Fouling is formation of deposits on the heat exchanger surfaces that adversely affects operation of heat exchanger. Fouling can be approximated through a set of estimated heat exchanger parameters, which may not be accurate, leading to uncertainty in operation/maintenance decisions and hence the losses. A system and a method for identification and forecasting fouling of a plurality of heat exchangers in a refinery has been provided. The system comprises a digital replica of the heat exchanger network. The digital replica is configured to receive real-time sensor data from a plurality of data sources and provides real-time soft sensing of key parameters. The system is also configured to diagnose the reasons behind a specific condition of fouling. Further, an advisory is provided, that alerts and recommends corrective actions. The system provides estimate for the remaining useful life (RUL) of the heat exchangers and suggests the cleaning schedule.

An integrated process-structure-property modeling framework for design optimization and/or performance prediction of a material system includes a powder spreading model using a discrete element method (DEM) to generate a powder bed; a thermal-fluid flow model of the powder melting process to predict voids and temperature profile; a cellular automaton (CA) model to simulate grain growth based on the temperature profile; and a reduced-order micromechanics model to predict mechanical properties and fatigue resistance of resultant structures by resolving the voids and grains.

Using processes and methods described herein, a digital twin of a physical space can train itself using sensors and other information available from the building. In some embodiments, a system to be controlled comprises a controller that is connected to sensors. This controller also has a thermodynamic model of the system to be controlled within memory associated with the controller. The thermodynamic model has neurons that represent distinct pieces of a controlled space, such as a piece of equipment or a thermodynamically coherent section of a building, such as a window. The neurons represent these distinct pieces of the controlled space using parameter values and equations that model physical behavior of state with reference to the distinct piece of the controlled state. A machine learning process refines the thermodynamic model by modifying the parameter values of the neurons, using sensor data gathered from the system to be controlled as ground truth to be matched by behavior of the thermodynamic model. The thermodynamic model may be warmed up by running the model using state data as input.

A system and method for determining a balance point of a building that has undergone or is about to undergo modifications (such as shell improvements) are provided. A balance point of the building before the modifications can be determined using empirical data. Total thermal conductivity of the building before and after the modifications is determined and compared. Indoor temperature of the building is obtained. The balance point temperature after the modifications can be determined using a result of the comparison, the temperature inside the building, and the pre-modification balance point temperature. Knowing post-modification balance point temperature allows power grid operators to take into account fuel consumption by that building when planning for power production and distribution. Knowing the post-improvement balance point temperature also provides owners of the building information on which they can base the decision whether to implement the improvements.