Abstract: A system and method for managing network traffic in a distributed environment. the system including: a plurality of logic modules configured to determine policy data related to bandwidth management and at least one split criteria for a basis for shaping network traffic; a control processor associated with each one of the plurality of logic modules, each control processor configured to determine data associated with each of a plurality of traffic flows at the associated logic module and to coordinate traffic actions over the plurality of logic modules; a packet processor associated with each control processor and configured to determine a traffic action based on each traffic flow and received policy data; and at least two shaper objects configured to receive a split of the traffic flows and enforce the determined traffic action on their respective traffic flow.

Abstract: The present disclosure describes systems, methods, and computer readable media that provide a hybrid approach that uses machine learning techniques and phenomenological reactor models for optimization of steam cracker units. While the phenomenological model allows capturing the physics of a steam cracker using molecular kinetics, the machine learning methods fill the gap between the phenomenological models and more detailed radical kinetics based steam cracker models. Also, machine learning based models can capture actual plant information and provide insight into the variation between the models and plant running conditions. The proposed methodology shows better interpolation and extrapolation capabilities as compared to stand-alone machine learning methods. Also, compared to detailed radical kinetics based models, the approach utilized in embodiments requires much less computational time in order to carry out whole plant-wide optimization or can be used for planning/scheduling purposes.

Abstract: The present disclosure describes systems, methods, and computer readable media that provide a hybrid approach that uses machine learning techniques and phenomenological reactor models for optimization of steam cracker units. While the phenomenological model allows capturing the physics of a steam cracker using molecular kinetics, the machine learning methods fill the gap between the phenomenological models and more detailed radical kinetics based steam cracker models. Also, machine learning based models can capture actual plant information and provide insight into the variation between the models and plant running conditions. The proposed methodology shows better interpolation and extrapolation capabilities as compared to stand-alone machine learning methods. Also, compared to detailed radical kinetics based models, the approach utilized in embodiments requires much less computational time in order to carry out whole plant-wide optimization or can be used for planning/scheduling purposes.