Technological Process Control Using a PID Dependency Matrix
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing control methods for technological processes, such as MPC and PID, face challenges in accurately determining system models, leading to oscillations and increased complexity, especially in complex systems with multiple variables, which affects production efficiency and stability.
Innovation Solution
The Advanced Control System (ACS) dynamically optimizes technological processes by using a layered architecture and a dependency matrix of proportional-integral-derivative controls, allowing real-time adjustment of setpoints to maintain controlled variables within defined limits, reducing oscillations and increasing operational stability without relying on system response models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model predictive control (MPC) is used to control the technological process, then the control performance can be close to optimal, but the system complexity increases and requires accurate system models that are difficult and time-consuming to determine
Solution Approach 1:
The patent replaces complex, expensive system models with simple first-order plus dead-time (FOPDT) models that are easy to obtain. These simplified models are sufficient for control purposes and can be quickly determined without extensive testing, effectively using 'cheap' simplified representations instead of 'expensive' accurate models.
Solution Approach 2:
The patent changes the parameters of the control model from complex multi-parameter system models to simplified FOPDT models with fewer parameters (gain, time constant, dead time). This parameter simplification reduces model determination complexity while maintaining adequate control performance.
2Measurement precision
If system models are determined through step-tests to ensure accurate control, then the control accuracy improves, but production quality and efficiency are temporarily lost and the process requires advance agreement with production responsible persons
Solution Approach 1:
The patent changes from requiring accurate detailed system models to using simplified FOPDT models that can be determined quickly. This parameter simplification allows model determination without extensive step-tests, avoiding production interruptions while achieving sufficient model accuracy for effective control.
Solution Approach 2:
The patent enables preliminary determination of simplified process models that can be used immediately for control without requiring extensive on-site testing. This preliminary modeling approach avoids the need for production interruptions during model determination.
3Manufacturing precision
If complex control models are used in MPC, then the optimal operating point can be accurately determined, but the software complexity increases adversely affecting reliability and stability
Solution Approach 1:
The patent replaces complex control models with simplified FOPDT models that are computationally lightweight and easy to implement. These simplified models maintain adequate accuracy for determining optimal operating points while significantly improving software reliability and stability by reducing computational complexity.
Solution Approach 2:
The patent reduces the number and complexity of model parameters from complex multi-parameter models to simple FOPDT parameters (gain, time constant, dead time). This parameter reduction improves software reliability while maintaining sufficient accuracy for optimal control.
4Reliability
If response models are used that perfectly reflect the real system behaviour, then the control performance will be close to optimal, but it is theoretically impossible to establish such models and they degenerate over time requiring repeated testing every 6-12 months
Solution Approach 1:
The patent changes from using complex response models that degenerate over time to simplified FOPDT models with robust parameters. These simplified models are less sensitive to system changes and maintain adequate accuracy over longer periods, reducing the frequency of model updates required.
Solution Approach 2:
The patent uses simplified models that are easier to update and regenerate when needed. While no model is perfectly permanent, the simplified FOPDT models can be quickly re-determined without extensive testing, effectively reducing the time loss associated with model maintenance.
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
The invention relates to a method for controlling a technological process characterised by dependent controlled variables CVi (i≥1), each of which depends on one or more independent manipulated variables MVj (j≥1), with the relationships between each pair of CVi and MVj variables being included in the ACS dependency matrix as separate proportional-integral-derivative controls PIDij, including the following steps: a) Retrieving the current r(t) values of dependent controlled variables CVi and independent manipulated variables MVj; b) For each independent manipulated variable MVj, selecting a specific dependent controlled variable CVm for control and limit SPm of said dependent controlled variable CVm; c) Calculating the adjustment deviation between the current value of the selected dependent controlled CVm and its limit SPm; d) Selecting from the ACS dependency matrix a proportional-integral-derivative controller PIDmj corresponding to the pair of variables selected in step b): dependent controlled CVm and independent manipulated MVj, and calculating the value of said proportional-integral-derivative controller PIDmj; e) Sending the value of the proportional-integral-derivative controller PIDmj calculated in step d) as the (control signal) setpoint u(t) to the technological process control system. The invention also comprises a product in the form of a computer programme written on a computer-readable medium and containing instructions for the implementation of this method.