Process Control of Technological Processes
Process Control of Technological Processes
Technological processes consist of handling, working, refining, combining, and manipulating materials and fluids to profitably produce end products. These processes can be precise, demanding, and potentially hazardous processes. Small changes in a process can have a large impact on the end result. Variations in proportions, temperature, flow, turbulence, and many other parameters are to be carefully and consistently controlled to consistently produce the end product of the desired quality with a minimum of raw materials and energy.
Generally, anything which needs continuous monitoring of an operation, involves the role of process control. Process control refers to the methods which are used to control the process variables of the technological process. It is the tool which enables the processes to have the process operation running within the specified limits and to set more precise limits to maximize process efficiency, ensure quality, and safety.
Every technological process needs a great amount of planning in order to successfully accomplish its laid down tasks. However, in order to accomplish these tasks, the process operators are to fully understand the process and the functions of the control systems. Control systems consist of equipment (measuring devices, and control devices etc.) as well as the operator’s intervention. Control systems are used to satisfy three basic needs of the process, namely (i) reduce the influence of external disturbances, (ii) promote the stability of the process, and (ii) enhance the performance of the process.
Instrumentation provides the various indications used to operate a technological process. In some cases, the operator records these indications for use in the operation of the process. The information recorded helps the operator evaluate the current condition of the process and take actions if the conditions are not as expected. Requiring the operator to take all of the required corrective actions is impractical, or sometimes impossible, especially if a large number of indications are to be monitored. For this reason, most technological processes are controlled automatically once they are operating under normal conditions. Automatic controls greatly reduce the burden on the operator and make the job manageable. Technological processes are controlled for three reasons namely (i) reduce variability, (ii) increase efficiency, and (iii) ensure safety.
Process control can reduce variability in the end product, which ensures a consistently high-quality product. With the reduction of the process variability, the process becomes more stable, reliable, productive, and economical. Some of the process parameters are to be maintained at specific levels for maximizing the process efficiency. Accurate control of these parameters ensures process efficiency. Further, a run-away process, such as an out-of-control chemical reaction, can result if during the process operation, the precise control of all of the process variables is not maintained. The consequences of a run-away process can be catastrophic. Hence, the precise control of the process is also required to ensure safety of the equipments and the workers.
The role of process control has changed throughout the years and is continuously being shaped by technology. The traditional role of process control was to contribute to safety, minimized environmental impact, and optimize processes by maintaining process variable near the desired values. In the past, the monitoring of the process parameters was done at the place of the process and the parameters were maintained locally by the operator. As the processes become larger in scale and/or more complex, the role of process automation has become more and more important. Today the automation has taken over the process control functions, which means that operators are helped by computerized distributed control system (DCS) which communicates with the instruments in the field.
Process control is a mixture between the statistics and engineering discipline which deals with the mechanism, architectures, and algorithms for controlling a process. For having an effective process control, besides the understanding of the process technology, it is also necessary to understand the key concepts and general terminology of the process control.
The reason for the control of a process is to have it behave in a desired way. This can involve the process becoming more accurate, more reliable, or more economical. In some cases the uncontrolled process is unstable and good control is necessary in order not to damage it. Hence, good control can mean different things in different applications.
In process control, the basic objective is to regulate the value of some parameter. To regulate means to maintain the quantity of the parameter at some desired value regardless of the external influences. The desired value is called the reference value or set-point. An operator can change the set-point. The process is self regulating if by changing an input set-point, the output changes to match the input set-point. .A self-regulating system does not provide regulation of a variable to any particular reference value. The parameter adopts some value for which input and output values are the same, and there it stays. But if the input flow rate is changed, then the output changes also, so it is not regulated to a reference value.
The operator aided control allows artificial regulation by the operator. For regulating the parameter, so that it maintains the needed value, a sensor is necessary to measure the parameter. The parameter is called the controlled variable. By operating the suitable control equipment, the output parameter can be changed to the set-point by the operator. The output parameter is called the manipulated variable or the controlling variable.
An automatic control system replaces the control system and uses machines, electronics, or computers which replace the operations by the operator. An instrument called a sensor is added which is able to measure the value of the parameter and convert it into a proportional signal. This signal is provided as input to a machine, electronic circuit, or computer called the controller. The controller performs the function of the operator in evaluating the measurement and providing an output signal, to change the control equipment setting via an actuator connected to the equipment by a mechanical linkage. When automatic control is applied to systems which are designed to regulate the value of some variable to a set-point, it is called automatic process control. Fig1 shows conceptual control process showing input output variables in a block diagram.
