Method for Monitoring or Controlling a Chemical Production Process

Simple SummaryContent extracted from patent full text and abstract with AI.

This invention provides a computer-implemented method and system for accurately monitoring and controlling chemical production processes, especially complex ones. The approach involves dividing the chemical process into smaller sub-processes (partial models), training these sub-models with historical data, assembling them into an overall model, and then retraining this comprehensive model to better reflect the whole process. This retrained model can more reliably process real-time sensor data to generate operational instructions, even when the chemical process involves cycles, nested cycles, or feedback loops.

Use CasesContent extracted from patent full text and abstract with AI.

• Real-time automated control and monitoring of chemical plants to optimize production efficiency and safety.
• Deploying the system in facilities with complex multi-stage chemical reactions, such as petrochemical, pharmaceutical, or specialty chemical manufacturing.
• Retrofitting existing chemical process controls for improved accuracy using available historical and sensor data.
• Predictive maintenance and process troubleshooting based on accurate real-time modeling and monitoring.
• Scenario and process optimization simulations when integrating new equipment or altering process parameters.

BenefitsContent extracted from patent full text and abstract with AI.

• Enables more accurate monitoring and control of highly complex chemical production processes, including those with cycles and nested loops.
• Reduces reliance on extensive physical-chemical understanding by leveraging data-driven or hybrid models.
• Improves efficiency and product quality by using operational instructions that match real process behaviors.
• Adapts easily to different types of processes and plants, providing versatility and scalability.
• Decreases the amount of required historical data by allowing for partial and incremental model training.
• Facilitates integration with modern control hardware (DCS, PLC) and industrial IoT systems.
• Enables earlier and more effective detection of process deviations, helping prevent suboptimal operation or accidents.

Technical Classifications (CPCs)

Inventors & Applicants

Patent Abstract

The invention is in the field of monitoring or controlling a chemical production process. It relates to computer-implemented method for monitoring and/or controlling a chemical production process comprising (a) receiving sensor data related to the chemical production process, (b) determining an operational instruction related to the chemical production process by providing the sensor data to a trained model, wherein the trained model contains a first partial model representing a first part of the chemical production process and a second partial model representing a second part of the chemical production process, wherein the first partial model is trained with historical data related to the first part of the chemical production process and the second partial model is trained with historical data related to the second part of the chemical production process and wherein the model containing the first trained partial model and the second trained partial model is retrained with historical data related to the chemical production process, and (c) outputting the operational instruction.