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.

This thesis discusses the chemical composition (basicity) control problem of HIsarna, an experimental iron furnace which operates with 30% less CO2 emissions than its traditional blast furnace counterparts. The control challenge is keeping the basicity of the plant in a narrow operating region. A mass balance model of the plant was constructed - as it is common practice in the literature for traditional blast furnaces - which we combined with a parameter search method to find the most optimal model parameters from data. Next a stochastic system model of the plant was derived using the prediction errors of the plant on a new dataset. The novelty of our work is the chosen dynamic programming controller approach that we used for controller synthesis that enables optimal control of the plant with respect to the known model error distribution. A continuous Markov Decision Process based infinite horizon controller was devised by using Value Iteration to find a fixed point in our value function space with respect to the Bellman operator: our static value function. We used multilinear interpolation and a state and inputs grid to define our value function for our continuous state space. The controller map was derived from the static value function and we used multilinear interpolation again in order to obtain a continuous controller. We validate our controller by simulating the controller performance on our stochastic system model and evaluating it versus the recorded operator runs according to our running cost function defined in the Value Iteration. In summary the resulting controller outperforms the operator on average and in the worst case has comparable performance to the operator from 1000 simulation runs. Improving the controller performance further would be possible by using a more accurate system model or using a different grid parameterization than the one we used for computational efficiency reasons.

In decision making problems, the ability to compute the optimal solution can pose a serious challenge. Dynamic Programming (DP) aims to provide a framework to deal with a category of such problems, namely ones that involve sequential decision making. By dividing the original control problem into sub-problems and solving it backwards in time, from the end of the time horizon to the start, the method can compute a map of optimal solutions with respect to the initial condition. In order to divide the original problem into subproblems the DP method takes advantage of the principle of optimality, which states that a sub-solution of the optimal solution should be the optimal solution for the equivalent subproblem. In control systems, where the state and decision spaces are continuous, the original DP framework can be intractable due to the size of the discretization needed to simulate the continuous space. Therefore, efficient approximations are needed to solve such problems. One promising method is called Conjugate Dynamic Programming (CDP). The CDP algorithm is able to transform the original framework and solve problems in the conjugate domain providing a computational advantage over the standard method. In this work, we aim to improve and extend the setting under which the CDP algorithm operates, thus providing a more concrete advantage over standard method . In that regard, we will extract the optimal control actions from within the CDP algorithm, eliminating the need for solving an extra optimization problem for their computation. In addition, we will introduce a different interpolation technique that can outperform the current one, in certain scenarios, thus granting the user more choice when solving a decision making problem.