Fig 1 Conceptual control process showing input output variables in a block diagram
Technological processes are dynamic in nature since they rarely operate at steady state. The operation of the technological processes consists of ensuring that the proper response is made to the ever-occurring disturbances so that operation is safe, efficient, and produces the desired product of specified quality at the required rate. Since the methods of production vary from process to process, the principles of automatic control are generic in nature and can be universally applied, regardless of the size and type of the process. The objectives of a process control system are to perform either one or both of the following tasks.
Maintain the process at the operational conditions and set-points – Many processes are required to work at steady state conditions or in a state in which it satisfies all the requirements such as cost, yield, safety, and other quality objectives. In many real-life situations, a process cannot always remain static and there are disturbances which are taking place to the process making the process unstable. In a process which is not stable, the process variables oscillate from its physical boundaries over a limited time span. The uncontrolled process variables can be controlled simply by adding control instruments and equipments which can control the process variables within their control limits either automatically or through the operator’s interventions.
Transition the process from one operational condition to another – In real-life situations, it becomes sometimes necessary to change the process operational conditions for a variety of different reasons. The reasons for the transitioning the process from one set of the operational conditions to another set of the operational conditions can be due to economics, product specifications, operational constraints, environmental regulations, and changed product specifications etc.
The development of a control strategy for a technological process consists of formulating or identifying (i) control objective(s), (ii) input variables which are either manipulated variables or disturbance variables and which can change continuously, or at discrete intervals of time, (iii) output variables which can be either measured variables or unmeasured variables and which can be measured either continuously or at discrete intervals of time, (iv ) constraints which can be either hard or soft, (v) operating characteristics which can be batch, continuous, or semi-continuous, (vi) safety, environmental, and economic considerations, and (vii) control structure where the controllers can be feed back or feed forward in nature. The formulating of the process control system for a technological process constitutes seven stages.
The first stage of developing the control system is to formulate the control objective(s). The technological process normally consists of several sub-processes. The controls of the technological process get reduced when the controls of the each sub-process are considered separately. Even then, each sub-process can have multiple, sometimes conflicting objectives, so the development of control objectives is normally a difficult issue.
The second stage constitutes determination of the input variables. The input variables show the effect of the surroundings on the process. It normally refers to those factors which influence the process. The input variables can be classified as manipulated or disturbance variables. A manipulated input is one which can be adjusted by the control system (or process operator). A disturbance input is a variable which affects the process outputs but which cannot be adjusted by the control system. There exist both measurable and immeasurable disturbance inputs. Inputs can change continuously or at discrete intervals of time.
The third stage constitutes determination of the output variables. Output variables are also known as the control variables. These are the variables which are process outputs that affect the surroundings. Output variables can be classified as measured or unmeasured variables. Measurements can be made continuously or at discrete intervals of time.
The fourth stage constitutes the determination of the operating constraints. Every process has certain operating constraints, which are classified as hard or soft. The example of a hard constraint is a minimum or maximum flow rate for which a valve is to operate between the extremes of fully closed or fully open condition. The example of a soft constraint is the product composition and it is desirable to specify the composition between certain limits, but it is possible to violate this specification without posing a safety or environmental hazard.
The fifth stage constitutes the determination of the operating characteristics. Operating characteristics are normally classified as batch, continuous, or semi-continuous. Continuous processes operate for long periods of time under relatively constant operating conditions before being ‘shut down’ for carrying out certain jobs such as cleaning, and periodic preventive maintenance etc. Batch processes are dynamic in nature, that is, they normally operate for a short period of time and the operating conditions can vary quite a bit during that period of time. Example batch process is the making of a heat in a steelmaking furnace. For a batch reactor, an initial charge is made to the reactor, and the process conditions are varied to produce a desired product at the end of the batch process. A typical semi-continuous process can have an initial charge to the reactor, but feed components can be added to the reactor during the course of the batch run. Continuous casting process is the example of a semi-continuous process. An important consideration is the dominant timescale of the process. For continuous processes, this is very often related to the residence time of the material in the reactor.
