08-02-2013, 03:51 PM
Advanced process control (APC)
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INTRODUCTION
Cascade, feedforward, ratio and inferential control systems are four types of advanced control systems that are able to reject disturbances more effectively than conventional PID control. Cascade control uses a secondary control loop within an overall control loop to reject specific disturbances within a process. Feedforward control requires incoming disturbances to be measured before entering a process, and control action is then undertaken to compensate for the implied effect of these disturbances, before a process is significantly affected. (This method is heavily reliant on process models). Ratio control handles feed flow rate disturbances. Inferential control uses fast-responding, secondary process measurements to estimate primary process variables that are difficult (or impossible) to measure on a time scale that will allow for appropriate controller response.
In control theory Advanced process control (APC) refers to a broad range of techniques and technologies implemented within industrial process control systems. Advanced process controls are usually deployed optionally and in addition to basic process controls. Basic process controls are designed and built with the process itself, to facilitate basic operation, control and automation requirements. Advanced process controls are typically added subsequently, often over the course of many years, to address particular performance or economic improvement opportunities in the process.
Process control (basic and advanced) normally implies the process industries, which includes chemicals, petrochemicals, oil and mineral refining, food processing, pharmaceuticals, power generation, etc. These industries are characterized by continuous processes and fluid processing, as opposed to discrete parts manufacturing, such as automobile and electronics manufacturing. The term process automation is essentially synonymous with process control.
Advanced control background
Since the advent of the distributed control system (DCS) in the 1970s, it has been widely used. DCS provided a tool for easy implementation of existing control strategies such as cascade, feed forward, nonlinear control, Smith predictors, constraint control and even decoupling control. These are control schemes based on the proportional-integral-derivative (PID) single-loop feedback controller, and they provided the platform of distributed and supervisory control, which is called (advanced) regulatory control.
What it is. Advanced control is a systematic studied approach to choosing relevant techniques and their integration into a co-operative management and control system that will significantly enhance plant operation and profitability. APC includes more sophisticated strategies, such as intelligent control, adaptive algorithms and MPC tied to empirical modeling. As an improvement over typical process control, APC represents an enhancement in the performance of control strategies that results in more consistent production, process optimization, better product qualities and less waste. While regulatory controls maintain mass and heat balances, advanced controls manipulate the mass and heat balances to achieve the best performance or quality.
With the development of information/computer technologies, having a powerful server connected to the plant collecting real-time data opened up numerous possibilities for complementary technologies. For example, complementary technology is an inferential estimation technology (soft sensor) that would infer the required composition of the stream to be used in the control system, in the absence of online analyzers or long delays in the measurement signals.
Disadvantages and misapplication of MPC:
MPC technology no longer relies on traditional servo-control techniques, e.g., feedback control was first designed to handle effects from unmeasured disturbances and have done a fairly good job for about 100 years. MPC assumes that the knowledge regarding the process is perfect and that all disturbances have been identified. MPC is an open-loop system. There is no way for an MPC to handle unmeasured disturbances, other than to readjust at each controller execution, and the bias is similar to an integral-only control action.
This partially explains MPC’s poor behavior when challenged by disturbances unaccounted for by the controller. MPC has been sold as the ultimate solution to every plant’s control and optimization needs. But, as engineers point out, for a multitude of reasons, the results from MPC implementations often produce short-lived, sub-optimal and/or poor outcomes. The products now in the marketplace have been over-used and are forced to perform functions for which they were neither designed, nor should have been allowed, to do.
But MPC products were so heavily marketed and over-sold that the few voices of reason that may have existed were overwhelmed and could not prevail. Over the last few years, MPC is applied too much and is continuing to receive records of lackluster performance. It is estimated that more than 50% of applications are in “off mode” or do not work at all (worse than regulatory control); only about 10% are fully working well, according to some industry experts.1,2
Combustion MPC design problem:
A good combustion-control system will save energy and improve the unit efficiency. This will depend on the fuel and air flow both in steady-state and dynamic modes of the process. In some petrochemical furnace systems, the fuel and air are independently controlled by temperature and oxygen. This absolutely devalues the combustion efficiency, in particular, for dynamic processes, e.g., when changing the temperature setpoints or disturbance happens.
Few combustion units are successfully controlled by MPC since the system needs more intelligent factors. Most MPC combustion-control designs use the fuel and air as MVs, and try to rely on the local tested model to achieve the firing rate demand. Obviously, this design does not consider efficient combustion. The correct design is to use the ratio of fuel and air as the MV. The idea for efficient combustion is to have air lead the fuel on increases in demand for and fuel to lead air on decreases in demand. On load increases, the air is increased ahead of the fuel. On load decreases, the fuel is decreased ahead of the air. This motivates us to develop cross-limiting combustion control strategies.
Material balance issues in MPC design:
A process system consists of inputs, outputs and internal dynamics; they work in a fixed balance relationship at all times. Sacrificing this relationship to get a local control/optimization is a short-sighted method, and it eventually will compromise the control/optimization, leading to an unstable state. It is necessary to maintain the materials balance at all times.
Consider these scenarios: 1) the feeds change; 2) outflows have a large change; and 3) tower reflux has major change. All of these conditions move the system into an unstable state or the systems experience wide swings due to a break in the balance relationship if there is no material balance control. MPC is widely used in process systems—the feed is normally feedforward to the MPC control system. When we test the system, we have to test it in a wide range, although, the MPC is still in a local model. The model gain is not able to adapt to the material-balance relationship when system inflows or outflows change.
Dynamic Response
Now let's examine the dynamic response of the proportional control. Assume the process is at steady state and the level is at the setpoint. At time = 0, an increase in the inlet flowrate, regarded as a disturbance, enters into the process. If no control action is taken, i.e. the outlet flowrate is not altered, the level (controlled variable) will increase. With proportional control, the level is brought back and maintained in a certain range near the setpoint. The history curve could typically be like that shown below. Different responses are obtained depending on the proportional band, B, of the controller.