No abstract

A new approach has been introduced for separation of oil-in-water emulsion by using ultrasound standing wavefield. A neural networks model was used to simulate changes in the size of droplet during treatment. Model outputs were then validated and generalization capability was evaluated. For each network, the optimum values of isotropic spread were obtained by minimizing the root mean square error and maximizing the corresponding coefficient. It was found that the predicted values were in good agreements with experimental results. Also, increasing voice speed was demonstrated to predict size of emulsion particles more efficiently and accurately.

A new method for breaking oil in water emulsion based on flocculation of droplets in high intensity ultrasonic standing wave field was developed in this study and the effect of initial droplets size, type of disperse phase as well as the time of sonication and the height of emulsion in the chamber on the extent of interdroplet interactions were investigated. The results showed that type of disperse phase affects the efficiency of separation process through controlling the initial size of droplets. For the two types of disperse phase in question the efficiency of separation was calculated to be 42.7% for canola oil/water emulsion and 37% for sunflower oil/ water emulsion. The time of sonication was found to have a positive contribution to the percent of flocculation and coalescence, so that the largest aggregates were formed after 30 minutes treatment. Also, the optimum height of emulsion in the treatment chamber was determined to be k/4 at which the strongest flocculation and highest percent of coalescence took place. Increasing the height of emulsion did not significantly influence the course of aggregation and separation.