What type of component is a decision making device that compares a sensed value to a setpoint?

7.1.1 BPCS

BPCS stands for basic process (plant) control system. This system handles the process controls and monitoring for the process. It takes the input from process sensors processes it according to control and monitoring strategy fixed at design stage to produce output for output devices/final control element, so that the process behaves according to design. Sometimes it also undertakes safety functions. According to IEC 61511, “BPCS is a key layer of protection which responds to input signals from the process, its associated equipment, other programmable systems and/or operator and generates output signals causing the process and its associated equipment to operate in the desired manner but which does not perform any safety instrumented functions with a claimed SIL ≥ 1.”

13.15.4 Basic Process Control System

The BPCS is not usually an IPL. It is, nevertheless, the next line of defense after the process design and has an important part to play. The Guidelines therefore deal with the safety considerations in the selection and design of the BPCS. The account given covers: (1) the technology selection, (2) the signals, (3) the field measurements, (4) the final control elements, (5) the process controllers, (6) the operator/control interfaces, (7) communication considerations, (8) electrical power distribution systems, (9) control system grounding, (10) batch control, (11) software design and data structures, and (12) advanced computer control strategies, and contain much practical material on these features.

The Guidelines advise that use of a supervisory computer should be subject to a discipline which restricts it to manipulation of loop set points. It should not normally be able to change the operational mode of the loops except for transfer to the back-up mode on computer failure or to computer mode on initialization. It should not compromise the integrity of the back-up controls.

The design philosophy of the Guidelines requires that the BPCS and the SIS should be separate systems. The BPCS should not be relied on to protect against unsafe process conditions. The integrity of the SIS should not be compromised by the BPCS. Appendix B of the Guidelines gives detailed guidance on separation.

7.16.6.1.4 Intelligent process control

Although Al-based POC technologies are still relatively immature, recent advances in AI tools and methodologies have resulted in an increased interest in capitalizing on the vast capabilities associated with the application of intelligent process control (IPC) in the control of advanced composite materials.70,71 In general, IPC can be viewed as a special form of closed-loop feedback control wherein desired process states are obtained by modifying critical process control parameters given the state of the current process. IPC systems are different from conventional, recipe-based POC systems because they rely on sensed data to self-direct the process using an Al-based analysis, optimization, and control strategy.

Typically, IPC systems are modeled in one of two ways:

Forward models,

Inverse models.

A forward model uses the current state and the actions that can be applied in that state to predict the results of these actions relative to the next state. An inverse model, on the other hand, assumes a “desired” or “expected” outcome and determines what action is required given the current state.

IPC systems require learned dynamic knowledge about the material system and the process to enable adaptive optimization and control of the multiple, conflicting, and often nonlinear aspects of the process. Therefore, the primary elements in any adaptive IPC system include

A mechanism for capturing sensor data.

A front-end processor that conditions, interprets, transforms, and analyzes the sensor data.

An optimization controller that models the control laws in accordance with a control policy.

Actuators that implement the recommended control policy by taking some action based on the feedback from the controller.

Overall, the IPC framework described above is flexible and robust. It is flexible in its ability to acquire the information needed to control the relevant processes and, based on the analysis of this information, appropriately adapt these control processes. It is robust in that it supports a wide range of Al-based adaptive capabilities that are superior to other less robust POC approaches.

20.2 Controller Modes

Process control systems can be divided into two major groups:

Continuous control that involves monitoring and controlling of events continuously,

Digital controls that involve the use of computers and microprocessors.

In this book, the continuous control systems are mostly dealt as they form the basis of the present digital system.

Controlling, say the level of a flotation tank which is being filled continuously and from which the pulp is withdrawn continuously, can be done crudely by observing the rise (or fall) of level and restoring it manually by manipulating valves and increasing or decreasing the input flow rate to the tank. Such an on–off method would result in an unsteady profile of level (Figure 20.1). This situation is unacceptable in most mineral processing circuits.

