Courtesy: Maverick Technologies
PID, APC April 1, 2021

Know where to start with control loop tuning

Understanding proportional gain and integral time’s functions in control loop tuning values and how they work together is important.

By Rocky Chambers
The image at left shows the crystal structure of a MoTe2|PtS2 heterobilayer with isocharge plots from a model created at Rice University. When the materials are stacked together, mirror symmetry is broken and there is a charge transfer that creates an intrinsic electric field. This field is responsible for Rashba-type spin-splitting shown by the band structure at right, where the spin is perpendicular to momentum. Courtesy: Sunny Gupta, Rice University
PID, APC March 1, 2021

Theory accelerates push for spintronic devices

Rice University models help ID materials for advanced electronics, computer memories for spintronics.

By Mike Williams
Courtesy: Honeywell Process Solutions
Process Manufacturing February 28, 2021

Future-proofing process control systems with Lean project execution

Control engineers can future-proof their process control systems (PCSs) with Lean project management principles, which gives companies the flexibility needed to meet ever faster changing customer demands.

By Joe Bastone
Courtesy: Avanceon
PID, APC February 26, 2021

Understanding PID tuning

Proportional-integral-derivative (PID) tuning can be challenging to learn, but the experience gained can serve engineers well in other areas. See six things to do when a PID loop underperforms.

By Brian Fenn
Image courtesy: Wood
AI and Machine Learning February 17, 2021

Evolution of control systems with artificial intelligence

Cover Story: Can artificial intelligence (AI) prove to be the next evolution of control systems? See three AI controller characteristics and three applications.

By Kence Anderson, Winston Jenks and Prabu Parthasarathy
Courtesy: Avanceon
AI and Machine Learning February 16, 2021

Manufacturing analytics and machine learning benefits

Analytics and machine learning (ML) are the norm in manufacturing and can help users get better, more actionable data. Two examples are highlighted.

By Matt Ruth
Symphony AzimaAI Performance 360, shown here in a dry mixing application, also was applied to optimize operation of a mining grinding. SAAI’s AI-driven digital twin models offer dynamic process optimization that can predict process uncertainties and recommend optimized control strategies to increase throughput and reduce the chance of failures. It received 2021 Engineers’ Choice recognition in the Grand Award category, winning most overall votes of among all product finalists for 2021. Courtesy: Symphony AzimaAI.
AI and Machine Learning February 10, 2021

Artificial intelligence applied to mill optimization

Variability controlled with artificial intelligence (AI) software. A digital twin-based AI optimizer was deployed to run alongside advanced process controllers (APCs). Results include a 1% increase in mill production throughput with annual topline impact of $3-4 million for the mine. See 4 objectives for recurrent neural networks and self-adaptive tuning.

By Dominic Gallello
Courtesy: Industrial Internet Consortium
IIoT, Industrie 4.0 February 5, 2021

IoT-enabled process validation system for COVID-19 vaccine rollout

An Industrial Internet of Things (IIoT)-enabled global process validation system with advanced process control (APC) capabilities can accelerate manufacturing efficiency, production capacity and reduce production cycle time for the COVID-19 vaccine rollout.

By Ramya Mopidevi
Courtesy: Tesco Controls
PLCs, PACs December 12, 2020

Migrating legacy PLC programs to modern PLC hardware

Zero downtime is a requirement for many water/wastewater processes, often necessitating upgrading legacy automation by migrating existing code

By Raju Nair
Courtesy: Opto 22
Embedded Systems, Edge Computing December 3, 2020

AMP upgrades to edge controllers

Largest U.S. rotary heat-treating facility modernizes controls and automation with tight database integration.

By Josh Eastburn