Production Recipe Optimization for Multi-Step Bioprocesses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional processes for producing chemical, pharmaceutical, and biotechnological products fail to accurately account for the complex interplay of factors influencing production outcomes, often relying on simplistic approaches that underestimate non-linear dynamics and lack comprehensive integration of available knowledge.
Innovation Solution
A computer-implemented method determines optimal recipes for production processes by considering multiple factors and available knowledge, using recipe parameters to control the execution of steps in bioreactors, and employing simulations to explore a wide range of alternatives for optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional simplistic optimization approaches focusing on single set-point targets are used, then the optimization process is easier to implement, but the accuracy of accounting for complex interplay between factors is insufficient
Solution Approach 1:
The patent segments the complex production process into multiple discrete steps, each with its own recipe parameters. The optimization is performed step-by-step through sequential experimentation, where each step's parameters are optimized independently while considering their impact on subsequent steps. This segmentation allows the complex multi-factor optimization problem to be broken down into manageable sub-problems that can be solved iteratively.
Solution Approach 2:
The patent employs dynamic optimization where recipe parameters are not fixed but are adjusted iteratively based on experimental results. The system continuously refines parameter values across multiple experimentation cycles, adapting the optimization strategy based on observed outcomes. This dynamic approach allows the system to capture non-linear relationships and complex interactions between parameters that static single-set-point methods miss.
2Reliability
If available knowledge about the process is integrated in a rudimental manner with a priori selected models, then the implementation is simpler, but the effective integration of knowledge is insufficient
Solution Approach 1:
The patent implements a feedback-driven optimization system where experimental results from each step are fed back into the optimization process. The system uses observed outcomes to refine parameter selections for subsequent experiments, continuously improving the integration of process knowledge. This feedback loop allows the system to learn from actual process behavior and adjust its model and parameter selections accordingly, rather than relying solely on a priori assumptions.
Solution Approach 2:
The patent performs preliminary experimentation and analysis to build an initial understanding of the process before full-scale optimization. Initial experiments are conducted to identify key parameters and their relationships, which then inform the selection of models and parameter ranges for subsequent optimization cycles. This preliminary action allows the system to incorporate available knowledge effectively while preparing for more rigorous optimization.
3Productivity
If maximisation of product titre is pursued as the single target, then the optimization focus is clearer, but the overall process performance is not optimised
Solution Approach 1:
The patent optimizes multiple parameters simultaneously across different steps of the production process, not just those directly affecting product titre. By changing and optimizing parameters at each step (temperature, pH, mixing conditions, timing) while considering their interrelationships, the system achieves better overall process performance. This multi-parameter optimization approach ensures that titre maximization does not compromise other critical performance aspects.
Solution Approach 2:
The patent extends the optimization from a single-dimension (product titre) to multi-dimensional optimization by incorporating multiple performance criteria and process steps. The system evaluates and optimizes parameters across the entire process workflow, considering interactions between different steps and their combined impact on overall performance. This dimensional expansion allows the system to balance titre maximization with other performance requirements.
Data Source
AI summary
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.