To solve the problem, instruments have been devised and strategies developed to minimize the fluctuations in level. Automatic controllers have therefore been devised which serve to control flow rates, density of slurries, tank and bin levels, pump operations and almost all unit operations such as crushers, mills, screens, classifiers, thickeners, flotation vessels and material handling systems.

The two basic control strategies or modes of these controllers are known as:

feed back control system and

feed forward control system.

In the feed back control system the output from a process is monitored continuously by a sensor. When the output changes the sensor detects the change and sends signals to a comparator which compares the signal with the set point for normal steady-state operation. It then estimates the error or the deviation from the mean. The error signal is passed on to the controller which compares the signal with the true set point and sends a signal to an operating device to reduce the error to zero. The signals are electrical, mechanical or pneumatic devices. Figure 20.2 is a typical block diagram illustrating the feed back system.

It can be seen that the comparator has three functions. Its first function is to correctly receive signals of measured value from the signal monitor. Its second function is to compare the signal with the set point and then compute the deviation against the norm (set point) and its third function is to activate the final controller to correct the error.

There are two process factors that make the feed back control unsatisfactory. These are the occurrence of frequent disturbances, often of large magnitude, and the lag time within the process between occurrence of an event and delay in recognising the signal. As shown later, these disturbances and lag times can be measured and corrective steps applied.

In the feed forward set-up, the input signal, say of the feed, is monitored and controlled prior to the feed entering the process. In so doing, it is expected that the feed to the process is unaltered and therefore the process performance remains unaffected. A block diagram of the feed forward system (Figure 20.3) illustrates the principle of its operation.

In this set-up the indicator in the input stream indicates the deviation in the input stream characteristics (like feed flow rate) to the controller. The controller confines its activity to the incoming stream (and not on the process), computes the magnitude of the error and signals to the controller to provide appropriate action to restore the input stream characteristics to its original level.

Controllers are designed so that the output signal is:

proportional to the error,

proportional to the integral of the error,

proportional to the derivative of the error,

proportional to a combination of the modes.

When the output signal O is proportional to the error, it is known as proportional controller. Mathematically, the control action is expressed as

(20.1)O=GPe

where

GP = the proportionality constant, usually known as the gain and

e = the error

It can be seen that the gain is the ratio of the fractional change in the ratio of the output to input signals. When e = 0, the output signal is also equal to 0. That is, no signal is emitted from the monitor. In this situation, Equation (20.1) is written as

(20.2)O=OO+GPe

The proportional operation is expressed as proportional band. The band width is the error to cause 100% change on the metering gauge or chart.

The integral controllers are known as reset controllers. They are so designed that the output is proportional to the time integral of the error. Thus, the output signal, O, is given by

(20.3)O=GI∫0te.dt

where GI is a constant.

For integral mode the reset action is more gradual than the proportional controllers.

The derivative mode of controllers stabilizes a process and the controller occupies an intermediate position. An example would be the monitoring of bubbling fluid level where only the average fluid level is measured and monitored, like the level in a flotation cell. The output signal in the derivative mode is expressed as

(20.4)O=GDdedt

where GD is the constant.

In practice, the proportional mode is usually combined with integral or derivative modes but most of the time all the three modes are combined. In each combination the output is an additive function, that is for:

For proportional and integral (P + I) mode:

(20.5)O=OO+GPe+GI∫0te.dt

For proportional + derivative (P + D) mode:

(20.6) O=OO+GPe+GDdedt

For proportional + integral + derivative (P + I + D) mode:

(20.7)O=OO+GP e+GI∫0te.dt+GDdedt

When any controller receives a signal from a sensor, the response time depends on the mode of the controller. Of the three modes, the response of the P + I + D (PID) controllers is the fastest. The P + I (PI) controllers take slightly more time, while the P + D (PD) and P controllers never return to the original situation but remain at a level. The difference between the original level and the new steady level of proportional controllers is known as off-set or the droop. The off-set value is, therefore, the difference between the steady state and the required control level or set point. In the P + I or P + I + D control systems no off-set is necessary. Figure 20.4 illustrates the relative control time taken by controllers operating on different modes.

