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During twin-roll steel strip casting, molten steel is poured onto the surface of two casting rolls where it solidifies to form a steel strip. The solidification process introduces a two-phase region of steel known as mushy steel which has a significant effect on the resulting quality of the manufactured steel strip. Therefore, an accurate model of the growth of mushy steel within the steel pool is imperative for ultimately improving strip quality. In this paper, we derive a reduced-order model of the twin-roll casting process that captures the dynamics of the mushy region of the steel pool and describes the effect that the casting roll speed and gap distance have on the solidification dynamics. We propose a switched-mode description that leverages a lumped parameter moving boundary approach, coupled with a thermal resistance network analogy, to model both the steel pool and roll dynamics. The integration of these models and simulation of the combined model are nontrivial and discussed in detail. The proposed reduced-order model accurately describes the dominant dynamics of the process while using approximately one-tenth of the number of states used in previously published models.

We consider the problem of dynamic coupling between the rapid thermal solidification and mechanical compression of steel in twin-roll steel strip casting. In traditional steel casting, molten steel is first solidified into thick slabs and then compressed via a series of rollers to create thin sheets of steel. In twin-roll casting, these two processes are combined, thereby making control of the overall system significantly more challenging. Therefore, a simple and accurate model that characterizes these coupled dynamics is needed for model-based control of the system. We model the solidification process with explicit consideration for the mushy (semi-solid) region of steel by using a lumped parameter moving boundary approach. The moving boundaries are also used to estimate the size and composition of the region of steel that must be compressed to maintain a uniform strip thickness. A novelty of the proposed model is the use of a stiffening spring to characterize the stiffness of the resultant strip as a function of the relative amount of mushy and solid steel inside the compression region. In turn this model is used to determine the force required to carry out the compression. Simulation results demonstrate key features of the overall model.

In this paper we propose an iterative learning control (ILC) algorithm for a class of periodic processes with a variable time-delay that is greater than one iteration in length. We estimate the delay by separating it into two components: an estimate based on the number of iterations contained within a single delay period and an estimate defined as the residual between the actual delay and the iteration-based estimate. This structure enables the derivation of a stability law for an ILC algorithm that is a function of the delay estimation error. The proposed ILC algorithm is then applied to twin roll strip casting and the results are validated using experimental process data. We also demonstrate the sensitivity of the ILC algorithm to estimation error through simulation results.

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