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COMBINING FEATURE SELECTION AND SURROGATE MODELS FOR THE FORECAST OF MATERIAL CONCENTRATION IN FLUIDS
COMBINING FEATURE SELECTION AND SURROGATE MODELS FOR THE FORECAST OF MATERIAL CONCENTRATION IN FLUIDS
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机译：结合特征选择和替代模型预测流体中的物质浓度
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摘要
Embodiments for intelligent forecasting of material concentrations in a fluid by a processor in a computing environment. A material concentration of a material in a fluid may be predicted according to one or more continuous stirred tank reactor (CSTR) surrogate models on statistical flow trajectories of the fluid defined by a principle component analysis (PCA) operation of a system.
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