In the operation of a P + I + D controller the derivative term signifies the rate of control action on a process affected by a disturbance.

4.1.2 Fusion of Maintenance and Control Data

Process control systems, such as those used in the oil and gas industry, pulp and paper industry, or other process industries, typically include one or more centralized or decentralized process controller communicatively coupled to at least one host or operator workstation and to one or more process control and instrumentation device, such as field devices. These devices, for example, valve, switches, and sensors, perform functions within the process, such as opening or closing valves and measuring process parameters.

The process controller receives signals indicative of process measurements or process variables made by or associated with the field devices and/or other information pertaining to the field devices. It uses this information to implement a control routine and then generates control signals which are sent over one or more of the buses to the field devices to control the operation of the process. Information from the field devices and the controller is typically made available to one or more applications executed by an operator workstation; this enables the operator to perform desired functions with respect to the process, such as viewing the current state of the process, modifying the operation of the process, etc. While a typical process control system has many process control and instrumentation devices, such as valves, transmitters, sensors, etc., connected to one or more process controllers with software that controls these devices during the operation of the process, many other supporting devices are necessary for or related to process operation.

These additional devices include power supply equipment, power generation and distribution equipment, rotating equipment, etc., located at numerous places in a typical plant. This equipment does not necessarily create or use process variables and, in many instances, is not controlled or even coupled to a process controller for the purpose of affecting the process operation. Nevertheless, it is important to and ultimately necessary for proper operation of the process. In the past, however, process controllers were not necessarily aware of these other devices or simply assumed they were operating properly. Fig. 4.2 shows a fan monitored by two accelerometers in such a way that the information generated by vibrations can close the loop and couple the device into the control system (Galar et al., 2012a).

Many process plants have other software systems, which execute applications related to business functions (enterprise resource planning, ERP) or maintenance functions (CMMS). In fact, many process plants, especially those using smart field devices, have applications to monitor and maintain the plant's devices regardless of whether they are process control and instrumentation devices or other types.

The integration of maintenance information, management, and monitoring is essential to close the loop of the process. That is why CMMS systems have evolved. Enterprise asset management (EAM) software is more sophisticated than CMMS (Fu et al., 2002). Such solutions usually enable communication with field devices and store data pertaining to them, allowing the operating state of the field devices to be tracked. In some instances, the EAM application may be used to communicate with a device to change parameters within that device, to cause it to run applications on itself, such as self-calibration routines or self-diagnostic routines, or to obtain information about its status or health, etc.

The information may be stored and used by a maintenance person to monitor and maintain these devices. Other types of applications are used to monitor other types of devices, such as rotating equipment and power generation and supply devices. These other applications are sometimes available to maintenance persons and used to monitor and maintain the devices within a process plant. In many cases, however, outside service organizations perform services related to monitoring process performance and equipment. They acquire the data they need, run typically proprietary applications to analyze the data and merely provide results and recommendations to the process plant personnel. While this is helpful, the plant personnel have little or no ability to view the raw data or to use the analysis data in any other manner. Fig. 4.3 shows a flow diagram of the information produced when CM is outsourced and a final report is recorded in the system (Galar et al., 2012a).

In typical plants, the functions associated with process control activities, device and equipment maintenance and monitoring activities, and business activities such as process performance monitoring are separate, both in the location where they take place and in the personnel who typically perform them. Furthermore, the people involved in these various functions generally use different tools, such as different applications run on different computers. In many instances, these different tools collect or use different types of data associated with or collected from the different devices within the process and are set up differently to collect the data they need. However, there should be cooperation among all departments in an enterprise and between experts in their respective domain knowledge if a maintenance policy is to succeed (Yu et al., 2004).

