Rapid Process Model Identification Using Ramp Rate and Deadtime
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Solution Overview
Problem
Existing process control systems face challenges in rapidly identifying and generating process models, especially for slow processes, which requires significant testing and is time-consuming, making it difficult to implement model-based control techniques effectively.
Innovation Solution
A method for rapidly identifying dynamic relationships between process inputs and outputs by collecting data related to process variables and manipulated variables, determining a ramp rate, and generating process models using the estimated ramp rate and deadtime, allowing for quick model generation and control simulation before the process reaches steady state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional process model identification methods are used for slow processes, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The system performs preliminary actions by collecting process data continuously during normal operation and pre-processing this data to identify candidate time periods. When a manipulated variable change occurs, the system has already prepared the data structure and identified potential analysis windows, enabling rapid model identification without waiting for the complete process response.
Solution Approach 2:
The invention extracts only the essential information needed for model identification by focusing on specific time periods after manipulated variable changes. Instead of analyzing the entire process response, the system identifies and extracts data from predetermined time windows that contain the most valuable information for determining process parameters, significantly reducing analysis time.
2Adaptability or versatility
If multiple process models are introduced to match current process conditions, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system implements dynamics by making the process model adaptive to changing conditions. Instead of using fixed models, the system continuously identifies process parameters from current operating data and updates the model accordingly. This allows a single model structure to adapt to various process conditions through real-time parameter identification, avoiding the need for multiple pre-configured models.
3Productivity
If process models are generated before steady state is reached, then productivity is improved, but measurement precision may be compromised
Solution Approach 1:
The system performs preliminary data collection and analysis during the process transient period. By preparing the model identification in advance and using predetermined time periods after manipulated variable changes, the system can generate process models before the process reaches steady state, enabling faster control system implementation without compromising accuracy.
Solution Approach 2:
The invention allows skipping the traditional waiting period for steady state by using a simplified identification approach that works with transient data. The system rushes through the model generation process by focusing on key time periods and using efficient parameter estimation techniques that provide sufficient accuracy without requiring complete steady state conditions.
Data Source
AI summary
A rapid process model identification technique identifies, in a relatively short period of time, the dynamic relationship between a process input and a process output by developing an estimate of an integrating gain and a process deadtime from the initial response of the process output to a change in the process input. The integrating gain and deadtime values are then used to generate a complete process model for any of many different types of processes. These process models can be used very quickly to perform process simulation or can be used for control purposes, so as to be able to bring a process control system that uses or relies on process models on line much more quickly than was possible in the past. Moreover, this rapid modeling technique can be used to develop simulation models before the controller has completed responding to even a single process upset.