Digital Twin Simulation Model Transformation for Process Plants
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Solution Overview
Problem
Existing methods for generating simulation models for digital twins of process engineering production plants are inefficient and do not allow for cost-effective utilization throughout the plant's lifecycle, lacking comprehensive integration of model transformations and applications.
Innovation Solution
A computer-implemented method and system that generates simulation models by transforming a steady-state flow-driven model into a steady-state pressure-driven model, incorporating piping and instrumentation data, and further extending it to include actuators and PID controllers, enabling consistent use across various lifecycle applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulation models are manually created by domain experts, then model accuracy and domain knowledge integration are improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system creates digital copies of physical process components by automatically generating simulation models from process description data. Instead of manually building models, the system copies the essential characteristics and behavior patterns from process data into executable simulation models, significantly reducing time consumption while maintaining accuracy through automated model generation algorithms
Solution Approach 2:
The system performs preliminary actions by pre-defining model templates, parameter structures, and simulation frameworks before actual model generation. Domain knowledge is embedded in advance into the model generation engine, so when process data is provided, the system can quickly instantiate accurate simulation models without requiring real-time manual expertise intervention
2Reliability
If detailed simulation models are created to accurately represent complex production processes, then process understanding and optimization capability are improved, but model complexity and resource requirements increase
Solution Approach 1:
The system segments complex production processes into discrete process steps and individual simulation models. Each process step is modeled separately with its own parameters and behavior, then integrated into a comprehensive simulation. This segmentation maintains high process understanding by focusing on individual components while reducing overall model complexity through modular structure
Solution Approach 2:
The system applies local quality by assigning different levels of model detail and complexity to different process steps based on their specific requirements. Critical process steps receive more detailed modeling while less critical steps use simplified models, optimizing the balance between process understanding and model complexity for each local region of the process
3Ease of manufacture
If simulation models are generated without automated tools, then development cost is reduced, but productivity and speed of model generation decrease
Solution Approach 1:
The system implements self-service by enabling automated generation of simulation models from process description data without requiring extensive manual intervention. The model generation engine automatically parses process data, selects appropriate model templates, configures parameters, and generates executable models, significantly improving productivity while keeping development costs low through automation of routine tasks
Solution Approach 2:
The system utilizes parameter changes by automatically adjusting model parameters based on input process data. The model generation engine dynamically configures simulation parameters, scales, and units based on the specific process being modeled, enabling rapid adaptation to different processes without manual reconfiguration and thereby increasing model generation speed
Data Source
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AI summary
The invention involves generating, for a digital twin of a process of a production installation, a stationary flow-driven simulation model (41) of the process (3) and, based on this simulation model, a stationary pressure-driven simulation model (51) of the process (3). The latter model is used to generate a dynamic pressure-driven simulation model (61) of the process, which comprises sensors and actuators of control loops of the installation. Each of the simulation models (41, 51, 61, 71) determines measurable state variables (T) of the production installation (1), preferably also characteristic values (Q) for a quality of the product (S), on the basis of material flows of educts (E) and operating media (B) that are supplied to the production installation (1). Model data (MD) of each of the simulation models (41, 51, 61, 71) are generated, and stored in a data memory (125), in such a way that they can be read by simulation software (126) and used to execute the simulation models (41, 51, 61, 71). The stationary flow-driven simulation model is therefore continuously used for a digital process twin. This model is continually developed further and matched to the respective application without losing information from earlier phases or having to manually enter said information again. Development of a digital process twin can thus be amortized over multiple incidents of use.