The sixth stage constitutes important considerations regarding safety, environmental, and economic issues. In a sense, economics is the ultimate driving force since an unsafe or environmentally hazardous process ultimately costs more to operate because of regulatory penalties and in-efficiencies. Further, it is important to minimize energy costs while producing products which meet the specifications. Better process automation and control allows processes to operate closer to the ‘optimum’ conditions and to produce products where variability specifications are satisfied.
The concept of ‘fail-safe’ is always important in the selection of instrumentation. As an example, a control valve needs an energy source to move the valve stem and change the flow. Most often it is a pneumatic signal (Normally 3 -15 PSI). If the signal is lost, then the valve stem go to the 3 PSI limit. If the valve is ‘air-to-open’, then the loss of instrument air causes the valve to close and this is known as a ‘fail-closed’ valve. If, on the other hand, a valve is air to close, when instrument air is lost the valve goes to its fully open state and this is known as a ‘fail-open’ valve.
There are two standard control types which are (i) feed-forward control, and (ii) feed-back control. A feed-forward controller measures the disturbance variable and sends this value to a controller, which adjusts the manipulated variable. The purpose of feed-back control is to keep the controlled variable close to its set-point. A feed-back control system measures the output variable, compares the value to the desired output value, and uses this information to adjust the manipulated variable. By its design the feed-back controller takes corrective action to reduce the deviation. A feed-back controller can only take action after the controlled variable deviates from its desired set-point and generates a non-zero error. However, the response to disturbance can be very sluggish, if the process or measurement changes very slowly. In such a situation, a feed-forward controller can improve the performance. The feed-forward controller predicts the effect which the disturbance has on the controlled variable and takes control action which counteracts the influence of the disturbances.
Determining the feed-back control structure for a process consists of deciding which manipulated variable is to be adjusted to control which measured variable. The desired value of the measured process output is called the set-point. There are two reasons for the controlled variable to deviate from its set-point. The set-point is changed deliberately in order to achieve better performance or the disturbance drives the operation away from its desired set-point. A controller designed to reject the disturbance is called regulators while the controller designed to track set-point changes is called servo-mechanism. Usually for the continuous processes set-point changes occur infrequently, typically only if the supervisory controller computes a more favourable operating point, and hence, a regulator is the most common form of feed-back controller used. In contrast, controller for servo-problems is common in batch processes, where frequent changes in the set-points occur.
A particularly important concept used in control system design is the ‘process gain’. The ‘process gain’ is the sensitivity of a process output to a change in the process input. If an increase in a process input leads to an increase in the process output, this is known as a positive gain. If, on the other hand, an increase in the process input leads to a decrease in the process output, this is known as a negative gain. The magnitude of the ‘process gain’ is also important.
Once the control structure is determined, it is important to decide on the control algorithm. The control algorithm uses measured output variable values (along with desired output values) to change the manipulated input variable. A control algorithm has a number of control parameters, which are to be adjusted to have acceptable performance. Frequently the adjustment is done on a simulation model before implementing the control strategy on the actual process. In the case of the model-based control, the controllers have a model of the process ‘built in’.
The block diagram of a technological process with a single manipulated variable and a single controlled variable (Fig 2) includes feed-forward, feed-back, and supervisory control. The main purpose of the feed-back controller is to keep the controlled variable X which is measured by some instrument as close as possible to the desired set point Xsp. The controlled variable can be any parameter of the technological process. Set-point is normally determined by a supervisory control system using real-time numerical optimization technique. There are several different types of final control elements. The disturbance variable D, also called the load variable, can cause the controlled variable to deviate from its set-point, requiring control action in order to bring it back to its desired operating point. Both feed-back and feed-forward control can reduce the effect of disturbance, where each method has its own advantage and disadvantage. Disturbance can result from a variety of sources, including external environmental variables. In any case, a disturbance variable cannot be influenced by the controller of the process. The error or deviation E between the controlled variable X and its set point Xsp is the input to the feed-back controller, which changes the manipulated variable M in order to decrease the error. In a typical technological process, there can be a large number of such control loops.
