Digitilising the energy sector: A comprehensive digital twin framework for biomass gasification power plant with CO2 capture

Abstract

The push to decarbonize the energy sector by incorporating renewable sources is increasing the complexity of power plant operations. One potential solution is to digitize power plants through digital twin (DT) technology, which can improve operational efficiencies and reduce maintenance costs. However, the application of DT in power plants remains in its early stages, with no existing implementations focused on gasification technology. This study aims to develop a comprehensive digital twin framework for a biomass gasification power plant with CO2 capture (DT-BGPP).

An overview of existing DT research in power plants and their classifications was conducted to assess the current state of the field and identify gaps. Based on this analysis, essential characteristics for the DT-BGPP framework were defined, leading to the identification of its main components. The classification revealed a common gap in mid-tier categories, with most available power plant Dts lacking complete bidirectional data flow with their physical counterparts. The key components of DT-BGPP include a high-order science-informed dynamic model, a data-driven model, actual data, pre-executed localized simulations, and a system genome.

Recommendations for advancing the proposed DT-BGPP include establishing connections between all framework components to achieve a fully integrated digital twin for a biomass gasification power plant with CO₂ capture.