Process control operators generally oversee the day-to-day operation of a process and are primarily responsible for assuring the quality and continuity of its operation. They typically affect the process by setting and changing set points within it, tuning its loops, scheduling its operations, etc. Maintenance personnel are primarily responsible for assuring that the actual equipment within the process is operating efficiently and for repairing and replacing malfunctioning equipment. They use tools, such as maintenance interfaces, the EAM application discussed above, and many other diagnostic tools, to get information about the operating states of the devices within the process. Maintenance persons also schedule maintenance activities, which may require portions of the plant to be shut down. For many newer types of process devices and equipment, for example, smart field devices, the devices themselves may include detection and diagnostic tools which automatically sense problems with the operation of the device and automatically report them to a maintenance person via a standard maintenance interface.

Maintenance interfaces and maintenance personnel comprise a huge data network (Davies and Greenough, 2000), generally located apart from process control operators, as shown in Fig. 4.4. This is not always the case, however. In some process plants, process control operators perform the duties of maintenance persons or vice versa, or the different people responsible for these functions may use the same interface (Galar et al., 2012a).

Many applications are used to perform the different functions within a plant, i.e., process control operations, maintenance operations, and business operations. They are not integrated and, thus, do not share data or information. In many cases, tasks, such as monitoring equipment, testing the operation of devices, determining if the plant is running in an optimal manner, etc., are performed by outside consultants or service companies who measure the data needed, perform an analysis, and provide only the results of the analysis to the plant personnel. In these cases, the data are typically collected and stored in a proprietary manner and rarely made available to the plant personnel for other uses.

Given the abundance of data analysis and other detection and diagnostic tools available in the process control environment, either in the plant itself or via outside service companies or consultants, a great deal of information about the health and performance of devices is available to maintenance personnel which could also be helpful to process operators or business officers. Similarly, information available to the process operator about the current operational status of the process control loops and other routines might be helpful to the maintenance person. And information generated by or used in the course of performing business functions could be helpful to the maintenance crew or the process control operator.

However, in the past, because these functions were separated, the information generated or collected in one functional area was not used at all or not used very well in other functional areas, leading to an overall suboptimal use of the assets in process plants (Galar et al., 2012a).

14.4.9.5 Nonlinear Control

Conventional process control systems utilize linear dynamic models. For highly nonlinear systems, control techniques directly based on nonlinear models provide significantly improved performance.

Most real processes display some nonlinear behavior. The process gain and dead time can change with load, time with equipment degradation, and dead time with transportation lag. In many cases, linear controllers provide adequate control performance. As the degree of nonlinearity increases, then improved control performance may be necessary and desired.

There are two classes of nonlinear control: discontinuous and continuous. The discontinuous methods include on-off and three state devices. These discontinuous methods are adequate only when accurate regulation is not essential. Continuous nonlinear control methods include fuzzy logic, output filtering, characterization, and dead-band or gap action.

3.3.6.5 Nonlinear Control

Conventional process control systems utilize linear dynamic models. For highly nonlinear systems, control techniques directly based on nonlinear models provide significantly improved performance.

Most real processes display some nonlinear behavior (Seborg, 2011). The process gain and dead time can change with load, time with equipment degradation, and dead time with transportation lag. In many cases, linear controllers provide adequate control performance. As the degree of nonlinearity increases, improved control performance may be necessary and desired.

There are two classes of nonlinear control: discontinuous and continuous. The discontinuous methods include on–off and three state devices. These discontinuous methods are adequate only when accurate regulation is not essential. Continuous nonlinear control methods include fuzzy logic, output filtering, characterization, and dead-band or gap action.

7.7.2 Programmable logic control systems

These process control systems incorporate very powerful control modules which can control the individual parameters and functions (also called axes) of the process according to a predetermined program sequence (Fig. 173). In electron beam welding machines one differentiates between the electrical parameters such as

and mechanical parameters such as

Movements in the X direction

Movements along the A axis, etc.

Programmable logical control systems are employed for the following operations:

In the operation of the welding machine:

Monitoring the mechanical, electrical and vacuum systems;

For warning of malfunctions and fault diagnosis;

For indicating servicing requirements.

