Multi-objective optimization for the economic production of d-psicose using simulated moving bed chromatography

Area of Science:

• Biocatalysis and Separation Science
• Carbohydrate Chemistry
• Process Engineering

Background:

• Biocatalytic production of rare sugars like d-psicose is gaining industrial interest for food and chemical applications.
• Efficient separation of d-psicose from d-fructose is crucial for its large-scale availability.
• Simulated Moving Bed (SMB) chromatography is a promising technique for continuous separation processes.

Purpose of the Study:

• To develop and optimize a simulated moving bed (SMB) process for the efficient production of rare sugar d-psicose.
• To investigate the impact of temperature on the productivity and desorbent requirement of the SMB separation process.
• To achieve high purity and productivity of d-psicose using multi-objective optimization.

Main Methods:

• Model parameter determination using the inverse method and experimental validation on a lab-scale SMB plant.
• Transport-dispersive true-moving bed modeling for simulation and prediction of separation performance.
• Multi-objective optimization of SMB process parameters (temperature, purity) focusing on productivity and desorbent requirement.

Main Results:

• Experimental SMB purities closely matched simulated data, validating the transport-dispersive true-moving bed model.
• Increased operating temperatures significantly enhanced d-psicose productivity (PR) from 3.4 to 5 kg/(L·day) between 20°C and 70°C.
• Pareto optimization demonstrated that higher productivity (up to 7.8 kg/(L·day)) can be achieved at 50°C with slightly relaxed purity requirements.

Conclusions:

• The SMB process is feasible and effective for the separation and production of d-psicose.
• Optimizing temperature is critical for maximizing productivity in rare sugar SMB separations.
• The study provides a foundation for scaling up d-psicose production through efficient chromatographic separation.