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

Chemical Recycling of Mixed Plastics in Electronic Waste Using Solvent-Based Processing

Currently, less than 20% of electronic waste (E-waste) produced in the U.S. is recycled. To improve the recycling rate of E-waste, the study aimed to: (1) identify the major plastics found within electronic shredder residue (ESR), (2) design solvents and processing conditions capable of separating out 90% of the plastic in ESR, and (3) estimate the energy efficiency of the solvent-based process developed. Preliminary screening showed 25 wt.% of the ESR was composed of plastics, with two polymers dominating the sorted plastic fraction—polystyrene (PS, 40 wt.%) and acrylonitrile butadiene styrene (ABS, 25 wt.%). Subsequently, solvents and anti-solvents were screened using Hansen Solubility Parameter Theory for PS, ABS, and ESR dissolution. The pre-screening results showed dichloromethane (DCM) and tetrahydrofuran (THF) as the most effective solvents for PS and ABS, with methanol (MeOH) and ethylene glycol (EG) as the most effective anti-solvents. By optimizing the dissolution time and the solvents used, the highest polymer dissolution yield (99 wt.%) was achieved using DCM for 48 h. Both MeOH and EG precipitated 71 wt.% of the polymer fraction of ESR. EG removed more phosphorus containing flame retardants (94 wt.%) than MeOH (69 wt.%). Energy analysis indicated that the solvent-based processes could save 25–60% of the embodied energy for PS and ABS. Characterization showed that the solvent-based processing could preserve the high molecular weight fraction of the polymers while removing flame retardants at the same time. The results from this study prove the potential of solvent-based processing to produce secondary plastic materials from E-waste for cross-industry reuse.

Speedy standing wave design and simulated moving bed splitting strategies for the separation of ternary mixtures with linear isotherms

Simulated Moving Bed (SMB) has advantages over batch chromatography in terms of productivity and solvent efficiency. However, SMB applications in large scale production are still limited because of the many design parameters that must be specified and the multiple splitting strategies that can be implemented. To overcome these barriers, this study extends the Speedy Standing Wave Design (SSWD) method of Weeden and Wang for binary linear systems to ternary linear adsorption systems. The dimensionless operating parameters, sorbent productivity, and solvent efficiency can be quickly calculated without process simulations. SSWD also gives an overview of the productivity and solvent efficiency as a function of two key dimensionless groups. This overview can be used for optimization of separation costs and for comparison of splitting strategies. The SSWD method was verified using rate model simulations for the separation of three amino acids. The simulated yields agree with the SSWD target yields within 1% for all components. The example was also used to illustrate the key design rules for ternary separations. High productivity and solvent efficiency can be achieved with a large difference in the retention factors of the heavy key and light key, which are the components that define the split of the feed between extract and raffinate products. For ternary ideal systems, solvent efficiency is inversely proportional to the largest difference in retention factors. For this reason, minimizing the overall range of retention factors can significantly improve the solvent efficiency and product concentration without sacrificing productivity. If more than one SMB is needed, the easiest split should be done first for higher productivity, solvent efficiency, and product concentration. In the example case study, both the productivity and solvent efficiency were about an order of magnitude higher when the easiest split was done in the first ring. The SSWD method can be used to design a wide array of multi-component separations with high yield, productivity, and solvent efficiency.