The steel industry is one of the largest emitters of greenhouse gases. Therefore, there is a need to develop revolutionary sustainable methods for producing iron and steel. HIsarna is one such sustainable method developed by TATA Steel Europe for a long time. Due to the flexibility offered by this iron-making process which allows using unprocessed iron ore, it is a promising technology. Currently, work is going on to stabilize and optimize this iron-making method. One of the steps of that process is maintaining the optimal level of slag in terms of its chemical composition. It is important to regulate the slag basicity to maintain the quality of iron produced. This is where the concept of distributionally robust control comes into use, as the fluctuations in the slag basicity are random that we wish to control under imperfect knowledge of the distribution of these disturbances. The aim of this project is twofold. First, the existing controller (from previous work done on HIsarna) is implemented on the real system. This controller outperformed human operators in a simulation environment which motivates this step. Second, using techniques from distributionally robust control, improves the robustness and performance of the controller. While the existing controller was trained in a simulation environment, the uncertainty in the real system may be different from that of the simulator. Using actual measurements together with more sophisticated training may lead to a controller that can handle various material properties and operating ranges appearing more common in production.

HIsarna is a revolutionary step towards production of green steel. Due to the complex nature of the process the control of various facets of HIsarna is difficult. One of these facets is the slag-composition control, or more specifically slag-basicity. In this thesis an optimal sequential decision making strategy has been developed using the Bellman Optimality rule and Value-Iteration. The algorithm has been evaluated at length in order to refine the design choices and decison tree based classification has been used to make the output interpretable for a wider audience. Simulations show the effectiveness of the proposed strategy over the existing control techniques.