Optimize production processes – APROL Advanced Process Control
Permanent optimization of production processes is mandatory to remain relevant over the long term in an atmosphere of ever-increasing global competition.
Meeting the demand for optimized process control and system efficiency requires innovative strategies that go beyond conventional PID controllers.
Generally speaking, Advanced Process Control (APC) encompasses all control procedures that go beyond standard PID closed loop control and sequential control. This optimizes process control in such a way that the processes themselves always remain reproducible. This increases throughput, efficiency and product quality on the one hand while lowering the costs for energy and raw materials on the other.
Improved performance of closed loop control
APROL Advanced Process Control makes it possible to easily model even complex relationships between process parameters so that they can be used for stable, automatic and flexible system operation.
- Using APC ensures that compensation for disturbances takes place considerably faster so that the setpoint value is reached faster as well.
- The setpoint can be moved closer to the operating limits.
- Operation with less wear on actuators is possible due to reduced variance of the manipulated variable.
APC – Expensive? Complicated? Time-consuming?
Many users shy away from using APC in new projects since it is often considered expensive, complicated, time-consuming and non-transparent.
As a matter of fact, blocks for high-level control – part and parcel of the standard Process Automation Library (PAL) included in B&R's APROL process control system – can easily cover a majority of the challenging control tasks found in the process industry using onboard methods. As a result, solutions can be implemented quickly, efficiently, transparently and with no extra costs.
Tuning PID control loops
Countless analyses of control loops across various industries often lead to the same conclusion:
- One-third of control loops exhibit good or satisfactory performance.
- Two-thirds of control loops are deficient or poorly configured (possibly even temporarily or permanently under manual control).
In the field, PID parameters are usually determined through the use of heuristic configuration rules. If data from other systems is available, the user will frequently rely on these parameter sets. With computer-aided control loop optimization, the process is either performed using a manipulated variable step-change during manual operation or a setpoint step-change in automatic mode.
Control performance monitoring for control loops
It is a well-known fact that PID controllers are generally not configured optimally and must therefore be operated manually at least some of the time. This type of operation guarantees stable system operation.
Monitoring control quality helps detect creeping degradations of control loop performance so that appropriate maintenance measures can be taken or control parameters optimized.
Statistical data identifies tendencies
The control module provides statistics that can be used to assess the quality of control loops. These figures are crucial for achieving the maximum possible increase in process control efficiency.
Model predictive controller (MPC)
Model-based predictive control is based on an optimization method that cyclically minimizes predicted control errors. Predicting control errors is backed up by the use of a model. To do so, pulse and/or step responses are saved in the control function block. The calculation is based on the assumption that the model is linear and time-invariant. Through the use of the dynamic model, the future course of the controlled variables can be expressed as a function of future changes to the manipulated variables.
While determining the solution, it is possible for constraints that affect the process such as manipulated variable limits of actuators to be taken into consideration directly. SISO and MIMO control configurable as needed: It is possible to control single-input single-output (SISO) systems as well as multiple-input multiple-output (MIMO) systems with 10 controlled variable, 10 manipulated variable and 10 disturbance variables.