Application of Optimization Techniques of ANN in Experimental Design for Material Content Extraction
Journal Title: International Journal of Electrical, Electronics and Mechanical Fundamentals (www.ijeemf.com) - Year 2012, Vol 3, Issue 1
Abstract
Here we are using artificial neural net modeling and its optimization techniques for de-sulphurization of hot metal process. In this steel industry application, the sulphur content of hot metal, obtained at the end of calcium carbide powder injection into 400 ton torpedo ladles is predicted as a function of hot metal weight, treatment time, initial sulphur content, gas flow rate, and powder injection rate. Here the method of inverse control and optimal control problems are discussed and desired sulphur content in the hot metal are achieved by optimal algorithm.
Authors and Affiliations
Narender , Gourve , Sanjeev Kumar
Keywords
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