Hybrid digital twins: Incorporating AI to advanced monitoring and optimization of energy recovery systems

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The project focuses on studying the dynamic behavior of HEXs networks undergoing fouling. HEXs are traditionally designed in isolation and steady operation but fouling is a dynamic process; it builds up over time, gradually reducing the performance of individual units and networks, and causing a complex set of interactions at the process system level. The way HEXs foul is very sensitive to local conditions (temperature, flow regime or composition), which in practice deviate often, and substantially, from a design target (e.g. start-ups, shutdown, cleaning, formulation changes, bypasses, units far from the network delivering cold/hot streams). This makes history effects, neglected in a traditional design approach, of a critical importance to understand how real energy recovery systems evolve during service. In this project we will investigate how key performance indicators in a HEX network (e.g. fouling resistance, thermal conductivity, rate, thickness) respond to perturbations during the operation of real systems (e.g. properties of heat transfer fluids, operational procedures, varying or compounded fouling mechanisms). We will use advanced statistics (i.e. AI/machine learning) to rigorously characterize the uncertainty of existing digital twins and develop and train new hybrid models to monitor efficiency, predict its evolution (i.e. new deposition/removal models, new model identification and parameter estimation tools) and potentially, respond in real-time with an optimal control strategy. Industry is slowly transitioning from obsolete (steady) models into advanced (dynamic) digital twins to maximize the return of large-scale energy recovery systems. This project will lead the way in creating accurate, more responsive digital twins and reducing tens of thousands of tons of CO2e via intelligent monitoring of HEX networks.