Fig 2 Block diagram for control of a technological process
Control hardware and software
Process control as practiced in the process industries has undergone significant changes since it was first introduced in the 1940s. In the early 1960s, electrical analog control hardware replaced much of the pneumatic analog control hardware. However, in many processes, certain control elements, i.e., control valve actuators, have remained pneumatic even today. Electrical analog controllers of the 1960s were single-loop controllers in which each input was first brought from the measurement point in the process to the control room where most of the controllers were located. The output from the controller was then sent from the control room to the final control element. The operator interface consisted of a control panel having a combination of display faceplates and chart recorders for single loop controllers and indicators. Control strategies primarily involved feed-back control, usually with a proportional-integral (PI) controller. During the late 1950s and early 1960s, process control computers to perform direct digital control (DDC) and supervisory process control were introduced. In case of the use of DDC, the DDC loops often had close to 100 % analog control back-up making the system costly.
Other early systems primarily used process control computers for supervisory process control. Regulatory control was provided by analog controllers, which did not require backup, but the operator’s attention was split between the control panel and the computer screens. The terminal displays provided the operator interface when supervisory control was being used, but the control panels were still located in the control room for the times when the analog backup was necessary. Within this environment, there was the broaden use of advanced control techniques such as feed-forward control, multi-variable decoupling control, and cascade control. The functionalities of these early control systems were designed around the capabilities of the computers rather than the process characteristics. These limitations, coupled with inadequate operator training and an unfriendly user interface, led to designs which were hard to operate, maintain, and expand. In addition, many different systems had customized specifications, making them extremely expensive. The infusion of digital system applications into the process industries took place around 1970, when inexpensive microprocessors became commercially available.
Distributed control system (DCS) – A DCS consists of many elements as shown in Fig 3. Host computers perform computationally intensive tasks like optimization and advanced control strategies. Data highways, consisting of a digital transmission link, connect all the components in the system. Redundant data highways reduce the possible loss of data. Operator control stations provide video consoles for operator communication with the system, in order to supervise and control processes. Many control stations contain printers for alarm logging, report printing, or hard-copying of process graphics. Remote control units implement basic control functions like PID algorithms and sometimes provide data acquisition capability. Programmer consoles develop application programs for the distributed control system. Mass storage devices store the process data for control purposes as well as corporate decisions.Storage devices can be in form of hard disks or databases. Communications and interactions between controllers, inputs, and outputs are realized by software, not by hardwiring. DCSs, hence, revolutionized many aspects of process control, from the appearance of the control room to the widespread use of advanced control strategies.
Fig 3 Typical structure of DCS system
Programmable Logic Controller (PLC) – Initially, PLCs controllers were dedicated, stand-alone, microprocessor-based devices executing straightforward binary logic for sequencing and interlocks. PLCs significantly improved the ease with which modifications and changes can be implemented to such logic. PLCs have become increasingly more powerful in terms of calculation capabilities. Batch process control is dominated by logic type controls, and PLCs are a preferred alternative to a DCS. Because of the availability of relatively smooth integrated interfaces between DCSs and PLCs, current practice is generally to use an integrated combination of a DCS and PLCs. Most PLCs also handle sequential logic and are equipped with internal timing capability to delay an action by a prescribed amount of time, to execute an action for a prescribed time, and so on.
Safety and shut-down system – Process control plays an important role in the safety considerations of the process. When automated procedures replace manual procedures for routine operations, the probability of human errors leading to hazardous situations becomes lesser. Also, the operator’s awareness of the current plant condition is enhanced. A protective system is to be provided for the hazardous e processes. One way is to provide logic for the specific purpose of taking the process to a state where this condition cannot exist, called a safety interlock system. Since the process control system and the safety interlock system serve different purposes, they are to be physically separated. It reduces the risk of unintentionally changing the safety system. Special high reliability systems have been developed for safety shutdowns, e.g., triple modular redundant systems. This permits the system to have an internal failure and still perform its basic function. Basically a triple modular redundant system consists of three identical subsystems actively performing identical functions simultaneously.
Alarms – The purpose of an alarm is to alert the process operator to a process condition which requires immediate attention. An alarm is activated whenever the abnormal condition is detected and the alert is issued. The alarm returns to normal when the abnormal condition no longer exists. Alarms can be defined on measured variables, calculated variables, and controller outputs. A variety of different classes of alarms exist.
Smart transmitters, valves, and field-bus – There is a clearly defined trend in process control technology toward increased use of digital technology. Digital communication occurs over a field-bus, i.e., a coaxial or fiber optic cable, to which intelligent devices are directly connected and transmitted to and from the control room or remote equipment rooms as a digital signal. The field-bus approach reduces the need for twisted pairs and associated wiring (Fig 4).
