In the current chapter, the application and industrial issues of calcium treatment were investigated. Laboratory experiments were performed to study the effect of the reoxidation on the calcium treatment. A neural network model was established to predict and improve the calcium yield during calcium treatment process. The required feeding length of the calcium wire could be precisely calculated online. A thermodynamic model based on the Gibbs energy minimization method was developed to predict the composition of inclusions and the molten steel during the calcium treatment process of an Al-killed steel. The accuracy of the model was validated with experimental and reported data. Furthermore, a software was developed to achieve the online control of precise calcium treatment with the objective of precisely controlling the composition of inclusions in the molten steel.