Advanced process control: Difference between revisions

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{{Short description|Concept in control theory}}

In [[control theory]] '''Advanced process control''' (APC) is a broad term composed of different kinds of [[process control]] tools, often used for solving multivariable control problems or discrete control problem. Advanced control describes a practice which draws elements from many disciplines ranging from control engineering, signal processing, statistics, decision theory and artificial intelligence bla bla bla

{{Refimprove|date=January 2017}}

In [[control theory]], '''advanced process control''' (APC) refers to a broad range of techniques and technologies implemented within industrial process control systems. Advanced process controls are usually deployed optionally and in addition to ''basic'' process controls. Basic process controls are designed and built with the process itself to facilitate basic operation, control and automation requirements. Advanced process controls are typically added subsequently, often over the course of many years, to address particular performance or economic improvement opportunities in the process.

== Overview ==

Advanced process control is composed of different kinds of [[process control]] tools, for example:

* [[Model predictive control]] (MPC),

* [[Statistical process control]] (SPC),

* Run2Run (R2R),

* Fault detection and classification (FDC),

* [[sensor|Sensor control]] and

* [[feedback|Feedback systems]].

APC applications are often used for solving multivariable control or discrete control problems.

[[Process control]] (basic and advanced) normally implies the process industries, which include chemicals, petrochemicals, oil and mineral refining, food processing, pharmaceuticals, power generation, etc. These industries are characterized by continuous processes and fluid processing, as opposed to discrete parts manufacturing, such as automobile and electronics manufacturing. The term [[process automation]] is essentially synonymous with process control.

Normally an APC system is connected to a [[distributed control system]] (DCS). The APC application will calculate moves that are sent to regulatory controllers. Historically the interfaces between DCS and APC systems were dedicated software interfaces. Nowadays the communication protocol between these system is managed via the industry standard [[OLE for process control]] (OPC) protocol.

Process controls (basic as well as advanced) are implemented within the process control system, which may mean a [[Distributed control system|distributed control system (DCS)]], [[Programmable logic controller|programmable logic controller (PLC)]], and/or a supervisory control computer. DCSs and PLCs are typically industrially hardened and fault-tolerant. Supervisory control computers are often not hardened or fault-tolerant, but they bring a higher level of computational capability to the control system, to host valuable, but not critical, advanced control applications. Advanced controls may reside in either the DCS or the supervisory computer, depending on the application. Basic controls reside in the DCS and its subsystems, including PLCs.

== Advanced process control: Topics ==

=== APC industries ===

*APC can be found in the (petro)chemical industries where it makes it possible to control multivariable control problems. Since these controllers contain the dynamic relationships between variables it can predict in the future how variables will behave. Based on these predictions, actions can be taken now to maintain variables within their limits. APC is used when the models can be estimated and do not vary too much.

[[ja:== Types of Advanced Process Control]] ==

*In the complex [[semiconductor industry]] where several hundred steps with multiple re-entrant possibilities occurs, APC plays an important role for control the overall production.

Following is a list of well-known types of advanced process control:

APC is more and more used in other industries. In the mining industry for example, successful applications of APC (often combine to Fuzzy Logic) have been successfully implemented. In the mining industry, the models change and APC implementation is more complex.

* Advanced regulatory control (ARC) refers to several proven advanced control techniques, such as override or adaptive gain (but in all cases, "regulating or feedback"). ARC is also a catch-all term used to refer to any customized or non-simple technique that does not fall into any other category. ARCs are typically implemented using function blocks or custom programming capabilities at the DCS level. In some cases, ARCs reside at the supervisory control computer level.

=== APC Engineers ===

* Advanced process control (APC) refers to several proven advanced control techniques, such as feedforward, decoupling, and inferential control. APC can also include Model Predictive Control, described below. APC is typically implemented using function blocks or custom programming capabilities at the DCS level. In some cases, APC resides at the supervisory control computer level.

Those responsible for the design, implementation and maintenance of APC applications are often referred to as APC Engineers or Control Application Engineers. Usually their education is dependent upon the field of specialization. For example, in the chemical industry the vast majority of APC Engineers have a chemical engineering background and typically hold a graduate degree. They combine deep understanding of advanced control techniques with expert process or product knowledge to provide solutions to the most difficult control problems. Because APC engineers are highly specialized many companies elect to contract engineering firms for this type of work. However, some companies view APC as a competitive advantage and maintain a staff of APC engineers who often provide services at more than one geographic ___location.

* Multivariable [[model predictive control]] (MPC) is a popular technology, usually deployed on a supervisory control computer, that identifies important independent and dependent process variables and the dynamic relationships (models) between them and often uses matrix-math based control and optimization algorithms to control multiple variables simultaneously. One requirement of MPC is that the models must be linear across the operating range of the controller. MPC has been a prominent part of APC since supervisory computers first brought the necessary computational capabilities to control systems in the 1980s.

* Nonlinear MPC is similar to multivariable MPC in that it incorporates dynamic models and matrix-math based control; however, it does not require model linearity. Nonlinear MPC can accommodate processes with models with varying process gains and dynamics (i.e., dead times and lag times).

