Method and apparatus for monitoring operational characteristics of an industrial gas plant complex

Patent metadata

Field	Value
Publication number	US-12228920-B2
Application number	US-202117193040-A
Country	US
Kind code	B2
Filing date	Mar 5, 2021
Priority date	Mar 5, 2021
Publication date	Feb 18, 2025
Grant date	Feb 18, 2025

Abstract

There is provided a method of monitoring operational characteristics of an industrial gas plant complex comprising a plurality of industrial gas plants. The method being executed by at least one hardware processor and comprising: assigning a machine learning model to each of the industrial gas plants forming the industrial gas plant complex; training the respective machine learning model for each industrial gas plant based on received historical time-dependent operational characteristic data for the respective industrial gas plant; executing the trained machine learning model for each industrial gas plant to predict operational characteristics for each respective industrial gas plant for a pre-determined future time period; and comparing predicted operational characteristic data for each respective industrial gas plant for a pre-determined future time period with measured operational characteristic data for the corresponding time period to identify deviations in industrial gas plant performance.

First claim

What is claimed is:

1. A method of monitoring operational characteristics of an industrial gas plant complex comprising a plurality of industrial gas plants, the method being executed by at least one hardware processor, the method comprising:
assigning a machine learning model to each of the industrial gas plants forming the industrial gas plant complex, the industrial gas plants comprising a hydrogen production plant for production of hydrogen, an air separation unit (ASU) for production of nitrogen, and an ammonia production plant for production of ammonia;
training the respective machine learning model for each industrial gas plant based on received historical time-dependent operational characteristic data for the respective industrial gas plant;
executing the trained machine learning model for each industrial gas plant to predict operational characteristics for each respective industrial gas plant for a pre-determined future time period to maximize ammonia production and produce hydrogen at a sufficient quantity to support the ammonia production given constraints on total amount of predicted power available for the pre-determined future time period and an amount of hydrogen in storage;
generating set points for the industrial gas plants based on the operational characteristics predicted via the executing of the trained machine learning model for use in the pre-determined future time period so the industrial gas plants operate to maximize ammonia production when operating during the pre-determined future time period so that as much ammonia is produced as possible for the pre-determined future time period based on the total amount of predicted power available for the pre-determined future time period;
the industrial gas plants utilizing the generated set points to produce ammonia during the pre-determined future time period so that as much ammonia is produced as possible for the pre-determined future time period;
comparing predicted operational characteristic data for each respective industrial gas plant for the pre-determined future time period with measured operational characteristic data for the corresponding time period to identify deviations in industrial gas plant performance for production and/or storage of nitrogen, ammonia, and hydrogen to identify future problems in the industrial gas plant complex; and
scheduling at least one remedial action based on the identified future problems to avoid unplanned shut-downs of the industrial gas plant complex, the at least one remedial action comprising scheduling of maintenance based on capacity of storage units to maintain continuity of service provided by the industrial gas plant complex while the maintenance is to be performed.

2. The method of claim 1 , wherein the step of comparing is carried out at the end of the pre-determined future time period of the predicted operational characteristic data or at a timestamp therein.

3. The method of claim 1 , wherein the step of comparing comprises comparing predicted operational characteristic data predicted for a pre-determined time window with actual measured operational characteristic data for the same time window.

4. The method of claim 1 , wherein the received historical time-dependent operational characteristic data for the respective industrial gas plant comprises data obtained from a direct measurement of a process or parameter of the respective industrial gas plant.

5. The method of claim 1 , wherein the received historical time-dependent operational characteristic data for the respective industrial gas plant comprises data obtained from a physics-based model representative of operational characteristics of the respective industrial gas plant.

6. The method of claim 5 , wherein measured data relating to a process or parameter of the respective industrial gas plant is input into the respective physics-based model.

7. The method of claim 1 , wherein the predicted operational characteristics for each industrial gas plant are utilized to determine predicted future resources, future failure and/or predicted future maintenance.

8. The method of claim 1 , wherein the hydrogen production plant includes a plurality of electrolyzer modules.

9. The method of claim 8 , wherein each of the electrolyzer modules is assigned a machine learning model.

10. The method of claim 1 , wherein the predicted operational characteristics for each respective industrial gas plant are utilized in a further model to generate an operational performance metric of the industrial gas plant complex.

11. The method of claim 10 , wherein the operational performance metric comprises an efficiency value for the industrial gas plant complex.

12. The method of claim 11 , wherein the determined efficiency value enables a predicted determination of ammonia produced via the ammonia production plant for a given level of energy input.

13. A system for monitoring operational characteristics of an industrial gas plant complex comprising a plurality of industrial gas plants, the system comprising at least one hardware processor operable to perform:
assigning a machine learning model to each of the industrial gas plants forming the industrial gas plant complex, the industrial gas plants comprising a hydrogen production plant for production of hydrogen, an air separation unit (ASU) for production of nitrogen, and an ammonia production plant for production of ammonia;
training the respective machine learning model for each industrial gas plant based on received historical time-dependent operational characteristic data for the respective industrial gas plant;
executing the trained machine learning model for each industrial gas plant to predict operational characteristics for each respective industrial gas plant for a pre-determined future time period to maximize ammonia production and produce hydrogen at a sufficient quantity to support the ammonia production and produce hydrogen at a sufficient quantity to support the ammonia production given constraints on total amount of predicted power available for the pre-determined future time period and an amount of hydrogen in storage;
generating set points for the industrial gas plants based on the operational characteristics predicted via the executing of the trained machine learning model for use in the pre-determined future time period so the industrial gas plants operate to maximize ammonia production when operating during the pre-determined future time period so that as much ammonia is produced as possible for the pre-determined future time period based on the total amount of predicted power available for the pre-determined future time period;
providing the generated set points to a control system of the industrial gas plants to control the industrial gas plants during the pre-determined future time period so that as much ammonia is produced as possible for the pre-determined future time period;
comparing predicted operational characteristic data for each respective industrial gas plant for a pre-determined future time period with measured operational characteristic data for the corresponding time period to identify deviations in industrial gas plant performance for production and/or storage of nitrogen, ammonia, and hydrogen to identify future problems in the industrial gas plant complex; and
scheduling at least one remedial action based on the identified future problems to avoid unplanned shut-downs of the industrial gas plant complex, the at least one remedial action comprising scheduling of maintenance based on capacity of storage units to maintain continuity of service provided by the industrial gas plant complex while the maintenance is to be performed.

Assignees

• Air Prod & Chem

Inventors

• Mehta Sanjay
• Misra Pratik

Classifications

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06F18/2148

  characterised by the process organisation or structure, e.g. boosting cascade · CPC title

• G05B23/0283

  Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] (preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06) · CPC title

• G05B23/0235

  based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold · CPC title