Real-time advisory system and method for steam distribution network operation

A non-transitory computer-readable medium storing instructions executable by a computer processor, the instructions comprising functionality for:

• obtaining plant steam data regarding a plurality of steam traps of an industrial plant implementing a steam distribution network, the plant steam data comprising a type of steam trap, temperature, pressure, steam volumetric flow rate, condensate accumulation, condensate removal rate, and trap condition at each of the plurality of steam traps, the trap condition comprising information regarding whether the condensate accumulation is below a predetermined threshold value for the type of steam trap;
• developing a database using the plant steam data;
• using the database, developing a mathematical correlation that describes the condensation removal rate as a function of parameters comprising the type of steam trap, the temperature, the pressure, and the steam volumetric flow rate;
• developing a digital twin of the steam distribution network using a machine-learning model and the mathematical correlation;
• and
• estimating a trap condition of a steam trap of the digital twin based on a set of plant steam data regarding the plurality of steam traps of the industrial plant.

A computer-implemented system comprising:

• a steam generator;
• a steam header coupled to the steam generator;
• a steam trap comprising an inlet and an outlet, the inlet being coupled to the steam header;
• and
• a steam trap manager coupled to the steam trap, the steam trap manager comprising a computer processor and a machine-learning model, the computer processor comprising a non-transitory computer readable medium storing instructions to:
• obtain a series of steam trap data comprising a type of the steam trap, temperature, pressure, steam volumetric flow rate, condensate accumulation, condensate removal rate, and trap condition of the steam trap, the trap condition comprising information regarding whether the condensate accumulation is below a predetermined threshold value for the type of the steam trap;
• develop a database from the series of steam trap data;
• using the database, develop a mathematical correlation between the condensation removal rate and one or more of the type of steam strap, the temperature, the pressure, and the steam volumetric flow rate;
• develop a digital twin of the steam trap using a machine-learning model and the mathematical correlation;
• and
• estimate a trap condition of the steam trap of the digital twin for a set of steam trap data.