Production Recipe Optimization Using Time-Varying Process Trajectories
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Solution Overview
Problem
Conventional processes for producing chemical, pharmaceutical, and biotechnological products fail to accurately account for the complex interplay between various factors affecting the production process, often relying on simplistic approaches that underestimate non-linear dynamics and lack comprehensive integration of available knowledge.
Innovation Solution
A computer-implemented method that optimizes the production process by considering a plurality of factors through the use of recipe templates and process evolution information, allowing for variable recipe and evolution parameters to simulate and determine optimal production recipes, thereby integrating available knowledge effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional simplistic optimization approaches focusing on single set-point identification are used, then the optimization process is easier to implement, but the accuracy of accounting for complex interplay between factors deteriorates
Solution Approach 1:
The patent transforms the optimization approach from identifying single set-points to determining time-varying trajectories for multiple process parameters simultaneously. This involves changing from static parameter optimization to dynamic trajectory optimization, where multiple parameters are optimized together as functions of time rather than as isolated target values.
Solution Approach 2:
The patent adds the time dimension to parameter optimization by introducing trajectories as time-based profiles. Instead of optimizing parameters at a single point in time, the system optimizes entire temporal profiles of multiple parameters, effectively moving from zero-dimensional (single point) to one-dimensional (time-based) optimization space.
2Device complexity
If available knowledge about the process is taken into consideration only in a rudimental manner, then the process design is simpler, but the overall performance optimization deteriorates
Solution Approach 1:
The patent implements feedback by using the utility function to evaluate how well simulated trajectories match desired performance criteria. The optimization process iteratively adjusts parameter trajectories based on this feedback, continuously improving the solution until the utility function indicates satisfactory performance. This allows systematic incorporation of domain knowledge into the optimization.
Solution Approach 2:
The patent performs preliminary simulation of process trajectories before final optimization. By simulating how the process would behave with candidate parameter trajectories and evaluating these against the utility function in advance, the system can identify promising optimization directions before committing to final parameter settings, reducing trial-and-error in the actual process design.
3Productivity
If maximization of product titre is pursued as the primary goal, then the product output is increased, but the comprehensive optimization of the production process deteriorates
Solution Approach 1:
The patent creates a multi-functional utility function that simultaneously evaluates multiple performance aspects including product titre, process efficiency, resource utilization, and other critical factors. This single comprehensive utility function allows the optimization to balance competing objectives and find trajectories that perform well across all dimensions rather than excelling at one while failing others.
Data Source
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AI summary
Summarizing the invention, a computer-implemented method of determining at least one recipe for a production process to produce a chemical, pharmaceutical and/or biotechnological product is provided, wherein the production process is defined by a plurality of steps specified by recipe parameter(s) controlling an execution of the production process and a recipe comprises the plurality of steps defining the production process.