Digital Twin Technology & Chemical Process R&D

Let’s take a closer look at a technology that is changing the landscape of chemical RD: digital twin technology. This is no fad tech. It’s a true game-changer providing an entirely new way of developing, simulating and optimising chemical processes. As we take a closer look, it becomes clear why this technology is becoming indispensable in the field of chemical RD.

What is a Digital Twin?

A digital twin is a virtual model that represents a physical object or process with great detail. In chemical manufacturing, a digital twin is a detailed simulation of a chemical process that includes all of the physical and chemical properties of the process, operational data and dynamics. This virtual model is continuously updated with data from the physical system, ultimately enabling it to simulate real-world operating conditions in stunning detail.

The Technical Backbone

The technology relies on IoT sensors, advanced data analytics and machine learning algorithms. IoT sensors monitor the physical system in real-time, measuring important parameters such as temperatures, pressures, flow rates and chemical compositions. The data is then fed into the digital twin, which contains a model of the process and uses machine learning to assess how it will perform when faced with certain conditions.

Enhancing R&D with Digital Twins

Using digital twin technology for chemical process research and development holds significant value. Here is a more detailed perspective on how digital twins are being used to innovate the field of chemical manufacturing.

Rapid Prototyping and Simulation

Digital twins give researchers the ability to experiment and test hypotheses in the virtual world before implementing them in the physical world – a process that greatly speeds up R&D. It also significantly reduces the cost and amount of time that is consumed by trial and error in the physical world. Researchers can quickly simulate process changes and reactant compositions to determine outcomes before ever actually experimenting with them.

Optimisation of Processes

Perhaps one of the most important benefits of digital twins in the chemical context is their use for optimising processes, such as discovering the cheapest pathways and conditions for carrying out a chemical reaction. This includes optimising energy usage, as well as usage of raw materials and minimising waste, which are important aspects of low-carbon chemical manufacturing.

Predictive Maintenance

Digital twins also fit an important role in predictive maintenance for chemical plants. By continuously tracking the state and performance of equipment, the digital twin can predict when preventive maintenance should be performed to forestall unexpected downtime and production losses. In this way, the predictive nature of the digital twin minimises unnecessary maintenance downtime and extends the useful life of equipment.

Scale-Up and Commercialisation

There is always a risk that scaling up a chemical process from the lab to industrial scale will introduce fundamentally different issues to the process that might disrupt its smooth operation. For this reason, digital twins can help to simulate large scale operations using data from the lab, which can help predict problems and bottlenecks before they occur in the actual plant. The application is particularly valuable in preventing issues that would have required the process to be scaled down again, or the starting approach to be completely redone.

This means a huge boost to accuracy of digital twins, another huge reduction in development costs, a further shorter time to market and greater commercial efficiency of chemicals manufacturing overall. This role of digital twin technology in chemical RD will continue to increase as the technology develops and is combined with other advanced digital tools. The reality for the chemical sector is that digital twins are not an option – they are a necessity for those who want to remain at the leading edge of innovation and remain competitive.

Beyond Simulation: Integrating Digital Twins with Emerging Technologies

Alongside this, the ongoing trend in chemical process R&D to apply digital twins will no doubt be enhanced through synergies with other emerging technologies, further extending the benefits of digital twins and creating new opportunities for innovation in chemical manufacturing.

Integration with Augmented Reality (AR) and Virtual Reality (VR)

Perhaps the most anticipated of future integrations is with Augmented Reality (AR) and Virtual Reality (VR). Here, the interactive nature of digital twins can be combined with AR and VR technologies to make the process simulations even more intuitive and immersive. For instance, it might be possible for a technician wearing VR headset to ‘walk through’ a digital twin of something like a chemical plant, and find its inherent flaws, or try out potential changes in a completely safe and virtualised environment. AR too can overlay process data from the digital twin on to real-world processes, giving operators detailed information and insights in real time.

Enhanced Decision Making with AI and Machine Learning

Combined with sophisticated AI and machine-learning algorithms, the digital twin becomes even more sophisticated. With access to data from the digital twin, AI can search for patterns, predict outcomes and suggest optimisations that might escape the human operator. For example, it could recommend how to vary reaction parameters based on historical data and predictive analytics to deliver a higher yield, or better quality. Suddenly a simple simulation model becomes a system of simulation – a ‘digital twin’ that learns.

IoT and Blockchain for Improved Security and Data Integrity

An added dimension can be provided by linking digital twins to the Internet of Things (IoT) and blockchain to ensure security and data integrity. Live data feeds from IoT devices can keep the digital twin automatically updated in real time, and blockchain can protect that data from being tampered with, so the digital twin remains a live, trustworthy copy of the physical process. These attributes are especially useful in processes where sensitive proprietary data and process security are required.

Collaborative Opportunities Across the Globe

Digital twins also promote better collaboration across geographic and organisational distances. Cloud-based digital twins allow teams in different locations around the world to connect and work on the same model simultaneously – for example, to improve processes, solve problems or innovate. This capability can help multisite global companies optimise operations and, alternatively, can help academic-industry collaborations involving multidisciplinary teams.

Future Prospects and Challenges

Looking ahead, the potential of digital twins in chemical process RND is vast, but realisation will face challenges. For one, the quality of the digital twin is highly correlated with the quality and quantity of input data, so mechanisms for collecting robust data are key. Further challenges boil down to standardising digital twin technology and integrating digital twins with legacy systems installed in existing chemical plants.

These challenges can be viewed as opportunities for innovation and improvement as the technology matures. The future of digital twins, as they continue to evolve, is one that will keep pushing the envelope of what’s possible with chemical manufacturing, opening the doors to more efficient, more sustainable, and more innovative processes.