LAUSR

Abstract Naphthalene based ionic liquids have been recently reported in applications of photoluminescence at room temperature. Further usages of Naphthalene cover phosphorescence aspects and liquid-phase exfoliation of graphite for graphene sheets production. Naphthalene is also known for its use in resonance studies and scintillation counters. All the aforementioned applications are sensitive to trace contaminants. One of the purification routes consists of zone refining, a large family of techniques based on the solidification/segregation theory. Separation of solute (impurity) and solvent is gradually increased through subsequent passes of a molten zone travelling along a bar. The impurities accumulate at its ends resulting into a high purity material at the intermediate region. Zone refining is a time costly batch technique since subsequent passes are needed and the movement of the molten zone is inherently slow to avoid impurity entrapment when the liquid solidifies. Since the length of the molten zone influences the impurity distribution, the challenge to achieve refining efficiency consists on establishing the best set of these lengths with a view to minimizing the molten zone passes. This work aims to fulfill this challenge using a semi-analytical/numerical model combined with two swarm artificial intelligence approaches. Experimental impurity profiles of Rhodamine after a number of molten zone passes in a Naphthalene bar are compared with theoretical predictions of the mathematical model for validation purposes. The two swarm artificial intelligence approaches applied are bio-inspired algorithms: Particle Swarm Optimization, which mimics the bird flock flying behavior - velocity and coordinate for each bird; and the Cuckoo Search, which is based on the brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds. Each algorithm is shown to successfully interact with the mathematical model permitting the purification of Naphthalene by zone refining to be optimized. Both algorithms have demonstrated that better purification effect can be achieved by using larger zone lengths for the initial zone passes. It is shown that fewer iterations are needed by Cuckoo Search to achieve convergence as compared to Particle Swarm Optimization.