Hybrid modeling-based digital twin of the direct air cooling system for operational performance optimization

Direct air-cooling systems are susceptible to environmental changes and dust accumulation, which can result in abnormally high and fluctuating back-pressure values. Such circumstances may result in a reduction in the unit's economic efficiency. This study proposes implementing a digital twin (DT) system, driven by both mechanism and data, to determine the optimal back pressure of direct air-cooling units in various environmental contexts and dust accumulation states. Furthermore, a mathematical airflow model is proposed to quantify the impact of dust accumulation on airflow and heat transfer within an air-cooling island. Subsequently, based on the numerical simulation method, coefficients are proposed to correct the air temperature and quantity in the DT model, respectively, to ensure the accuracy of the back pressure calculation. The proposed DT was finally applied to a 660 MW direct air-cooling unit. In an industrial case study, the system was found to have saved 565.998 tonnes of standard coal and reduced 1411.034 tonnes of carbon emissions in a fortnight, which represents a significant impact.