Fig 4 DCS with remote room terminals and field- bus
Various field network protocols provide the capability of transferring digital information and instructions among field devices, instruments, and control systems. The field-bus software mediates the flow of information among the components. Multiple digital devices can be connected and communicate with each other via the digital communication line, which greatly reduces wiring.
Process control software – The most widely adopted user-friendly approach is the fill-in-the-forms or table-driven process control languages (PCL). Popular PCLs include function block diagrams, ladder logic, and programmable logic. The core of these languages is a number of basic function blocks or software modules, such as analog in, digital in, analog out, digital out, and PID etc. In general, each module contains one or more inputs and an output. The programming involves commuting outputs of blocks to inputs of other blocks via the graphical-user interface. Users are required to fill in templates to indicate the sources of input values, the destinations of output values, and the parameters for forms/tables prepared for the modules. The source and destination blanks can specify process I/O (input/output) channels and tag names when appropriate. To connect modules, some systems require filling in the tag names of modules originating or receiving data. User specified fields include special functions, selectors (minimum or maximum), comparators (less than or equal to), and timers (activation delays). Most DCSs allow function blocks to be created.
Facility control hierarchy – The five levels in the technological process where various optimization, control, monitoring, and data acquisition activities are employed are shown in Fig 5.The relative position of each block in the figure is intended to be conceptual, because there can be overlap in the functions carried out. The relative time scales where each level is active are also shown. Each of the five conceptual control levels has its own requirements and needs in terms of hardware, software, techniques, and customization. Because information flows up in the hierarchy and control decisions flow down, effective control at a particular level occurs only if all the levels beneath the level of concern are working well. The highest level (planning and scheduling) sets production goals to meet supply and logistics constraints and addresses time-varying capacity and manpower utilization decisions. This is called enterprise resource planning (ERP).
Fig 5 Five levels of process control and optimization
Generally, the various levels of control applications are aimed at one or more of the following objectives namely (i) determining and maintaining the process at a practical optimal operating point, (ii) maintaining safe operation for the protection of personnel and equipment, (iii) minimizing the need for operator attention and intervention, and (iv) minimizing the number, extent, and propagation of upsets and disturbances.
Instrumentation – It consists of the components of a control poop. Instrumentation, which provides the direct interface between the process and the control hierarchy, serves as the fundamental source of information about the process state and the ultimate means by which corrective actions are transmitted to the process. The function of the process measurement device is to sense the value, or changes in value, of process variables. The actual sensing device can generate a physical movement, pressure signal, and milli-volt signal etc. A transducer transforms the measurement signal from one physical or chemical quantity to another, e.g., pressure to milli-amps. The transduced signal is then transmitted to the control room through the transmission line. The transmitter is hence a signal generator and a line driver. The modern control equipment requires a digital signal for displays and control algorithms, thus the analog-to-digital converter (ADC) transforms the transmitter’s analog signal to a digital format.
The most commonly measured process variables are temperatures, flows, pressures, levels, and composition. When appropriate, other physical properties are also measured. The selection of the proper instrumentation for a particular application is dependent on factors such as the type and nature of the fluid or solid involved, relevant process conditions, rangeability, accuracy, and repeatability required, response time, installed cost, and maintainability and reliability.
Signal transmission and conditioning – A wide variety of phenomena are used to measure the process variables required to characterize the state of a process. Because most processes are operated from a control room, these values are to be available there. Hence, the measurements are usually transduced to an electronic form, most often 4-20 mA, and then transmitted to a remote terminal unit and then to the control room. It is especially important that proper care is taken so that these measurement signals are not corrupted owing to ground currents, interference from other electrical equipment and distribution, and other sources of noise.
Final control elements – Good control at any hierarchial level needs good performance by the final control elements in the next lower level. At the higher control levels, the final control element can be a control application at the next lower control level. However, the control command ultimately affects the process through the final control elements at the regulatory control level, e.g., control valves, pumps, dampers, louvers, and feeders etc.
Process dynamics and mathematical models – A thorough understanding of the time-dependent behaviour of the technological processes is required in order to instrument and control the process. This in turn requires an appreciation of how mathematical tools can be employed in analysis and design of process control systems. There are several mathematical principles which are utilized for the automatic control. These are (i) physical models and empirical models, (ii) simulation of dynamic models, (iii) Laplace transforms, transfer functions, and block diagrams, and (iv) fitting dynamic models to experimental data etc.