For the workpiece:

To determine the position of the workpiece;

To control the movement of the workpiece.

During welding:

To enter the welding parameters (the set parameters);

To enter welding programs, where the parameters are controlled independently of one another;

To give commands to the mechanical, electrical and vacuum systems;

To determine the actual welding parameters (the measured parameters);

To compare the welding parameters set with those actually measured and to indicate differences;

To store and document the parameters;

To recall the welding parameters set for repeated use.

Visual display purposes:

To provide VDU process information in clear text and with diagrams;

To enable the operator to interact through dialogue to control the welding machine;

To indicate the operating condition of the machine, to enter commands and to receive an indication that these have been carried out.

These possible methods of control have had an important effect on the overall economic viability of the electron beam welding process.

Examples of this are:

Automatic control of the vacuum system

Pressure dependent switching of the valves and pumps can be controlled, see section 8.6.5, taking into account minimum evacuation times for the working chamber and if necessary for the electron beam gun after the cathode has been changed, and preventing incorrect operation. The state of the vacuum system together with information regarding the number of hours to the next service, oil change, etc, is also indicated in clear and easy to understand diagrams.

Optimisation of cathode heating

After the cathode has been replaced it is heated automatically, see section 7.5, and the heating current applied during this process optimised. In addition to a series of electron optical advantages, this also helps to achieve the maximum service life from the cathode.

Faster determination of the welding parameters to be set

Previously, the optimum beam current Is needed to be determined by trial welds, with a separate weld being carried out for each different setting. If the welds were carried out next to one another, the results obtained were of real practical use only if the workpiece were allowed to cool after each weld run.

A much shorter and more efficient way, however, is to make a single trial weld with a successively increasing beam current.

Control of the welding parameters set

The actual parameters are measured during the welding process and compared with the set values:

If the limits set for the values of any of these parameters for the particular welding operation are exceeded, an optical or acoustic warning signal is given.

Welding of components

The parameters used for welding a particular component can be stored under a particular identification code and can be called up and reentered into the machine control system. In this way, welding of series of components can also be repeated in any desired sequence.

As a practical example, Fig. 174 shows part of an airbag system designed to protect the head and upper body of a driver in a collision [137]. Five joints have to be welded with the gas charge already in the casing, having fusion zone depths of between 2.5 and 4 mm. The welding parameters set for the respective joints, and in particular the beam current Is, the lens current IL, the welding speed Vs and the diameter of the circumferential seams are stored in the control system and are called up automatically according to the program and monitored during the welding process to ensure that the very close tolerance limits set are not exceeded.

PLC systems with their numerous applications have now become a central component in modern electron beam welding machines. They are able to process both analogue and digital values and can control axes independently of one another using various different modules. This type of control is, however, independent of both the distance welded and of time.

Process Control Systems

The process control system should be viewed as the first level of the safety protection for the installation, as its function is to control the process within the design limits of the process plant and make the operator aware of any process excursion beyond these limits. It also performs the main data acquisition and display function, collecting information from all areas of the installation and displaying it in a central location in an easily comprehensible manner.

Modern process control systems have the capability of adjusting the individual control loops automatically to optimise loop performance leading to a safer and more efficient operation. ‘Predict & Control’ (P&C) provides multivariable model predictive control to ensure that process performance remains optimal and repeatable in all operating conditions and across all product changes. It uses state-of-the-art state space modelling to reduce process variations, increase throughput and reduce production costs by operating safely at the closest possible process constraint limits. P&C has an unrivalled success rate in improving productivity in a wide variety of industrial processes (ABB Advanced Process Control). They can also analyse the data coming back from the plant instruments to automatically monitor plant operation and schedule preventative maintenance activities thus enhancing operational efficiency and therefore safety by reducing the probability of breakdowns.

