Process Scale-Up Parameter Mapping for Mixing and Power Constraints
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Solution Overview
Problem
Current methods for scaling chemical, pharmaceutical, and biotechnological processes across different scales face challenges such as loss of process performance, inadequate consideration of multiple variables, and failure to account for prior knowledge about optimal conditions, leading to risks in process transitions and suboptimal product quality.
Innovation Solution
A computer-implemented method that uses parameter evolution information and recipe templates to simulate and optimize process parameters across scales, ensuring similarity in process performance by adjusting parameters like stir speed and gassing rates to maintain consistent conditions, and integrating prior knowledge to identify suitable process variants and potential issues before hardware deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If larger stirrer speeds are selected at larger scales to compensate for increased mixing time, then mixing time is reduced, but specific power input dramatically increases which may be detrimental to cells or product
Solution Approach 1:
The patent applies parameter changes by systematically varying multiple process parameters (stirrer speed, gassing rate, fill volume) simultaneously rather than changing single parameters in isolation. This allows optimization of mixing time while controlling specific power input through coordinated adjustment of multiple parameters to maintain similar microenvironments across scales.
Solution Approach 2:
The patent uses computational simulation to predict and evaluate scaling outcomes before actual hardware deployment. By performing preliminary simulations across multiple scales and evaluating microenvironment similarity, the system identifies optimal parameter settings that avoid detrimental power inputs while achieving acceptable mixing times.
2Device complexity
If traditional scaling methods use a single scale-independent parameter as intermediary, then scaling is simplified, but process performance is lost due to inadequate consideration of multiple variables and their interactions
Solution Approach 1:
The patent segments the scaling process into distinct computational stages: defining scale-independent parameters, simulating process trajectories, comparing microenvironments, and evaluating acceptability. This segmentation allows comprehensive consideration of multiple variables while maintaining manageable complexity through structured analysis.
Solution Approach 2:
The patent transitions from traditional single-parameter scaling to multi-dimensional scaling by considering multiple scale-independent parameters simultaneously (mixing time, oxygen transfer, power input) and evaluating their combined effects on microenvironment similarity across scales.
3Device complexity
If scale translation is performed as single-step translation from one source scale directly to a single target scale, then the process is simplified, but subsequent transitions may lead to dead ends where no account was taken of the end goal
Solution Approach 1:
The patent performs preliminary computational simulations to evaluate multiple potential scaling paths before committing to hardware deployment. By simulating trajectories across intermediate and final scales in advance, the system identifies optimal intermediate steps that maintain flexibility for subsequent transitions while working toward the final scaling goal.
Solution Approach 2:
The patent implements feedback by evaluating microenvironment similarity at each scaling step and using this information to adjust subsequent scaling decisions. The acceptability evaluation provides feedback on whether intermediate scaling steps are appropriate, allowing correction of suboptimal paths before they lead to dead ends.
4Device complexity
If prior process knowledge is not integrated during scaling, then the scaling process is simpler, but unrealistic process parameter values result and prior information about optimal conditions is lost
Solution Approach 1:
The patent incorporates prior process knowledge in advance by defining acceptability functions that encode optimal operating ranges and constraints before scaling simulations begin. This preliminary integration ensures that scaling recommendations maintain realistic parameter values consistent with established process understanding.
Solution Approach 2:
The patent uses feedback by comparing simulated scaling outcomes against acceptability criteria that reflect prior process knowledge. This evaluation provides feedback on whether scaled parameters remain within acceptable ranges, allowing adjustment of scaling strategies to maintain manufacturing precision.
Data Source
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AI summary
The present application generally pertains to scaling of a production process to produce a chemical, pharmaceutical and/or biotechnological product and/or of a production state of a respective production equipment. Particularly, there is provided a computer-implemented method of scaling a state of a production equipment for a production process to produce a chemical, pharmaceutical and/or biotechnological product, the scaling being from a source scale to a target scale, wherein the state is defined by a set of state parameters describing a condition and/or a behaviour of the production equipment, the method comprising: retrieving mapping information that describes how the state parameters relate to a set of derived parameters; receiving: a source setup specification of a source setup used for executing the production process at the source scale, the source setup specification comprising the source scale value; a target setup specification of a target setup used for executing the production process at the target scale, the target setup specification comprising the target scale value; a first set of state parameters at the source scale; a second set of state parameters at the target scale, wherein at least one of the state parameters at the target scale is a parameter being variable and having no predetermined value at the outset; at least one acceptability function defining conditions on the values of the state parameter(s) and/or the values of the derived parameter(s) at the source scale and/or at the target scale; calculating a first set of derived parameters at the source scale using the first set of state parameters, the source setup specification and the mapping information; providing an input value for the at least one variable parameter in the second set of state parameters; calculating a second set of derived parameters at the target scale using the second set of state parameters, the input value, the target setup specification and the mapping information; comparing the first set of state parameters with the second set of state parameters and/or comparing the first set of derived parameters and the second set of derived parameters; computing, based on the comparison and on the at least one acceptability function, an acceptability score for the second set of state parameters; computing an optimal value for the at least one variable parameter by optimising the acceptability score and/or computing an acceptable range for the at least one variable parameter, wherein values within the acceptable range yield an acceptability score above a specific threshold.