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In the daily of nowadays, cancer has been an important topic during the experts and normals. Due to the high death rate and high cost of healing, it is usually classified as incurable disease. However, with the development of the medic technology, some kinds of the cancers are able to healed. To analyze the result of the disease is a very important part of both clinic and study. In the area of computer science, data mining is a very useful way to analyze the big data. Combine the data from SEER stat, a database of cancer, and data mining algorithms, it is possible to push the result of the disease analyzation.

In this paper, an energy-efficient extraction–distillation process to separate diluted azeotropic acetonitrile–water mixtures is newly developed. Compared with the conventional azeotropic separation methods (i.e., extractive distillation), the potential dominant benefit of this proposed method is that the main separation task can be achieved by an extraction column without needing reboiler duty. In this work, an efficient solvent of n-propyl chloride is proposed to extract the organic compound into the extract phase and to let water remain in the raffinate phase. Ternary liquid–liquid equilibrium experiments are also conducted to verify the separation performance in the extraction column and decanter of the proposed process. It is found that significant savings of 40.3% in steam cost and 34.7% in total annual cost can be obtained by the proposed separation method as compared to that of a three-column extractive distillation system published in open literature.