The traditional waste heat valve control technology is mainly divided into two methods

mechanism modeling and data-driven. However

in practical applications

the former is difficult to accurately describe due to the complex mechanism. The latter requires high data quality and full working condition samples

which is difficult to meet in a short time. Aiming at the above problems

a fusion-driven optimization method for waste heat valve control is proposed. Firstly

the mechanism knowledge and data knowledge are fused to construct a knowledge graph model based on fuzzy sets

and the valve opening knowledge is materialized. Secondly

the LSTM valve opening optimization model based on time protection mechanism is established

and the time protection mechanism algorithm is proposed to determine the optimal adjustment frequency of the valve. Finally

the recommended valve opening is obtained by knowledge reasoning. Through experimental analysis and verification

this method integrates qualitative knowledge such as waste heat recovery mechanism and quantitative knowledge such as equipment operation data. While improving the safety of equipment

the probability of generating high-temperature saturated steam enthalpy is increased by 94%

and the average daily increase is 8 640 kJ

which realizes the intelligent decision of waste heat recovery valve opening.