Feed -back control systems – Measurements of the controlled variable are available in many process control problems. Specifically, this is the case when temperatures, pressure, or flows are to be controlled. In these situations the controlled variable can be directly measured and the manipulated variable is adjusted via a final control element. A feedback controller takes action when the controlled variable deviates from its set-point, as detected by the non-zero value of the error signal. The various types of feed-back controls are (i) on/off control, (ii) proportional control, (iii) proportional plus integral (PI) control, (iv) proportional plus integral plus derivative (PID) control, and (v) digital PID.
The simplest controller can only show two settings and is called an on/off controller. The output of this controller is either at its maximum or its minimum value, depending on the sign of the error. While this type of controller is simple, it is seldom used. The proportional controller offers more flexibility than the on/off controller because the manipulated variable is related not just to the sign of the error but also to its magnitude. The input-output behaviour of an actual proportional controller has upper and lower bounds i.e. the output saturates when the control limits are reached. Standard limits on the controller output are 3-15 PSI for pneumatic controllers, 4-20mA for electric controllers, and 0-10 VDC for digital controllers.
Integrating action needs to be included in the control loop, if an offset-free response in the presence of constant load disturbances or for set point changes is needed. If the process does not show integrating behaviour itself then it is possible to implement a proportional plus-integral controller to achieve the desired performance. There are both and disadvantages associated with integral action in a controller. One disadvantage of a PI controller is that the integral action can cause it to react more sluggishly than a proportional controller. If it is important to achieve a faster response which is to be offset-free then this can be accomplished by including both derivative and integral action in the controller. In order to anticipate the future behaviour of the error signal, a PID controller computes the rate of change of the error, thus the directional trend of the error signal influences the controller output. While many controllers have traditionally been analog PI/PID controllers, the trend towards digital control systems has also had an influence on controller implementation. In many modern process plants the analog PI/PID controllers have been replaced by the digital counterparts.
Open-loop and closed-loop dynamics – Open-loop dynamics refers to the behaviour of a process if no controller is acting on it. Similarly, if the controller is turned off by setting the proportional constant to zero, the control system shows open-loop behaviour and the system’s dynamics are solely determined by the process. Hence, it is not possible to reach a new set-point for a process in open-loop unless the input is changed manually. It is also not possible to reject disturbances when the process is operated without a controller.
The purpose of using closed-loop control is to achieve a desired performance for the system. This can result in the system being stabilized, in a faster system response to the set-point changes, or in the ability to reject disturbances. The choice of the controller type as well as the values of the controller tuning parameters influences the closed-loop behaviour. For a controlled process one needs to find controller settings which result in a fast system response with little or no offset. At the same time, the system is to be robust to the changes in process characteristics. Finding the appropriate settings is called ‘tuning’ the controller.
Controller tuning and stability – Finding of the optimum tuning parameters for a controller is an important task. Unsuitable parameters can result in not achieving the desired closed-loop performance (e.g. slowly decaying oscillations, or a slow acting process). It is also possible that a closed-loop process with a badly tuned controller can result in performance which is worse than for the open-loop case or that the process can even become unstable.
Mathematical software for process control – A variety of different software packages is available which support the controller design, controller testing, and implementation process.
Advanced control techniques
While the single-loop PI/PID feedback controller is satisfactory for many process applications, there are cases for which advanced control techniques can result in a significant improvement in closed-loop performance. These processes often show one or more of such phenomena as (i) slow dynamics, (ii) time delays, (iii) frequent disturbances, (iv) multi-variable interaction. A large number of advanced control strategies are being used. Some important ones are briefly discussed below.
Feed- forward control – One of the disadvantages of conventional feed-back control with large time lags or delays is that disturbances are not recognized until after the controlled variable deviates from its set point. However, if it is possible to measure the load disturbance directly then feed-forward control can be applied in order to minimize the effect which this load disturbance has on the controlled variable. In addition to being able to measure the load disturbance, it is also needed to determine a mathematical correlation for the effect which the load disturbance has on the controlled variable in order to apply a feed-forward controller. The reason for this is that the feed-forward controller inverts this model in order to cancel the effect that the disturbance has. A feed-forward controller can be designed either based on the steady-state or dynamic behavior of the process.
