Simulated Moving Bed Optimization for Chromatographic Separations

Simulated moving bed (SMB) chromatography is a continuous separation technology that exploits differential adsorption affinities of solutes on a stationary phase to achieve high-purity and high-yield separations. In an SMB process, multiple columns are arranged in series and periodically switched to mimic a counter-current movement of solid and liquid phases. Key operational parameters include the sequence of switching times, flow-rate ratios among feed, desorbent and extract streams, and the boundaries of separation zones. Optimisation of these variables relies on accurate mathematical modelling of adsorption isotherms, mass-transfer kinetics and hydrodynamics. Advanced numerical methods—such as compact finite difference schemes—have been developed to solve the underlying advection–diffusion equations with moving boundaries. In parallel, control strategies ranging from fuzzy logic to model predictive control are employed to maintain target purities under disturbances. Modern research also addresses multi-objective trade-offs between productivity, solvent consumption and energy demand, as well as extending SMB to ternary systems by adopting concepts analogous to dividing wall column distillation. Recent advances in parameter estimation and uncertainty quantification further enhance the predictive power of SMB models, enabling robust design and scale-up for applications in fine chemicals, pharmaceuticals and bio-based feedstocks.

Research from Nature Portfolio

Recent studies have focused on intelligent control and high-fidelity simulation of SMB systems. One work introduces an advanced fuzzy controller that incorporates error acceleration to maintain extract and raffinate purities within 0.1 % of set-points, demonstrating robust performance under variations in adsorbent characteristics, feed concentration and switching times. Another report presents a hierarchical fuzzy-logic control architecture that achieves precise concentration control without detailed knowledge of system parameters, outperforming conventional PID and model predictive schemes in transient scenarios. A separate contribution develops a fourth-order compact finite difference algorithm for the advection–diffusion model of SMB, addressing moving-boundary boundary conditions via direct and pseudo-grid-point treatments. By coupling this numerical scheme with a continuous prediction method, computational time is reduced by nearly half while maintaining close agreement with experimental separation data for both sugar and chiral systems.

Research from all publishers

Parallel efforts have extended SMB design to multicomponent separations and parameter-identification methodologies. A novel process concept parallels dividing wall column distillation by using dual-layer SMB configurations for ternary separations, leading to improved separation efficiency in simulation studies. A gradient SMB design framework based on triangle theory offers a shortcut method for isocratic and gradient operation, optimising flow-rate ratios and modifier concentrations to enhance volume-specific productivity across a range of isotherms. In addition, new model-parameter estimation strategies employ global optimisation and Latin hypercube sampling to screen adsorption isotherm equations and determine the minimum experimental data needed for robust model identification, improving estimability and uncertainty quantification in SMB modelling.

Simulated Moving Bed Optimization for Chromatographic Separations publication trend

The graph below shows the total number of articles in simulated moving bed optimization for chromatographic separations across all publications each year (not limited to Nature Index journals).

Technical terms

Simulated moving bed (SMB): A continuous chromatographic process that periodically shifts inlet and outlet ports across multiple columns to emulate counter-current movement of phases.

Adsorption isotherm: A mathematical relationship describing the equilibrium concentration of solutes on the stationary phase as a function of liquid-phase concentration.

Fuzzy controller: A rule-based control system that handles nonlinearity and uncertainty by mapping input errors to corrective actions via membership functions.

Model predictive control (MPC): An optimisation-based control approach that uses a dynamic model to predict future outputs and compute control moves over a finite horizon.

Dividing wall column: A distillation configuration that enables multicomponent separation in a single shell by dividing the column into parallel internal sections.

Gradient SMB: A variant of SMB in which solvent composition or modifier concentration is varied along the zones to improve separation of components with different selectivities.

Parameter estimation: The process of determining model parameters—such as isotherm coefficients and mass transfer rates—by fitting to experimental or simulated data.

References

1. Purity control of simulated moving bed based on advanced fuzzy controller. Scientific Reports (2024).
2. Applicant hierarchical fuzzy controller for concentration control of simulated moving bed. Scientific Reports (2021).
3. Development of a fourth-order compact finite difference scheme for simulation of simulated-moving-bed process. Scientific Reports (2020).
4. Double-Layer Simulated Moving Bed Chromatography for Ternary Separations: Serialized Layer Configurations. Industrial & Engineering Chemistry Research (2021).
5. Shortcut design method for multicomponent gradient simulated moving beds. AIChE Journal (2023).
6. Strategies for Simulated Moving Bed Model Parameter Estimation Based on Minimal System Minimal Knowledge: Adsorption Isotherm Equation Screening and Estimability Analysis. Industrial & Engineering Chemistry Research (2023).