Digital Process Twin Soft Sensor for Real-Time Quality Control
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Solution Overview
Problem
The pharmaceutical industry faces inefficiencies in process development, quality control, and data management, leading to increased experimentation time, waste, and costs, with a need for real-time quality assessment and sustainable process development.
Innovation Solution
A soft sensor system integrated with a digital process twin, utilizing a non-real-time simulation model and machine learning to estimate quality attributes of output products in real-time, optimizing process performance and reducing waste and raw material usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sampling and offline quality measurements are used, then product quality can be assessed, but experimentation time increases and production efficiency decreases
Solution Approach 1:
The patent replaces manual mechanical sampling and offline laboratory measurement systems with an automated soft sensor system based on machine learning models. The soft sensor uses process data from the digital twin to predict quality attributes in real-time, eliminating the need for physical sampling and offline analysis, thereby reducing experimentation time while maintaining quality assessment accuracy
Solution Approach 2:
The patent creates a virtual copy (digital twin) of the physical process system that replicates process behavior and quality outcomes. This digital replica allows quality assessment to be performed virtually in real-time without requiring physical sampling and offline measurement, thus reducing time loss while preserving measurement precision
2Reliability
If multiple engineering runs and offline quality measurements are conducted, then product quality is safeguarded, but production costs increase
Solution Approach 1:
The patent implements preliminary quality assessment through the soft sensor system that predicts quality attributes before actual production runs are completed. By using the digital twin to simulate and evaluate quality outcomes in advance, the system can identify optimal process parameters and potential quality issues beforehand, reducing the need for multiple costly engineering runs and minimizing raw material consumption while maintaining quality assurance
Solution Approach 2:
The patent replaces physical offline quality measurements that consume raw materials with virtual soft sensor predictions based on the digital twin. This substitution eliminates the need to physically produce and test multiple batches, thereby reducing raw material consumption while maintaining reliable quality assurance through accurate predictive modeling
3Productivity
If real-time quality monitoring is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The patent creates a universal digital twin platform that serves multiple functions: process simulation, quality prediction, optimization, and control. This multi-functional system consolidates what would otherwise require separate systems for each function, enabling real-time quality monitoring and improved productivity while managing overall system complexity through integration rather than proliferation of separate components
Solution Approach 2:
The patent introduces a soft sensor as an intermediary layer between the digital twin and the physical process control system. The soft sensor translates complex digital twin simulations into actionable quality predictions that can be used for real-time monitoring and control, simplifying the interface between the complex simulation model and the practical control system, thereby enabling productivity improvement without proportionally increasing operational complexity
4Loss of substance
If digital twin solutions are implemented, then waste is reduced, but implementation costs increase
Solution Approach 1:
The patent uses the digital twin to perform preliminary simulations and optimizations before actual production, allowing waste reduction to be achieved through virtual experimentation and parameter optimization. This approach minimizes the need for costly and complex physical implementation changes by first testing and validating improvements in the virtual environment
Solution Approach 2:
The patent creates a digital copy of the production system that allows waste reduction to be achieved through virtual modeling and simulation. By replicating process behavior in the digital twin, the system can identify and eliminate waste sources without requiring complex physical modifications, thereby reducing substance loss while managing implementation complexity through software-based solutions
Data Source
AI summary
A digital process twin system and method for operating a process plant with at least one automation component to control an industrial process within the process plant with at least one input ingredient and at least one output product, wherein a non-real-time simulation model of the industrial process is used to generated quality attributes as a function of process variables and process parameters, the generated quality attributes and related process variables are used as an input for a machine learning model serving as a soft sensor to estimate quality attributes of the output product as a function of measured or simulated process variables of the industrial process, and the performance of the process plant is optimized based on the estimated quality attributes of the output product, whereby the method and system allow process operations and control that are faster, more efficient, and more reliable.