Cascade control – Another possibility of controlling processes with multiple or slow-acting disturbances, is to implement cascade control. The main idea behind cascade control is that more than just one controller is used to reject disturbances. Instead a secondary controller is added to take action before the slow-acting disturbance has an effect on the primary controlled variable. In order to achieve this, the secondary controller also requires a secondary measurement point which needs to be located so that it recognizes the upset condition before the primary controlled variable is affected. Cascade control strategies are among the most popular process control strategies.
Selective and override control – Some processes have more controlled variables than manipulated variables. Such a situation does not allow an exact pairing of controlled and manipulated variables. A common solution is to use a device called a selector which chooses the appropriate process variable from among a number of valid measurements. The purpose of the selector is to improve control system performance as well as to protect equipment from unsafe operating conditions by choosing appropriate controlled variables for a specific process operating condition. Selectors can be based on multiple measurement points, multiple final control elements, or multiple controllers.
Adaptive control and auto-tuning – Operating conditions of a process can frequently change during plant operations. This does lead to the process behaving differently from the model which has been used for the controller design. Hence, the controller does not have accurate knowledge of the process at the current operating point and hence cannot be able to provide adequate disturbance rejection or set-point tracking. One possibility to circumvent this is to use an adaptive control system which automatically adjusts the controller parameters to compensate for changing process conditions. Auto-tuning is a related method where the closed-loop system is periodically tested, and the test characteristics automatically determine new controller settings.
Fuzzy logic control – For many processes, it is very time consuming to determine accurate process models. However, at the same time, it can be intuitive to get a rough estimate of how the manipulated variable is to react to a process condition. For such a case, fuzzy logic controllers can offer an advantage over conventional PID controllers. The reason for this is that fuzzy controllers do not need an exact mathematical description of a process. Instead, they classify the controller inputs and output as belonging to one of several groups (i.e. low, normal, and high). Fuzzy rules are then used to compute the output category from the given inputs. These rules either have to be provided by the control engineer or they have to be identified from plant operations by auto-tuning. It is also possible to combine fuzzy logic controllers with neural networks in order to form neuro-fuzzy controllers. This type of controller can offer significant advantages over conventional PID when applied to non-linear systems whose characteristics change over time.
Statistical process control (SPC) – SPC, also called statistical quality control (SQC), has found widespread application in recent years due to the growing focus on increased productivity. Another reason for its increasing use is that feed-back control cannot be applied to many processes due to a lack of on-line measurements. However, it is important to know if these processes are operating satisfactorily. While SPC is unable to take corrective action while the process is moving away from the desired target, it can serve as an indicator that product quality might not be satisfactory and that corrective action are to be taken for further plant operations.
For a process which is operating satisfactorily, the variation of product quality falls within acceptable limits. These limits normally correspond to the minimum and maximum values of a specified property. Normal operating data can be used to compute the mean deviation and the standard deviation s of a given process variable from a series of observations. The standard deviation is a measure for how the values of the variable spread around the mean. A large value indicates that wide variations in the variable. Assuming the process variable follows a normal probability distribution, then 99.7 % of all observations is to lie within an upper limit and a lower limit. This can be used to determine the quality of the control. If all data from a process lie within the limits, then it can be concluded that nothing unusual has happened during the recorded time period, the process environment is relatively unchanged, and the product quality lies within specifications. On the other hand, if repeated violations of the limits occur, then the conclusion can be drawn that the process is out of control and that the process environment has changed. Once this has been determined, the process operator can take action in order to adjust operating conditions to counteract undesired changes which have occurred in the process conditions.
Multi-variable control – Many technological processes contain several manipulated as well as controlled variables. These processes are called multi-variable control systems. It is possible to analyze the interactions among the control loops with techniques like the relative gain array. If it turns out that there are only small interactions between the loops then it is possible to pair the inputs and outputs in a favourable way and use single loop controllers which can be tuned independently from one another. However, if strong interactions exist, then the controllers need to be detuned in order to reduce oscillations.
Model predictive control (MPC) – MPC is a model-based control technique. It is the most popular technique for handling multi-variable control problems with multiple inputs and multiple outputs (MIMO) and can also accommodate inequality constraints on the inputs or outputs such as upper and lower limits. All of these problems are addressed by MPC by solving an optimization problem and therefore no complicated override control strategy is needed. A variety of different types of models can be used for the prediction. Choosing an appropriate model type is dependent upon the application to be controlled. The model can be based upon first-principles or it can be an empirical model. Also, the supplied model can be either linear or nonlinear, as long as the model predictive control software supports this type of model.
