Despite the challenges of applying Model Predictive Control (MPC) to process control projects, it is still worthwhile because its performance is often significantly better than conventional control methods. Therefore, in today's highly competitive economic environment, adopting this new technology to better achieve factory production and efficiency goals has become an increasingly important competitive tool. The most powerful feature of MPC is its ability to simulate various real-world process control scenarios to the greatest extent possible by designing the future trajectories of the controlled variables (CVs). A representative scenario is a large amplitude given by low-intensity control. The greatest advantage of choosing Model Predictive Control is its easy integration with process optimizers (the subject of the latter two sections) and the significant economic benefits that are difficult to achieve using conventional control strategies. [align=center] Figure 1: The effects of process interactions are clear: the flow rates of the two components affect all three controlled variables; while the steam flow rate only affects the product temperature. Figure 2: A reactor model based on the effect of the B flow rate factor on the product temperature shows an inverse response. Figure 3: MPC responds to the reactor after eliciting the same change. [/align] Obtaining good data during testing is the most important step. The main steps of applying MPC in reactor control include: ■ Acquiring data representing the response relationships during the control process; ■ Defining the controlled variables, manipulated variables, and disturbance variables of the process model; ■ Developing the process model using model identification tools; ■ Integrating the process model into the terminal controller; ■ Controller commissioning and final deployment. Process Testing. Initially, the relevant controlled variables, manipulated variables, and important disturbance variables are not defined beforehand. If the entire control process is complex, the definition becomes even more difficult. The dynamic interactions between these variables are often unclear. Process testing provides the necessary information. In process testing, the effects of manipulated variables on controlled variables, as well as any changes caused by possible disturbance variables, need to be recorded progressively. This often forms a pseudo-random binary sequence (PRBS) test. Figure 1 reflects some PRBS test data collected from the target reactor at this stage. It shows the changes in factor A, factor B, and steam flow rate over any given time period and their impact on the controlled variables. Model Structure Definition. This is a crucial step and is not as simple as it sounds. Because engineers are not always certain which variables should be included in MPC, they must identify: ■ The key controlled variables that affect product quality and production volume; and ■ The manipulated variables that most significantly influence the controlled variables. Engineers must also identify measurable disturbances that will cause significant impact or change. Data testing provides quantitative analytical basis, and understanding this is crucial for correctly identifying relevant dependent and independent variables, and more importantly, for understanding the process. Engineers must determine whether the controlled variables are setpoints or mandatory variables. Finally, if available degrees of freedom, designers must determine which manipulated variables will have target values. For example, in a reactor, the variables include: ■ Three controlled variables; ■ Three manipulated variables; ■ Two feedforward variables. Since each controlled variable has its own setpoint and there are only 3 degrees of freedom (manipulated variables), independent multivariate target values ​​cannot be defined. Model Identification. Because all model-based predictive control software packages include tools for identifying process models from test data, several key questions must be addressed, including: ■ What will be the model's prediction interval? The model prediction interval determines the time interval at which the system's future behavior is predicted. This value must be small enough to adequately satisfy the fastest controlled variable dynamic process. ■ How many function coefficients will there be in the model? The number of coefficients in the model determines the predicted historical record. It must be sufficient to encompass the entire response process and must also satisfy the length of time required to complete the change caused by a single input. ■ What will be the model's prediction range? This is the length of time the prediction will take place. Unless individual models have special range requirements, this time must generally be long enough for the slowest model to complete its response. If the process is multivariable, some output controlled variables will be affected by some input manipulated variables and disturbances. A matrix containing independent input/output models is a convenient way to express this entire set of input/output relationships. Figure 2 shows the entire segmented response process of the reactor model: ■ The left axis includes the controlled variables of the product: proportions, flow rate, and temperature; ■ The top axis represents the manipulated variables and feedforwards—A flow factor, B flow factor, steam flow rate, and temperature factor. Controller integration control platform. Although many settings must be configured, this is the simplest process because it is largely mechanical and programmed. Details vary depending on the specific control scheme. Controller debugging. When the controller is put into application, all previous efforts yield results. The most fundamental difference between traditional control methods and model-based control methods immediately becomes apparent. With traditional control methods, the entire controller response can be achieved by simply setting parameters during debugging. However, the control behavior of a model controller depends almost entirely on its model. If the model is accurately designed, the controller will work well. If the model is inaccurate, the controller will fail to achieve the expected results. Tuning parameters have only a very small impact on the controller's response. Perfecting an incorrect model through debugging is very difficult, if not impossible. Therefore, obtaining effective data during testing should be the most important step in applying model-based predictive control. Figure 3 compares MPC with previous control methods, concluding that model-based control outperforms previous control methods in all aspects. Under normal conditions, changing the production rate yields the following comprehensive indicators: ■ 3.3 times better than Advanced Process Control (ARC); ■ 62 times better than Basic Control (BRC); and ■ 103 times better than fuzzy logic control. After changing the setpoint of the product composition, it outperforms Advanced Process Control (ARC) by 9% and fuzzy logic control by 70%. Like ARC, Model-Based Predictive Controllers (MPCs) operate similarly to multivariable controllers. Although only one setpoint changes, the controller correspondingly alters the values ​​of all controllable variables. By better understanding the dynamic process, MPCs improve their performance and thus outperform ARC. The performance improvement largely stems from better temperature control. By better understanding the temperature dynamics, MPCs can control actions within a more appropriate range. In this example, the controller first changes the previous steady-state value of the steam flow rate in response to the change in the composite setpoint. This appropriately compensates for the inverse response in the process and maintains temperature stability. Conversely, the product composition response to the setpoint change does not show a significant difference in the given value. The setpoint simply changes from 1.7 to 2.7. The component response is controlled by the dead time, but no controller can eliminate the effect of the dead time. Even if a controller can perfectly respond to the setpoint change, there is still a delay in its corresponding control action. Within a dead zone, there must be a deviation equivalent to the setpoint change. A minimum ISE is unavoidable. Due to this error and dead zone, the minimum ISE is approximately 1.5 units. Only when the ISE exceeds this value can it be eliminated by control. For BRC, this value is 0.29; for MPC, it is 0.21. Compared to BRC, MPC reduces this value by approximately 28%. Rule-based control, on the other hand, increases it by nearly 413%. Impact on Operators Operating the MPC system presents a new challenge for operators. When using it, operators must first consider multivariate scenarios. The controller generates multiple changes and simultaneously achieves multiple objectives; however, the effects may not be immediately apparent. Furthermore, because the controller is sensitive to dynamic changes, such as inverse reactions and delayed responses, operators may not immediately understand changes in its control logic. In addition, MPC introduces some new control objects. Some variables will be classified as constraint variables, and the controller will only respond when they approach their limit values. The purpose of the manipulated variable may be a new concept that requires careful definition. Operators may be unfamiliar with the controller's human-machine interface. In addition to setpoints, operators also need to input limit values. Because the number of variables within the controller is limited, this information may only be displayed in tabular form. Furthermore, the controller's state transitions will be more complex. Traditional PID controllers have only two states—manual and automatic—and the switch between these states is instantaneous. However, MPCs will inevitably go through a longer period and several states before reaching full control. At the same time, because MPCs provide setpoints for lower-level control modes, the operator will also see other states and the transitions between these controllers.