Lubomír Klimeš

This Hot Off the Press paper summarizes our recent work "Soft computing methods in the solution of an inverse heat transfer problem with phase change: A comparative study" published in Engineering Applications of Artificial Intelligence [5]. In the paper, we study inverse heat transfer problems with phase change, where the boundary heat flux is estimated. Such problems are ill-posed and their solution is challenging. Although there were conventional developed for this problem in the past, they are not well-suited for cases including phase change, as these contain strong non-linearities that bring additional computational difficulties. For such problems, soft computing methods provide a promising approach. Four methods from distinct categories of techniques are applied to this problem and thoroughly compared - the conventional gradient-based method, a fuzzy logic-based method, a population-based meta-heuristic, and a surrogate-assisted method. A reformulation of the problem utilizing dimension reduction and decomposition schemes was developed, bringing extensive computational improvements. The metaheuristic and the surrogate-based methods showed superior performance. Their performance was also rather stable and insensitive to the location of the temperature sensor (the source of data for the inverse estimation). A Zenodo repository with the complete implementation of all considered problems and methods is available¹.

The paper presents the comparison of optimization-regulation algorithms applied to the secondary cooling zone in continuous steel casting where the semi-product withdraws most of its thermal energy. In steel production, requirements towards obtaining defect-free semi-products are increasing day-by-day and the products, which would satisfy requirements of the consumers a few decades ago, are now far below the minimum required quality. To fulfill the quality demands towards minimum occurrence of defects in secondary cooling as possible, some regulation in the casting process is needed. The main concept of this paper is to analyze and compare the most known metaheuristic optimization approaches applied to the continuous steel casting process. Heat transfer and solidification phenomena are solved by using a fast 2.5D slice numerical model. The objective function is set to minimize the surface temperature differences in secondary cooling zones between calculated and targeted surface temperatures by suitable water flow rates through cooling nozzles. Obtained optimization results are discussed and the most suitable algorithm for this type of optimization problem is identified. Temperature deviations and cooling water flow rates in the secondary cooling zone, together with convergence rate and operation times needed to reach the stop criterium for each optimization approach, are analyzed and compared to target casting conditions based on a required temperature distribution of the strand. The paper also contains a brief description of applied heuristic algorithms. Some of the algorithms exhibited faster convergence rate than others, but the optimal solution was reached in every optimization run by only one algorithm.