PID, APC

Figure 2: Real-time dashboards provide the information required for each level of the plant’s organization. Courtesy: Yokogawa Electric Corp.
PID, APC November 4, 2019

Profit-driven operations require IT/OT integration

Maximizing process plant profits requires close integration across all levels of an organization.

By Yasunori Kobayashi
A study from Flow Research projects the worldwide flowmeter market will grow to almost $9 billion by 2023. Courtesy: Flow Research Inc.
PID, APC November 2, 2019

Flowmeter market growth expected thanks to oil and gas industry recovery

A study from Flow Research projects the worldwide flowmeter market will grow from $7 billion to almost $9 billion by 2023 as the oil and gas industry continues recovering. Flow measurement technologies advance.

By Mark T. Hoske and Chris Vavra
Factory automation and process control tag-naming matters for consistency, understanding, and troubleshooting. Courtesy: Frank Lamb, Automation Consulting, Automation Primer
PID, APC November 1, 2019

Control Engineering hot topics, October 2019

Control Engineering's most clicked articles in October 2019 included stories about the Engineers' Choice finalists, PLC naming conventions, digital engineering practices, RPC and model-based control, and more. Miss something? You can catch up here.

By Chris Vavra
Better control means getting useful information when needed to the right people, for smarter decisions. Intuitive dashboards showing plant parameters provide faster understanding, as shown at the 2019 Honeywell Users Group Americas meeting in June. Courtesy: Mark T. Hoske, Control Engineering, CFE Media and Technology
PID, APC October 31, 2019

Opening your options: Control system migration

Inside Process: Aging process control systems (PCSs) create nine major problems. There are three good reasons to migrate distributed control systems before obsolescence.

By Satnam Bhogal
Figure: Rate-predictive control (RPC) is inherently adaptive (think naturally self-tuning) to changes in process gain. In the top graph, process gain is 1.0, while in the bottom graph, process gain is 2.0. Control performance remains “perfect,” with no change to RPC tuning parameters. Courtesy: APC Performance LLC
PID, APC October 7, 2019

Top 5 Control Engineering articles September 30 to October 6

Articles about RPC and model-based control, water/wastewater applications, OOIP, advanced process control, and prolonging power plant life were Control Engineering’s five most clicked articles from September 30 to October 6. Miss something? You can catch up here.

By Chris Vavra
Courtesy: CFE Media
PID, APC September 18, 2019

Five advanced process control, data analytics connections

Are there connections between advanced process control (APC) and data analytics? Recent developments in the field of information technology (IT) and its use in the manufacturing world provide insights.

By Jim Ford, Ph.D.
Figure 1: In OOIP, the control design is built from objects which match those in the physical plant or equipment design. Courtesy: ControlSphere Engineering
PID, APC September 18, 2019

OOIP Part 3: interfaces and methods

Interfaces and methods used in object-oriented industrial programming (OOIP) help deliver productivity of object-oriented programming (OOP) without the complexity. Because plants and equipment are assembled from objects, it’s logical that their control programming should be, too.

By Gary L. Pratt, P.E.
Figure: Rate-predictive control (RPC) is inherently adaptive (think naturally self-tuning) to changes in process gain. In the top graph, process gain is 1.0, while in the bottom graph, process gain is 2.0. Control performance remains “perfect,” with no change to RPC tuning parameters. Courtesy: APC Performance LLC
PID, APC September 9, 2019

Advantages of RPC and limits of model-based control

Part 3: Rate-predictive control, an alternative algorithm to proportional-integral-derivative (PID) and model-based control, can provide single-loop control. See three RPC advantages and two model-based control limitations.

By Allan Kern
Figure 3: Here the block diagram from figure 2 has been rearranged into a more familiar configuration that is functionally identical to a traditional feedback controller (blue) equipped with a setpoint filter (green) and a feedforward path (red). Most commercial feedback controllers include setpoint filtering and feedforward as standard options, making this version of model-following control simpler than the original version, and much easier to implement. Source: Control Engineering
PID, APC September 9, 2019

Top 5 Control Engineering articles September 2-8

Articles about model-following control, industrial controller cybersecurity, machine learning, improving engineer retention, and IIoT platforms were Control Engineering’s five most clicked articles from September 2-8. Miss something? You can catch up here.

By Chris Vavra
Figure 3: Here the block diagram from figure 2 has been rearranged into a more familiar configuration that is functionally identical to a traditional feedback controller (blue) equipped with a setpoint filter (green) and a feedforward path (red). Most commercial feedback controllers include setpoint filtering and feedforward as standard options, making this version of model-following control simpler than the original version, and much easier to implement. Source: Control Engineering
PID, APC August 30, 2019

The basics of model-following control

Two proportional-integral-derivative (PID) controllers can be better than one. Model-following control may be the best choice if it's important to get the process variable to the setpoint without hunting or overshoot.

By Control Engineering