The success of many process operations depends on the ability to monitor, to regulate and to control process conditions such as pressure, temperature, velocity, density, flow, weight, inventory among other measured or calculated conditions. These conditions are controlled through feedback of a signal, representing the measured or calculated condition, to a controller that manipulates the process based upon the difference between the signal and a desired value or control setpoint. A typical controller uses proportional integral and derivative (PID) control algorithms. The process is adjusted by manipulating process equipment to effect the flow of energy or material, such as, for example, by adjustment of a valve that impedes or otherwise restricts fluid flow.
Control of process conditions is important because process conditions affect product quality and production rate, both of which have a major effect on process plant operating profit. Controlling plant conditions at optimal values helps to maximize profit while deviation of conditions from optimal values reduces profit. Thus, it is economically important to reduce deviation.
Optimum conditions are frequently determined by processing and equipment limitations. In a distillation separation process, for example, quality specifications will limit the product impurity content. However, if the impurity is of little value, it is, nevertheless, profitable to sell as much impurity with the product as the quality specification permits. Thus, controlling the product composition as close to specification as possible without violating the specification limit, for example, without introducing too much impurity, is economically advantageous.
The amount of energy flowing through a distillation column determines the extent of separation, thus, the amount of product recovery. The amount of energy flowing through the column can be limited by reboiler or condenser heat transfer or by column flooding. Column flooding and heat transfer conditions can be calculated from measurements and used to establish operating limits. Maximum recovery is achieved by controlling column energy flow as close to the limits as possible. This control is implemented by maximizing column energy flow until restricted by a constraint controller with setpoints established at each of the limiting values.
In another example, the heating capacity of a furnace or boiler may be limited by some maximum temperature above which structural materials begin to lose integrity. Maximum production is frequently determined by such utility limits. Optimum operation is obtained by maximizing furnace load until temperatures increase to just within maximum limits.
Boilers, for example, can be controlled so that they can operate up to but not above their maximum capacity. Pressure is usually the control measurement that determines fuel flow to a boiler. The temperature of a metal boiler tube, a monitored variable, is measured, and this measurement is used as a feedback signal for an override controller that overrides the pressure controller, thus reducing fuel flow when tube temperature exceeds the override controller temperature setpoint. Other limiting measurements can be added to the control scheme to form a constraint control system.
The override temperature setpoint is set to a soft limit value below the boiler tube metal failure temperature which is the hard limit. Temperatures above the soft target are said to be in the limit violation direction from the soft target.
Fuel flow is adjusted to control either boiler pressure or maximum tube temperature and is the common process parameter. Either pressure or tube temperature can be controlled at an instant of time, but not both. A constraint controller provides a means of combining a pressure controller and a maximum tube metal temperature controller.
The control function of temperature overriding pressure control can be implemented through a constraint controller that adjusts the setpoint of a fuel flow controller. In a digital control system, the constraint controller selects the appropriate adjustment from an array of adjustments formed from the outputs of the pressure controller, temperature controller and any other controller included in the constraint controller. The array of adjustments represents the results of the control algorithms operating on the setpoint and monitored variable's measured value at an instant of time for each of the individual controllers constituting the constraint controller. These instantaneous values are stored in a digital control system for calculation and access.
Many process conditions are interrelated so that process adjustments, made to control one condition, affect other conditions. Constraint control systems regulate a selected group of process conditions (monitored variables) at optimal values and within processing and equipment limitations.
A constraint controller manipulates a single process adjustment in response to a multiplicity of process conditions or measurements. The constraint controller controls only one process condition at an instant of time. The constraint controller controls at some primary optimal condition or at the most limiting condition.
One primary optimal measurement in distillation, for example, is the concentration of product in a recycle stream. The optimal composition setpoint is calculated by balancing recovery energy costs against value of the recovered product.
The most limiting condition is the condition whose limit would first be violated in attempting to adjust the process to operate at the primary optimal condition. The mechanisms of associating process adjustments with constrained process conditions and the switching of control to respond to different varying conditions, is the subject of this invention.
A constraint controller is a combination of override controllers each interrelated with another and having the ability to override another and to operate in order to maintain the process conditions at an optimum value or at a constraint setpoint that is near, but within, a process limitation. The individual override controllers each develop an output signal value that will regulate the process so as to control the measurement at the setpoint. Only one of the individual override controllers can be successful at controlling its measurement at setpoint because a constraint controller has one output that adjusts one degree of process freedom. Accordingly, some rational must be used to select the individual controller output signal that is to be used to adjust the process.
The standard implementation of constraint controller has been to select as the output of the constraint controller either the highest or lowest of the individual controller output signals (adjustment array). Either a high or a low "select" is used depending on which action will move process conditions safely away from the limits. The nonselected controller output signals must track the constraint controller output so that they will promptly assume control when their measurement moves toward the limit violation side of setpoint. Herein lies a problem.
Output tracking, applied continuously to the nonselected individual controllers, causes each controller to be poised and ready to assume control. This would appear to be a good control objective, but the practical result is an unresponsive system. Measurement variations cause frequent selection switching among the controllers. Outputs in only one direction are passed through the selector resulting in an excessive distorted gain. The magnitude of the gain increases with increasing frequency of the measurement variation. As a consequence, the individual controllers must be "detuned" for constraint controller stability, which detuning produces poor control response when measurements are such that one controller remains selected, over some extended period.
Consider, for example, use of high selection of the output of two velocity mode digital controllers. If both outputs are increasing, the greater change in output is selected at each control execution. If the outputs vary, so that the selected output switches between the two controllers, then the sum of the selected change in outputs is greater than the change in output of either controller over the summation period. The effective gain of the constraint control is greater than the gain of either controller.
Consider a second example wherein both outputs are increasing on the average over some period and one of the outputs has an oscillation imposed on it so that it may increase or decrease between executions. In this example, then the constraint controller effective gain can be much greater than in the first example. The contribution to the summed output of the oscillating controller can be a factor greater than one times the net change in oscillating controller output. This occurs because all of the increases and none of the decreases are summed. The effective gain is thus a function of the control disturbance frequency.
Any controller tuned unintentionally by a process disturbances will cause problems. This explains why, in practice, it is found that override and constraint controllers are detuned relative to single loop controllers. If the control of a single constraint variable is tuned as a single loop controller, it probably will be unstable in constraint control. The insidious feature is that it may not go unstable until constraints are reached or an oscillating disturbance is encountered.
The preemptive constraint control of this invention, solves this problem providing responsive control independent of process disturbance frequency. The preemptive feature of the control takes control action prior to exceeding a limit setpoint, when it is projected that the limit variable would otherwise exceed the limit. Deviations above soft limit setpoints are thereby reduced, enabling soft limits established closer to "hard" constraints. The preemptive constraint control of this invention increases profit by operating closer to constraints.