Speedy standing wave design, optimization, and scaling rules of simulated moving bed systems with linear isotherms.

Simulated Moving Bed (SMB) systems with linear adsorption isotherms have been used for many different separations, including large-scale sugar separations. While SMBs are much more efficient than batch operations, they are not widely used for large-scale production because there are two key barriers. The methods for design, optimization, and scale-up are complex for non-ideal systems. The Speedy Standing Wave Design (SSWD) is developed here to reduce these barriers. The productivity (PR) and the solvent efficiency (F/D) are explicitly related to seven material properties and 13 design parameters. For diffusion-controlled systems, the maximum PR or F/D is controlled by two key dimensionless material properties, the selectivity (α) and the effective diffusivity ratio (η), and two key dimensionless design parameters, the ratios of step time/diffusion time and pressure-limited convection time/diffusion time. The optimum column configuration for maximum PR or F/D is controlled by the weighted diffusivity ratio (η/α2). In general, high α and low η/α2 favor high PR and F/D. The productivity is proportional to the ratio of the feed concentration to the diffusion time. Small particles and high diffusivities favor high productivity, but do not affect solvent efficiency. Simple scaling rules are derived from the two key dimensionless design parameters. The separation of acetic acid from glucose in biomass hydrolysate is used as an example to show how the productivity and the solvent efficiency are affected by the key dimensionless material and design parameters. Ten design parameters are optimized for maximum PR or minimum cost in one minute on a laptop computer. If the material properties are the same for different particle sizes and the dimensionless groups are kept constant, then lab-scale testing consumes less materials and can be done four times faster using particles with half the particle size.

Size-exclusion simulated moving bed for separating organophosphorus flame retardants from a polymer.

Over 500,000t of flame retardants in electronic wastes are consigned to landfills each year. A room-temperature, size-exclusion simulated moving bed (SEC-SMB) was developed to recover high purity (>99%) flame retardants with high yield (>99%). The SSWD method for ternary mixtures was developed for SEC-SMB. Fourteen decision variables were optimized to obtain the lowest separation cost within 1min. The estimated cost is less than 10% of the purchase cost of the flame retardants. The estimated cost of the optimized SEC-SMB is less than 3% of that of a conventional batch SEC processes. Fast start-up methods were developed to reduce the SMB start-up time by more than 18-fold. SEC-SMB can be an economical method for separating small molecules from polymers.

Speedy standing wave design of size-exclusion simulated moving bed: Solvent consumption and sorbent productivity related to material properties and design parameters.

Size-exclusion simulated moving beds (SEC-SMB) have been used for large-scale separations of linear alkanes from branched alkanes. While SEC-SMBs are orders of magnitude more efficient than batch chromatography, they are not widely used. One key barrier is the complexity in design and optimization. A four-zone SEC-SMB for a binary separation has seven material properties and 14 design parameters (two yields, five operating parameters, and seven equipment parameters). Previous optimization studies using numerical methods do not guarantee global optima or explicitly express solvent consumption (D/F) or sorbent productivity (PR) as functions of the material properties and design parameters. The standing wave concept is used to develop analytical expressions for D/F and PR as functions of 14 dimensionless groups, which consist of 21 material and design parameters. The resulting speedy standing wave design (SSWD) solutions are simplified for two limiting cases: diffusion or dispersion controlled. An example of SEC-SMB for insulin purification is used to illustrate how D/F and PR change with the dimensionless groups. The results show that maximum PR for both diffusion and dispersion controlled systems is mainly determined by yields, equipment parameters, material properties, and two key dimensionless groups: (1) the ratio of step time to diffusion time and (2) the ratio of diffusion time to pressure-limited convection time. A sharp trade off of D/F and PR occurs when the yield is greater than 99%. The column configuration for maximum PR is analytically related to the diffusivity ratio and the selectivity. To achieve maximum sorbent productivity, one should match step time, diffusion time, and pressure-limited convection time for diffusion controlled systems. For dispersion controlled systems, the axial dispersion time should be about 10 times the step time and about 50 times the pressure-limited convection time. Its value can be estimated from given yields, material properties, and column configuration. Among the material properties, selectivity and particle size have the largest impact on D/F and PR. Particle size and 14 design parameters can be optimized for minimum D/F, maximum PR, or minimum cost on a laptop computer.

Method for efficient recovery of high-purity polycarbonates from electronic waste.

More than one million tons of polycarbonates from waste electrical and electronic equipment are consigned to landfills at an increasing rate of 3-5% per year. Recycling the polymer waste should have a major environmental impact. Pure solvents cannot be used to selectively extract polycarbonates from mixtures of polymers with similar properties. In this study, selective mixed solvents are found using guidelines from Hansen solubility parameters, gradient polymer elution chromatography, and solubility tests. A room-temperature sequential extraction process using two mixed solvents is developed to recover polycarbonates with high yield (>95%) and a similar purity and molecular weight distribution as virgin polycarbonates. The estimated cost of recovery is less than 30% of the cost of producing virgin polycarbonates from petroleum. This method would potentially reduce raw materials from petroleum, use 84% less energy, reduce emission by 1-6 tons of CO2 per ton of polycarbonates, and reduce polymer accumulation in landfills and associated environmental hazards.