressure drops were high, reaching 1100 to 1475 pascals per meter across species, but mechanical damage was minimal, between 1.2 and 4.2 percent, because pellets moved gently as slow-moving slugs. As velocity increased, pressure drop fell to a minimum at roughly 15 to 16 meters per second, the point of highest conveying efficiency, where flow was fully suspended and stable. Beyond that sweet spot, pressure drop climbed again and pellet damage rose sharply, reaching up to 8 percent for fragile pre-starter pellets at 25 meters per second, driven by increasingly violent impacts against walls and neighboring particles.

Species and growth stage mattered enormously. Pre-starter pellets, which are crushed and compacted into small diameters, suffered the worst damage, between 2.8 and 3.1 percent even at the gentle velocity of 10 meters per second, while robust fattening pellets lost only 0.7 to 0.8 percent under the same conditions. Sturgeon pellets, with the highest bulk densities at 590 to 650 kilograms per cubic meter, consistently demanded the most fan power, up to 677 watts at 25 meters per second, while common carp pellets, the lightest and most free-flowing, entered the dilute phase at lower velocities and showed the lowest pressure drops overall. These differences, rooted in measurable properties such as pellet diameter, sphericity, terminal velocity, and drag coefficient, were carefully characterized using digital calipers, a vertical wind tunnel, and universal testing machine measurements of Poisson’s ratio, shear modulus, and friction coefficients.

To capture the physics that experiments alone cannot resolve, the team built a coupled computational fluid dynamics and discrete element method simulation. The gas phase was solved with the standard k-epsilon turbulence model in ANSYS FLUENT, while each pellet’s translational and rotational motion was tracked with Newton’s second law, using the Hertz-Mindlin contact model for particle interactions and the Gidaspow drag model for gas-solid coupling. Crucially, the pellets were not treated as indestructible spheres: a Bonded Particle Model represented each pellet as an assembly of sub-particles connected by breakable bonds, so that fracture occurred whenever impact energy exceeded the pellet’s specific fracture energy, calibrated against single-particle impact tests that measured critical breakage velocities between 16.1 and 19.8 meters per second. A particle replacement model then swapped fractured pellets for fragments, allowing the simulation to compute breakage ratios under every operating condition.

The simulations validated well against experiments, with relative errors of 10.5 to 15.7 percent for pressure drop and 2.50 to 3.23 percent for particle velocity across the full operating range. But full CFD-DEM runs are computationally expensive, which limits their usefulness for the rapid, repeated evaluations that real optimization demands. To overcome this, the researchers trained a surrogate artificial neural network on 480 data points, combining 384 simulation-derived records with 96 experimental measurements. The network, a multi-layer perceptron with an 8-20-12-4 architecture, dropout regularization, and batch normalization, predicts four key outputs at once: pressure drop, average particle velocity, mechanical damage, and power consumption. It achieved a coefficient of determination above 0.96 across all outputs, with test-set mean absolute errors of roughly 12.8 pascals per meter for pressure drop and 0.32 percent for damage, while cutting computation time by several orders of magnitude.

Interpretability analyses confirmed that the network had learned genuine physics rather than statistical artifacts. SHAP analysis identified inlet air velocity as the dominant input, followed by mass flow rate and pellet type, together explaining over 70 percent of predictive variance. Sobol variance-based sensitivity analysis with 65,536 quasi-Monte Carlo samples echoed this ranking, assigning total-order indices of about 0.51 to air velocity for pressure drop and 0.47 for mechanical damage. Even more impressively, the model held up under out-of-distribution testing: when challenged with eight extrapolated scenarios, including air velocities up to 30 meters per second and flow rates outside the training envelope, relative errors stayed below 9.1 percent, defining a reliable operating boundary of 8 to 28 meters per second and 0.1 to 1.0 kilograms per second.

The practical upshot is a set of design guidelines that aquaculture engineers can act on immediately, with an important caveat. For most pellet types, the dilute-phase window around 15 to 16 meters per second offers the best multi-objective compromise: minimum pressure drop, fully suspended stable flow, and acceptable damage. When pellet integrity is the overriding priority, as with fragile pre-starter feeds, controlled dense-phase operation near 10 meters per second minimizes breakage despite higher pressure drop and blockage risk, while the unstable zone should always be avoided. The authors stress that all quantitative boundaries come from a six-meter laboratory pipeline, so industrial installations with longer runs, bends, and higher throughputs will need further validation before these numbers become set-points. Still, the framework itself, experiments feeding simulations feeding an interpretable AI surrogate, offers a template for making one of aquaculture’s most invisible losses visible, measurable, and ultimately controllable.

Subject of Research: Hybrid CFD-DEM and machine learning modeling of mechanical damage and flow regimes in pneumatic conveying of aquaculture feed pellets

Article Title: Hybrid CFD-DEM and ANN modeling of pneumatic conveying of aquaculture feed pellets: Flow regimes, mechanical damage, and performance optimization

Article References: Hybrid CFD-DEM and ANN modeling of pneumatic conveying of aquaculture feed pellets: Flow regimes, mechanical damage, and performance optimization. (n.d.). https://doi.org/10.1016/j.jafr.2026.103285

Image Credits: AI Generated

DOI: 10.1016/j.jafr.2026.103285

Keywords: pneumatic conveying, aquaculture, fish feed pellets, CFD-DEM, artificial neural network, flow regimes, mechanical damage, pressure drop, SHAP analysis, Sobol sensitivity, suction conveyor, feed conversion ratio