Abstract: This disclosure relates generally to system and method for optimization of industrial processes, for example a tundish process. Typically geometries for industrial processes are simulated in a numerical analysis model such as a CFD. In order to simulate a physical phenomenon (such as tundish process) numerically, the domain thereof is discretized in order to convert the differential equations to be solved in the domain into linear equations. The accuracy of a CFD solution is dependent on a mesh of the domain, which in turn depends on a geometry thereof. For setting up an optimization task, the disclosed method provides first a CFD friendly base geometry, so that a faulty geometry can be detected before forming the complete geometry.

Abstract: In some examples, artificial intelligence based plantable blank spot detection may include generating a plurality of clusters of input images of areas that are to be analyzed for plantable blank spot detection. For each cluster of the plurality of clusters, a model may be identified to analyze corresponding images of a cluster. A model may be selected, from the models identified for the plurality of clusters, to analyze the input images. Canal lines may be identified in the analyzed images. Plantable blank spots may be determined in the analyzed images. An operation of a drone may be controlled to validate the determination of the plantable blank spots.

Abstract: This disclosure relates generally to system and method for optimization of industrial processes, for example a tundish process. Typically geometries for industrial processes are simulated in a numerical analysis model such as a CFD. In order to simulate a physical phenomenon (such as tundish process) numerically, the domain thereof is discretized in order to convert the differential equations to be solved in the domain into linear equations. The accuracy of a CFD solution is dependent on a mesh of the domain, which in turn depends on a geometry thereof. For setting up an optimization task, the disclosed method provides first a CFD friendly base geometry, so that a faulty geometry can be detected before forming the complete geometry.

Abstract: In some examples, artificial intelligence based plantable blank spot detection may include generating a plurality of clusters of input images of areas that are to be analyzed for plantable blank spot detection. For each cluster of the plurality of clusters, a model may be identified to analyze corresponding images of a cluster. A model may be selected, from the models identified for the plurality of clusters, to analyze the input images. Canal lines may be identified in the analyzed images. Plantable blank spots may be determined in the analyzed images. An operation of a drone may be controlled to validate the determination of the plantable blank spots.