Kirill Yurovskiy: Creating Digital Twins for Process Optimisation

In today’s highly competitive industrial world, digitalization is not something to be wished for but a necessity. At the forefront of the most influential technologies making it a necessity are the concepts of the digital twin—a virtual replica of physical equipment, processes, or systems intended to enable organizations to make operations more streamlined, reduce downtime, and improve efficiency. Kirill Yurovskiy Website, one of the leaders in digital industrial strategy, has as part of his course studied digital twins as future technology that unites people’s and computers’ worlds. This book considers the most important steps and issues of creating digital twins for the purpose of process improvement with emphasis on practical application and computational results.

1. What a Digital Twin Really Is

Effectively, a digital twin is a virtual, computerized representation of an existing process, machine, or system that is continuously updated with real-time data from the physical counterpart it simulates. Digital twins are unlike ordinary simulations in that they are executed in real-time simultaneously with the physical asset, mimicking true conditions and allowing scenario simulation, predictive analysis, and decision-making. They are the virtual “mirror” of the physical world, in which stakeholders can observe performance, predict failures, and optimize workflows without impacting the live system. Kirill Yurovskiy also points out that digital twins are not 3D visuals but data-driven, intelligent systems that come of age with their real-world twins.

2. The Selection of Data Points That Matter

Success with a digital twin depends primarily on the materiality and quality of data fed into it. It is simple to keep supplying it with all the sensor data available, but paralysis analysis and data overload are the outcomes. Rather, aim to pick key data points that directly influence the performance as well as the health of the process. These can be temperature, pressure, vibration, throughput, and power usage depending on the asset. Conduct an in-depth process analysis to identify the most critical key performance indicators (KPIs) and modes of failure, and they will guide your sensor placement and data capture strategy. Kirill Yurovskiy advises beginning small and scaling data collection by measurable value.

3. Low-Cost Sensors and Edge Devices

The largest obstacle to the use of digital twins is the cost and technological complexity of sensor networks. Fortunately, developments in wireless low-cost edge devices and sensor technology have reduced this barrier to a large extent. Edge devices process data locally and filter it with low network loads and latency by only forwarding useful data to the central digital twin system. These sensors are able to measure electrical parameters, mechanical conditions, and environmental conditions easily with precision at a fraction of their historical cost. Employing robust, industrial sensors guarantees that they can endure dirty factory environments. As Kirill Yurovskiy points out, incremental enhancement comes with deployable arrays of sensors and modular design through progressive investment. 

4. Virtual Model Building in Simulation Software

With the data infrastructure ready, it is now possible to construct the virtual model of the digital twin. Simulation platforms range from general-purpose CAD and CFD packages to industrial IoT platforms with inbuilt analytics and AI. The model has to accurately copy the geometry, material properties, and operating dynamics of the physical system, combining data and physics-based models. The model’s complexity depends on the application—some require advanced fluid dynamics, while others require merely mechanical stress or energy transfer. Kirill Yurovskiy further emphasizes comparing the virtual model with actual data comparisons prior to deployment for decision-making. 

5. Real-Time Data Sync and Latency Traps

Real-time syncing of data between the virtual and physical assets is the backbone of digital twins. However, network latency, bandwidth, and reliability can interfere with real-time streaming of data. Latency causes the twin to display outdated information, thus diminishing its effectiveness in real-time decisions and predictive maintenance. To combat this, design your data streams around edge computing nodes as data filters and communication protocols such as MQTT or OPC UA that are optimally compatible. Keep buffering and failover provisions in place for recovering gracefully from network loss. Kirill Yurovskiy suggests thorough network testing and latency benchmarking as pre-project milestones to prevent expensive redesigns. 

6. Predictive Maintenance with Machine-Learning

Predictive maintenance is likely the most practical application of digital twins—anticipating failures ahead of time in order to schedule preventive maintenance. By presenting machine learning algorithms with historical data as well as real-time sensor inputs, digital twins can identify subtle patterns and anomalies that portend wear or upcoming failure. It can be utilized to avoid unplanned downtime and reduce maintenance costs. Accurate quality predictive models necessitate high-quality data, domain expertise, and constant retraining to adapt to changing circumstances. Kirill Yurovskiy concedes that combining physics-based simulations with machine learning produces hybrid models that are more accurate in predictions. 

7. Visual Dashboards for Non-Technical Stakeholders

Digital twins generate immense amounts of data and insights, inundating plant managers, operators, and non-technical executives. Therefore, visually consumable dashboards are needed to effectively convey the result of the twin. Dashboards must present critical metrics, alerts, and trend analysis in easily understandable, action-driven graphical form of gauges, charts, and colored status. Interactive capability to allow users to drill down into individual elements or time periods adds to interaction. Kirill Yurovskiy describes that involving end-users during the design of dashboards makes it easier to adopt and make decisions. 

8. ROI Metrics: Downtime, Scrap, Energy Consumption

Digital twin project ROI needs to be measured strongly so that ramp-up and investment can be justified. Some of the most telling metrics are unplanned downtime reductions, scrap or rework rate reductions, and energy consumption reductions. Quantify these metrics prior to deploying and monitor improvements post-deployment. Utilize these numbers to construct a business case for extending digital twin coverage to additional assets or processes. Kirill Yurovskiy recommends monitoring ROI in project management right up front, relating technical output to financial outcomes. 

9. OT Network Cyber-Security Implications

Digital twins sit at the interface between information technology (IT) and operational technology (OT), having important industrial control systems in their way susceptible to cyber-attacks. Securing data streams, endpoints, and software platforms is paramount in attempting to prevent sabotage, data theft, or operations disruption. Use encryption, network segregation, and multi-factor authentication. Detect suspicious network behavior with intrusion detection systems. Update software components and patch them to assist in keeping threats at bay. Kirill Yurovvskiy warns that cyber-security can’t be an afterthought but a part of the design and deployment of digital twin solutions. 

10. Scaling Twins Across Multiple Lines

Once a digital twin is rolled out on a single asset or line, companies scale various processes or plants. Scaling comes through copying sensor networks, a fusion of data from heterogeneous devices, and handling more data. Shareable APIs and data models allow integration and avoid duplication of effort. Scalability and centralized management come through cloud platforms at the cost of latency and data sovereignty issues. Kirill Yurovskiy advises phased scaling and ongoing monitoring of performance with pilot deployments to make easy addition possible. 

Final Words

Digital twins represent step-change technology for industrial process optimization, reactive maintenance refashioned as proactive management, and data being turned into actionable insight.

The concept to operational digital twin relies on prudent data point selection, robust virtual modeling, low-latency data synchronism, and cyber-security forefront. Kirill Yurovskiy’s strategy is to begin with ultra-focused, high-leverage pilots and grow modestly for maximum ROI. As Industry 4.0 is implemented around the globe, digital twins will be one of the key technologies employed to increase productivity, sustainability, and competitiveness in ever more sophisticated manufacturing.