The information generated by the control system is presented to the operations personnel at a central location on the installation in a readily understandable way, using multiple graphical screens. As always the information presented by intelligent systems is only as good as the data available for processing, and to maximise the benefit of these control systems, intelligent instruments must be used. ‘FOUNDATION fieldbus (Ff) devices deliver predictive alerts, millisecond data capture, validated data, field-based control, diagnostics, and asset information bi-directionally with the DeltaV system. DeltaV Ff I/O communicates digitally with field devices, increases your input/output capacity, and provides access to more information about your process than conventional I/O subsystems. DeltaV Ff I/O enhances device diagnostics that affect the control strategy and alert operators to device malfunctions’ (Emerson Process Management DeltaV system overview). These instruments have the ability to self-diagnose faults and report back to the control system the nature of the fault and the time of occurrence. The facility to time-stamp in real time at the instant an alarm level being reached is invaluable in tracing the cause of any process upset and is a basic requirement of an effective alarm management system. Alarm management systems are necessary to prevent operator information overload and assist in the detection of the cause of the alarm event to ensure safe and speedy rectification of the problem. The ability to store real-time process information and display in detail the chronological sequence of events leading up to an alarm after the event is also helpful in analysing the root cause of the problem.

Information on plant status can also be made available at a remote location such as the shore support base, engineering centre or company management offices. Details such as real-time process operations, historical data, and operational efficiency can be made available to the relevant departments to assist with problem solving, maintenance planning and resource availability without having to visit the installation. This reduces the number of personnel required on the installation, thus increasing safety by reducing the risk to personnel and reduces the time taken to identify and rectify problems, further reducing risk and maximising the availability of the plant. ‘Honeywell’s Experion ® Process Knowledge System (PKS) with unique distributed system architecture and digital video management enables real-time remote monitoring, maintenance and operation of offshore facilities from a centralized onshore control centre. With this solution, you can leverage expertise across remote sites, make faster and more effective decisions, and achieve greater productivity. Additionally, you can reduce helicopter flights, ship movements and supply of material to your offshore platform, and above all, improve safety in hazardous environments by removing personnel from remote locations’ (Honeywell Offshore Oil and Gas Solutions).

These sophisticated process control systems are available from several sources; those that are most commonly found in North Sea oil and gas installations are ABB’s Advanced Process Control System, Emerson Process Management’s DeltaV Digital Automation System and Honeywell’s Experion Process Knowledge System. All systems offer similar performance and expansion capabilities; all systems can also offer an integrated process shutdown capability with fire and gas detection as part of the package.

13.15.1 Guidelines for Safe Automation of Chemical Processes

The safety aspects of process control systems are the subject of Guidelines for Safe Automation of Chemical Processes (CCPS, 1993/14) (the CCPS Safe Automation Guidelines). The Safe Automation Guidelines cover the safety aspects of the whole process control system, including the basic process control system (BPCS), the safety interlock system (SIS) and the human operator. Two types of interlock are distinguished: (1) failure interlocks and (2) permissive interlocks. The distinction corresponds to that used here between trips and interlocks proper.

The headings of the Guidelines are: (1) overview; (2) the place of automation in chemical plant safety – a design philosophy; (3) techniques for evaluating integrity of process control systems; (4) safety considerations in the selection and design of BPCSs; (5) safety considerations in the selection and design of SISs; (6) administrative controls to ensure control system integrity; (7) an example involving a batch polymerization reactor and (8) the path forward. Appendices deal with SIS technologies, separation of the BPCS and SIS, watchdog timer circuits, communications, sensor fail-safe considerations, SIS equipment selection, PES failure modes and factory acceptance test guidelines.

The Guidelines are concerned particularly with PES-based SISs. As described earlier, at least until recently, the normal approach has been to use for the safety interlock (SI) a hardwired system separate from the rest of the control system, whether or not this be computer based. The Guidelines describe a design philosophy in which the system of choice for an SIS is a PES-based system. In large part the guidance is concerned with ensuring that a PES-based system has the availability and reliability required for this duty.

This section gives an outline of the Guidelines. The latter contain a wealth of practical guidance on the various topics which are touched on here.