Advanced process control: Difference between revisions

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{{Short description|Concept in control theory}}

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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 Intelligenceintelligence and Machinemachine Learninglearning 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 leadslead 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, inferentialsinferential, 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]]

*SIS: [[Safety instrumented system]]

*SME: Subject matter expert