Abstract: Methods, systems, and computer storage media for providing an optimal control configuration for a material processing system are provided. In operation, a material processing engine accesses causal graph input data. Causal graph input data includes input data of a continuous flow process. Based on the causal graph and the input data, a causal graph that aligns with do-calculus manipulations—associated with determining identifiable causal relationships corresponding to input materials of the continuous flow process—is generated. The causal graph is parsed based on the do-calculus manipulations to determine valid conditioning sets associated with estimating a causal impact on an optimization target. Based on the valid conditioning sets, an optimal control configuration comprising optimal control variable values is generated.

Abstract: Methods, systems, and computer storage media for providing an optimal control configuration for a material processing system are provided. In operation, a material processing engine accesses causal graph input data. Causal graph input data includes input data of a continuous flow process. Based on the causal graph and the input data, a causal graph that aligns with do-calculus manipulations—associated with determining identifiable causal relationships corresponding to input materials of the continuous flow process—is generated. The causal graph is parsed based on the do-calculus manipulations to determine valid conditioning sets associated with estimating a causal impact on an optimization target. Based on the valid conditioning sets, an optimal control configuration comprising optimal control variable values is generated.

Abstract: Methods, systems, and computer storage media for providing a blending flow configuration for a material processing system that blends a material from multiple sources. A blending flow configuration identifies an arrangement of components and settings of the components in the material processing system to support blending a material. The blending flow configuration can support optimizing outcomes of different types of downstream processes. The material properties data are identified based on different types of measurements. For example, block models, lab assays, and on-stream analyzers can be used to determine a composition of the material. Grinding line performance data (or grinding line operation data) that estimates the grinding line performance or capacity can also be accessed. A description of a conveyance network design of the material processing system is generated.

Abstract: Methods, systems, and computer storage media for providing an overflow management configuration for a material processing system that blends a material from multiple sources. The overflow management configuration identifies an arrangement of components and settings of the components in the material processing system to support blending a material while reducing a risk of overflow. The blending flow configuration can support optimizing outcomes of different types of downstream processes. The material properties data are identified based on different types of measurements. For example, block models, lab assays, and on-stream analyzers can be used to determine composition of the material. Grinding line performance data that estimates the grinding line performance or capacity can also be accessed. A description of a conveyance network design of the material processing system is generated.