=== Terminology ===

* Inferential control: The concept behind inferential control is to calculate a stream property from readily available process measurements, such as temperature and pressure, that otherwise might be too costly or time-consuming to measure directly in real time. The accuracy of the inference can be periodically cross-checked with laboratory analysis. Inferential measurements can be utilized in place of actual online analyzers, whether for operator information, cascaded to base-layer process controllers, or multivariable controller CVs.

Manipulated Variables (MVs) are variables where advanced controllers send setpoints to. Controlled variables (CVs) are variables that normally need to be controlled between limits. Disturbance variables (DVs) or Feed Forward variables (FF) are only used as an input to the controller, they cannot be influenced, but when measured contribute to the predictability of the CV.

* Sequential control refers to discontinuous time- and event-based automation sequences that occur within continuous processes. These may be implemented as a collection of time and logic function blocks, a custom algorithm, or a formalized [[sequential function chart]] methodology.

* [[Intelligent control]] is a class of [[Control theory|control]] techniques that use various [[artificial intelligence]] computing approaches like [[Artificial neural network|neural networks]], [[Bayesian probability]], [[fuzzy logic]], [[machine learning]], [[evolutionary computation]], and [[Genetic algorithm|genetic algorithms]].

== Related Technologies ==

The following technologies are related to APC and, in some contexts, can be considered part of APC, but are generally separate technologies having their own (or in need of their own) Wiki articles.

*[[Statistical process control]] (SPC), despite its name, is much more common in discrete parts manufacturing and batch process control than in continuous process control. In SPC, “process” refers to the work and quality control process, rather than continuous process control.

*Batch process control (see ANSI/ISA-88) is employed in non-continuous batch processes, such as many pharmaceuticals, chemicals, and foods.

*Simulation-based optimization incorporates dynamic or steady-state computer-based process simulation models to determine more optimal operating targets in real-time, i.e., periodically, ranging from hourly to daily. This is sometimes considered a part of APC, but in practice, it is still an emerging technology and is more often part of MPO.

*Manufacturing planning and optimization (MPO) refers to ongoing business activity to arrive at optimal operating targets that are then implemented in the operating organization, either manually or, in some cases, automatically communicated to the process control system.

*[[Safety instrumented system]] refers to a system independent of the process control system, both physically and administratively, whose purpose is to assure the basic safety of the process.

== APC Business and Professionals ==

Those responsible for the design, implementation, and maintenance of APC applications are often referred to as APC Engineers or Control Application Engineers. Usually, their education is dependent upon the field of specialization. For example, in the process industries, many APC Engineers have a chemical engineering background, combining process control and chemical processing expertise.

Most large operating facilities, such as oil refineries, employ a number of control system specialists and professionals, ranging from field instrumentation, regulatory control system (DCS and PLC), advanced process control, and control system network and security. Depending on facility size and circumstances, these personnel may have responsibilities across multiple areas or be dedicated to each area. Many process control service companies can be hired for support and services in each area.

== Artificial Intelligence and Process Control ==

The use of artificial intelligence, machine learning, and deep learning techniques in process control is also considered an advanced process control approach in which intelligence is used to optimize operational parameters further.

For decades, operations and logic in process control systems in oil and gas have been based only on physics equations that dictate parameters along with operators’ interactions based on experience and operating manuals. Artificial intelligence and machine learning algorithms can look into the dynamic operational conditions, analyze them, and suggest optimized parameters that can either directly tune logic parameters or give suggestions to operators. Interventions by such intelligent models lead to optimization in cost, production, and safety.<ref>{{Cite news |date=2016-04-06 |title=Oil and Gas, AI, and the Promise of a Better Tomorrow |language=en-US |work=[[SparkCognition]] Inc. |url=https://www.sparkcognition.com/oil-gas-ai-promise-better-tomorrow/ }}</ref>

=== Terminology ===

*APC: Advanced process control, including feedforward, decoupling, inferential, and custom algorithms; usually implies DCS-based.

*ARC: Advanced regulatory control, including adaptive gain, override, logic, fuzzy logic, sequence control, device control, and custom algorithms; usually implies DCS-based.

*Base-Layer: Includes DCS, SIS, field devices, and other DCS subsystems, such as analyzers, equipment health systems, and PLCs.

*BPCS: Basic process control system (see "base-layer")

*DCS: Distributed control system, often synonymous with BPCS

*MPO: Manufacturing planning and optimization

*MPC: Multivariable [[Modelmodel predictive control]] (MPC),

*SIS: [[Safety instrumented system]]

*SME: Subject matter expert

== References ==

{{Reflist}}

{{Unreferenced|date=September 2007}}

== External links ==

* [https://web.archive.org/web/20060421092145/http://lorien.ncl.ac.uk/ming/advcontrl/apc.htm Article] about Advanced Process Control.

* [http://hpsweb.honeywell.com/Cultures/en-US/NewsEvents/NewsRoom/PressReleases/PasAcquisition_PR_013007.htm Press Release]. Honeywell Acquires PAS Advanced Process Controls Business

* [http://viewer.zmags.co.uk/publication/c87cd4eb?page=48#/c87cd4eb/48/ Case Study]. Lancaster Waste Water Treatment Works, optimisation by means of Advanced Process Control from Perceptive Engineering

* [http://APC-network.com APC-network.com] Advanced Process Control network for Refining and petrochemical Industry.

[[Category:Control theory]]

[[Category:Cybernetics]]

[[Category:Digital signal processing]]

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[[ja:Advanced Process Control]]