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Desktop only

To use the writing tool please open Ingenuity on a desktop device

What are you writing about?

Need some help to get started? Read our writing tips.

What kind of article do you want to write?

Case study template

Use this template to write case studies and other long-form articles. Case studies present a challenge one of our customers faced and explains how we solved it. It consists of:

  • High-level summary
  • Background
  • Challenges
  • Findings
  • Reccomendations
  • Implementation

Use this template for: case studies, profile posts, future thinking, 'tools of the trade' or guest posts

List template

Show information in a concise, simple and structured way. Whether you’re writing "Top 10" articles or collecting content from across the web. Introduce your topic and let the readers explore it further themselves. This template consists of:

  • Introduction
  • List section

Use this template for: top 10, A-Z, checklists or similar list-based articles, curated lists of hyperlinks

Interview template

Present a personal conversation in a simple question-answer format. This template consists of:

  • Introduction
  • Questions
  • Answers

Use this template for: expert interviews, how-to guides.

Some writing tips

  • Choose a topic that truly fascinates you. Your enthusiasm will come through in your writing.

  • Write from your own point of view and use your own voice.

  • Challenge the status quo. In order to write about groundbreaking developments, you sometimes have to take a controversial stance.

  • Write in a clear, concise and comprehensible way. Write short sentences, use simple words and build a logical narrative.

  • Encourage discussion. Invite colleagues to contribute and readers to discuss your article.

  • Always check your grammar and make sure that your facts are correct before you hit “publish”.

Process Automation
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So, this Digital Twin (DT) is really getting a lot of attention in the digitalization age. Recently, I’ve seen numerous articles, white papers and presentations on the topic that cover various aspects of the technology. And with the move towards the Industrial Internet of Things (IIoT) and digitalization, the focus is certainly warranted as there are many benefits of employing the DT in a process environment. It is being deployed in all phases of a product, plant or process lifecycle from design to operations and maintenance.

What is the Digital Twin?

A DT is essentially nothing more than a virtual representation of a physical product or process. The DT is used to better understand and predict the performance characteristics of a finished product throughout the lifecycle. Use cases include simulation, prediction and optimization of the product as well as the entire production system before investing in any physical prototypes. And that can result in quite the cost-savings as the days of correcting issues in the commissioning phase are over if the DT is employed properly.  

The concept of the DT is not new. For more than 30 years, product and process engineering teams have used 3D renderings and process simulation to validate manufacturability. What is new, however, is that several factors have now converged to bring the concept of the DT to the forefront as a disruptive trend in the process industry. The DT is not a single technology but in reality, a combination of technologies including one single database for all the plant or product engineering data, simulation software, real-time data from the production environment and much more. With our ability today to easily access data from many sources, aggregate it into a dashboard style environment and to add contextual information into the mix, the DT, however, has become more powerful today than ever.

What are the benefits of the Digital Twin?

The benefits of the DT are numerous. First and foremost, by incorporating additional disciplines such as data analytics and machine learning, the DT is able to demonstrate impacts of usage and training scenarios in a virtual setting. This enables identification of potential issues in the design phase as opposed to during commissioning, which is very common today, and with that costly changes are avoided because changes only need to be made to the virtual twin. Secondly, process and sensor data from physical objects is collected and analyzed to determine real-time performance and operating changes over time. Feeding this data back into the product lifecycle, the DT is continuously updated to reflect changes to the physical counterpart. This creates a closed-loop of virtual feedback that makes products, production, and performance optimization possible at minimal cost. Lastly, the engineering project in its entirety can be transferred to the Process Logic Controller (PLC), Distributed Control System (DCS) or SCADA for more efficient programming.  

Do you have experience employing the Digital Twin in a process environment having gained some of the benefits listed here? 

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