Abstract: A method for using a trained machine learning (ML) model to determine an ETA of a railcar to a destination and to generate a fleet readiness alert, including: collecting railcar trip information; collecting coordinates of a rail station associated with a rail carrier; creating a joined dataset by combining the trip information and coordinates; performing feature engineering (FE) on the joined dataset to create a feature engineered output including a plurality of engineered features; combining the joined dataset with the feature engineered output to create ML model feature inputs for the trained ML model; and activating the ML model, which accepts the model feature inputs as an input, determines the ETA of the railcar, and generates the fleet readiness alert based on the determined ETA of the railcar, indicating at least a usability status of a fleet of railcars at the depot yard within a fixed time.

Abstract: A method for using a trained machine learning (ML) model to determine an ETA of a railcar to a destination and to generate a fleet readiness alert, including: collecting railcar trip information; collecting coordinates of a rail station associated with a rail carrier; creating a joined dataset by combining the trip information and coordinates; performing feature engineering (FE) on the joined dataset to create a feature engineered output including a plurality of engineered features; combining the joined dataset with the feature engineered output to create ML model feature inputs for the trained ML model; and activating the ML model, which accepts the model feature inputs as an input, determines the ETA of the railcar, and generates the fleet readiness alert based on the determined ETA of the railcar, indicating at least a usability status of a fleet of railcars at the depot yard within a fixed time.

Abstract: This disclosure describes systems, methods, and devices related to for estimating the end-of-life of a furnace catalyst bed. A method may include extracting first measurements of a furnace catalyst bed; determining time-period averages of the extracted first measurements of the furnace catalyst bed over multiple time periods; determining a catalyst activity indicative of an efficiency of a selective reduction catalyst in reducing NOx concentrations for the furnace catalyst bed at a first time; generating a first linear regression of the catalyst activity based on averages of the multiple time periods; and generating a health score of the furnace catalyst bed at a second time based on the first linear regression, the health score indicative of an estimated end-of-life of the furnace catalyst bed.

Abstract: Applying a machine learning model to output predicted melt property values of a polymer extrudate based on an input data set derived from operating parameters of a polymer extruder that produces the polymer extrudate. The machine learning model can also be trained.