An integrated hydrometallurgical process was used for the zinc leaching and purification from a zinc ore containing 9.75 wt% zinc. The zinc minerals in the ore were hemimorphite, willemite, and calcophanite. Main gangue minerals were quartz, goethite, hematite, and calcite. Central composite design (CCD) method was used to design leaching experiments and the optimum conditions were found as follows: 30% of solid fraction, 22.05% sulphuric acid concentration, and the leaching temperature of 45 °C. The PLS containing 35.07 g/L zinc, 3.16 g/L iron, and 4.58 g/L manganese impurities was produced. A special purification process including Fe precipitation and Zn solvent extraction was implemented. The results showed that after precipitation of iron, Zn extraction of 88.5% was obtained with the 2 stages extraction system composed of 30 vol% D2EHPA as extractant. The overall Zn recovery from the ore was 71.44%. Therefore, an appropriate solution containing 16.6 g/L Zn, 0.05 g/L Fe, and 0.11 g/L Mn was prepared for the electro-winning unit without using the roasting and calcination steps (conventional method), which result in environmental pollution.

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Numerous evolutionary algorithms have been proposed which are inspired by the amazing lives of creatures, such as animals, insects, and birds. Each inspired algorithm has its own advantages and disadvantages, and has its own way to accomplish exploration and exploitation. In this paper, a new evolutionary algorithm with novel concepts, called Wildebeests Herd Optimization (WHO), is proposed. This algorithm is inspired by the splendid life of wildebeests in Africa. Moving and migration are inseparable from wildebeests’ lives. When a wildebeest wants to choose its path during migration, it considers the best path known to itself, the location of the more mature wildebeests in the crowd, and the direction of wildebeests with high mobility. The WHO algorithm imitates these traits, and can concurrently explore and exploit the search space. For validating WHO, it is applied to optimization problems and data mining tasks. It is demonstrated that WHO outperforms other evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization, in the assessed problems. Then, WHO is applied to the customer segmentation problem. Customer segmentation is one of the most important tasks of data mining, especially in the banking sector. In this paper, the customers of a bank with current accounts are segmented using WHO based on four aspects: profitability, cost, loyalty and credit; some of these aspects are calculated in a novel way. The results were welcome by the bank authorities.

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