Yokogawa Industry Blog

chemical and pharmaceutical

A city within a state? The process plant

The people of ancient Greece worshipped the goddess Automatia (“the one who comes by herself”). In addition, the Greeks at that time created city states (polis) with the claim of “autonomia”, to live as an independent unit. Today, on the journey from industrial automation to industrial autonomy, we are using an understanding that people already had in ancient times. However, the question remains: will the process plant of today develop into an autonomous polis?

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Autonomy through perseverance

Artificial intelligence, machine learning, information retrieval or even data mining – these are all buzzwords that come up in connection with autonomous systems within the process industry. Are autonomous systems the future in the process industry and thus practically the next step beyond automation? I already wrote in my previous post that I see in precisely this circumstance the beginning of a change in automation that is moving towards industrial autonomous systems. But what actually makes us so confident that this transformation will actually take place?

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Make your digital transformation in the process industry

Digital transformation, so-called Industry 4.0, has been talked about for ten years now. What began at that time as a future project for comprehensive digitalisation is now taking place as a transformation throughout society and is also establishing itself in industry. The past twelve months in particular have given another enormous push in this direction. For example, the pandemic has accelerated the digital communication strategy of companies by six years on average worldwide. Digital transformation offers opportunities in all areas of business to adapt and evolve in the world of rapidly changing markets and technologies. The continuous development of digital technologies opens up new opportunities in research and development, but also in the areas of engineering, marketing, sales and services, all the way to logistics and value chains. Join us here on this blog with more topics on digital transformation in the process industry.

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Risk Assessment: First Step to Securing an OT Environment

Imagine this scenario: A chemical processing company decides to launch a cybersecurity program for a manufacturing plant, so it brings together an IT expert and someone from operations who is moderately well versed in the plant networks. The two have other responsibilities and complete their task quickly by inserting a smattering of security appliances at strategic points and declare the plant protected.
Meanwhile, a hacker who has been systematically analyzing the plant’s networks over several months, has a better grasp of the architecture and what assets are deployed than anyone in the facility. The hacker gained access because some system integrator, a few years ago, installed a Brand X PLC to solve a chemical injection problem. The technician left a path to access the PLC via the internet for follow-up service, but everyone has forgotten about it. The hacker is aware of a key vulnerability—a default password—with that PLC because its characteristics were published, but the plant never acted on changing it because they had forgotten it is even there. This vulnerability provides the hacker a path into the larger network through an unprotected connection from the PLC to the automation system.
While this scenario is an oversimplification, it illustrates the problems many companies face as they consider how to approach cybersecurity strategy for operations technology (OT) networks. A well-thought-out strategy will find and correct these problems, and for the rest of this article, we will look at how to implement this type of plan.

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A simple experiment on Predictive Maintenance and IA2IA

It is not easy to find your way in the sea of technological solutions related to predictive maintenance. As Product Marketing Manager, it is my job to understand and test the real potential of the tools I am dealing with, in this case Yokogawa’s wireless sensors, called “Sushi Sensors”, which monitor parameters such as vibration, pressure and temperature. Read the blog article with the title “A simple experiment on Predictive Maintenance and IA2IA” here.

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Real-time, Model Based Digital Twin for Energy and Emissions Management

The Visual MESATM energy management applications suite is configured to work autonomously, gathering, and processing the necessary forecasts of the external variables—such as fuel/power price projections and weather forecasts -, executing calculations, historizing key performance indicators, producing and distributing the resulting reports. These can run as open loop or closed loop actions, depending on whether targets generated from the schedule are to be automatically passed from the scheduler to the optimizer and, from there, to be manually implemented by the Operators or flowing down to the Advanced Process Control (APC) or basic control layer.
Consistency among the decisions systems at different time scales, optimizing in real time but accounting for the constraints imposed by the optimal schedule is guaranteed. Consequently, overall cost and GHGs emissions can be substantially reduced with the help of a real-time, model based Digital Twin approach.

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