Continuous Ultrafiltration Control for Variable Feed Concentration
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Solution Overview
Problem
Conventional systems for monitoring and controlling continuous ultrafiltration processes, particularly in biopharma manufacturing, face challenges in handling variating concentrations in feed streams, are inefficient, and require expensive optimization of UF module configurations to maintain consistent product concentration, with limited process analytical tools and complex scheduling.
Innovation Solution
A system and method that utilizes a data receiving module, conductivity data conversion, prediction module, optimization module, and control module to preprocess sensor data, convert conductivity data into protein concentration, predict and optimize volumetric concentration factor (VCF) and throughput, and control these values based on trajectory profiles, while considering fouling indices and dynamic optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional monitoring and control methods are used for continuous ultrafiltration processes, then the system structure is simple, but the system cannot handle variating concentrations in feed streams effectively and requires expensive optimization of UF module configurations
Solution Approach 1:
The system dynamically adjusts operating parameters (feed flow rate, transmembrane pressure) based on real-time feed concentration measurements to maintain target VCF and throughput. This allows the system to adapt to varying feed concentrations without requiring expensive hardware modifications to the UF modules themselves.
Solution Approach 2:
The patent replaces physical optimization of UF module configurations with a software-based control system that uses sensors, data processing algorithms, and automated control to achieve the same adaptability. This substitutes mechanical/hardware changes with informational and computational processes.
2Reliability
If process analytical tools are limited in conventional systems, then the device complexity is reduced, but the monitoring and control of mAbs concentration in continuous UF is insufficient to handle potential challenges
Solution Approach 1:
The control system performs multiple functions using a single integrated platform: it measures feed concentration, converts conductivity data to protein concentration, predicts VCF and throughput, optimizes operating parameters, and controls the UF process. This multi-functional approach enhances reliability without proportionally increasing device complexity.
Solution Approach 2:
The system implements continuous feedback loops where real-time sensor data on feed concentration is used to adjust operating parameters, and predicted VCF and throughput values are fed back to optimize control decisions. This feedback mechanism ensures reliable monitoring and control of mAbs concentration throughout the continuous UF process.
3Productivity
If UF module configuration is optimized to handle continuous process requirements, then the productivity and adaptability are improved, but the cost significantly increases in the downstream process
Solution Approach 1:
The system achieves continuous process efficiency through dynamic control of operating parameters rather than static optimization of UF module configurations. By continuously adjusting feed flow rate and transmembrane pressure based on real-time conditions, the system maintains high productivity without the capital expense of customized hardware configurations.
Solution Approach 2:
The patent uses software models and simulations to replicate and optimize UF process behavior, replacing the need for expensive physical optimization of UF module configurations. The digital twin or process model allows virtual testing and optimization without modifying the actual hardware.
4Manufacturing precision
If real-time control of VCF and throughput is implemented, then the manufacturing precision of product concentration is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary data preprocessing (removing outliers, imputing missing values) and converts conductivity data to protein concentration before control decisions are made. This preparation work is done in advance to streamline the real-time control process and reduce computational complexity during critical control moments.
Solution Approach 2:
The system introduces intermediate calculations and conversions (conductivity to protein concentration, prediction of VCF and throughput) as mediators between raw sensor data and final control actions. These intermediary steps transform complex measurements into actionable control parameters, improving precision while managing computational complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables robust and efficient monitoring and control of continuous ultrafiltration processes, maintaining target VCF and throughput by handling real-time deviations and long-term operations, reducing fouling, and optimizing feed flow rates and pressures for improved process efficiency.
Implementation Method 1
Membrane ultrafiltration (UF) is a pressure-modified, convective process that uses semipermeable membranes to separate species in aqueous solutions by molecular size, shape, and/or charge
Implementation Method 2
The conductivity data conversion (CDC) module converts an inline conductivity sensor data associated with the real time data into a concentration of protein of interest
Data Source
AI summary
This disclosure relates generally to method and system to monitor and control continuous ultrafiltration (UF) process units. In real time, continuous operation of UF to handle variating concentration in feed stream is tedious and complex. The UF plant system receives a plurality of input data configured to UF process units and from the real time data outliers are removed and missing values are imputed. The prediction module predicts a volumetric concentration factor (VCF) value and a throughput value by selecting a model from a model repository. The optimization module optimizes the VCF value, and the throughput value based on a plurality of optimal variables recommended for a given feed concentration. The UF plant system controls the VCF value and the throughput value for a predefined period of a prediction horizon based on a plurality of trajectory profiles recommended for the feed flow rate, the pressure data, and a feed concentration.