Real-time optimization – Operating objectives for process facilities are set by economics, product orders, availability of raw materials and utilities, etc. At different points in time it can be advantageous or necessary to operate a process in different ways to meet a particular operating objective. A technological process, however, is a dynamic, integrated environment where external and internal conditions can cause the optimal operating point for each operating objective to vary from time to time. These operating points can be computed by real-time process optimization (RTO), where the optimization can be performed on several levels, ranging from optimization within model predictive controllers, to supervisory controllers which determine the targets for optimum operation of the process, to optimization of production cycles. The plant-wide problems which can be solved by optimization techniques on a daily or hourly basis can be large containing thousands or even tens of thousands of variables.
Batch and sequence control
In batch processes, the product is made in discrete batches by sequentially performing a number of processing steps in a defined order on the raw materials and intermediate products. Large production runs are achieved by repeating the process. The term recipe has a range of definitions in batch processing, but in general a recipe is a procedure with the set of data, operations, and control steps to manufacture a particular grade of product. A formula is the list of recipe parameters, which includes the raw materials, processing parameters, and product outputs. A recipe procedure has operations for both normal and abnormal conditions. Each operation contains resource requests for certain ingredients (and their amounts). The operations in the recipe can adjust set-points and turn equipment on and off. The complete production run for a specific recipe is called a campaign (multiple batches). A production run consists of a specified number of batches using the same raw materials and making the same product to satisfy customer demand. The accumulated batches are called a lot.
In multi-grade batch processing, the instructions remain the same from batch to batch, but the formula can be changed to yield modest variations in the product. In flexible batch processing, both the formula (recipe parameters) and the processing instructions can change from batch to batch. The recipe for each product must specify both the raw materials required and how conditions within the reactor are to be sequenced in order to make the desired product.
Batch process control hierarchy – Functional control activities for batch process control can be summarized in four categories namely (i) batch sequencing and logic control, (ii) control during the batch, (iii) run-to- run control, and (iv) batch production management.
In batch sequencing and logic control, sequencing of control steps follow the recipe involve. For example: mixing of ingredients, heating, waiting for a reaction to complete, cooling, or discharging the resulting product. Transfer of materials to and from batch reactors includes metering of materials as they are charged (as specified by each recipe), as well as transfer of materials at the completion of the process operation. In addition to discrete logic for the control steps, logic is needed for safety interlocks to protect personnel, equipment, and the environment from unsafe conditions. Process interlocks ensure that process operations can only occur in the correct time sequence for a prescribed period of time. Detection of when the batch operations are to be terminated (end point) can be performed by inferential measurements of product quality, if direct measurement is not feasible.
Run-to-run control (also called batch-to-batch) is a supervisory function based on off-line product quality measurements at the end of a run. Operating conditions and profiles for the batch are adjusted between runs to improve the product quality using tools such as optimization. Batch production management entails advising the plant operator of process status and how to interact with the recipes and the sequential, regulatory, and discrete controls. Complete information (recipes) is maintained for manufacturing each product grade, including the names and amounts of ingredients, process variable set points, ramp rates, processing times, and sampling procedures. Other database information includes batches produced on a shift, daily, or weekly basis, as well as material and energy balances. Scheduling of process units is based on availability of raw materials and equipment and customer demand.
Sequential function charts – Compared to a continuous process, batch process control requires a greater percentage of discrete logic and sequential control than regulatory control loops. Batch control applications is to control the timing and sequencing of the process steps based on discrete input and outputs as well as analog outputs. The complexity of the interactive logic within and between the various control levels, the required interactions with operators and the need for ongoing application modification and maintenance are reasons why organization, functional design, and clear documentation are so important to the successful use of batch control applications. In order to describe what is to be done, structural models are normally used to represent the required batch processing actions, the batch equipment, and the combination of components. Various formats have been proposed for describing the batch control applications, e.g., how the batch processing steps are carried out with the batch equipment and instrumentation, interfaces between the various levels of control, interfaces between the batch control and the operator actions and responses, and interactions and coordination with the safety interlocks. The formats proposed include flow charts, state charts, decision tables, structured pseudo-code, state transition diagrams, petri nets, and sequential function charts. A sequential function chart (SFC) describes graphically the sequential behaviour of a control program.
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