Pharmaceutical Process Design System for Batch Manufacturing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current process design and management systems for pharmaceutical and chemical manufacturing are inefficient, as they lack a fully integrated solution that can operate at both a generic master recipe level and a specific facility level, leading to difficulties in scaling recipes across different manufacturing plants and inadequate real-time process control, which results in substantial time and expense for achieving consistent product quality.
Innovation Solution
A process design and management system comprising General Design, General Master Design, and Master Design digital software objects that allow users to assemble and derive process designs and recipes, including material balances, cycle time, and constraints, with a graphical user interface for multiple views and dynamic interaction, enabling the creation of a Master Recipe for plant-specific equipment processes and facilitating real-time monitoring and adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a process design system operates at both generic master recipe level and specific facility level, then adaptability and versatility improve, but device complexity increases
Solution Approach 1:
The system divides process design into hierarchical levels: generic master recipe level (defining process steps, parameters, and sequences) and specific facility level (defining equipment instances and configurations). This segmentation allows each level to be managed independently, improving adaptability without overwhelming complexity.
Solution Approach 2:
The system creates universal process designs at the generic level that can be instantiated across multiple facilities. A single generic recipe can serve multiple specific implementations, enabling one design to perform multiple functions across different plants while maintaining consistency.
2Productivity
If recipes are scaled across different manufacturing plants, then productivity improves, but manufacturing precision deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where process data from specific facility executions is captured and used to refine and update generic master recipes. This continuous feedback loop ensures that scaling across plants maintains precision by learning from actual performance data and making corrective adjustments.
Solution Approach 2:
The system allows parameter changes at the specific facility level while maintaining the core process structure. Equipment-specific parameters can be adjusted to match local conditions without altering the fundamental recipe, enabling precise local adaptation while maintaining overall process consistency across scaled implementations.
3Reliability
If real-time monitoring and adjustments are implemented, then reliability improves, but device complexity increases
Solution Approach 1:
The system implements real-time monitoring that feeds process data back to the control system, enabling automatic adjustments and alerts. This feedback loop improves reliability by detecting and correcting deviations before they affect product quality, while the automated nature of the feedback reduces the operational complexity burden.
Solution Approach 2:
The system enables self-adjusting capabilities where the process control system automatically makes adjustments based on real-time data without requiring constant human intervention. This self-service approach improves reliability through continuous monitoring while reducing the operational complexity of manual control.
4Loss of information
If comprehensive process design documentation is maintained, then loss of information decreases, but device complexity increases
Solution Approach 1:
The system creates digital copies of process designs, recipes, and parameters that can be stored, retrieved, and replicated across facilities. Instead of managing complex physical documentation, the system uses digital copying to maintain comprehensive information, reducing information loss without proportionally increasing complexity through automated digital management.
Data Source
AI summary
A process design and management system for batch manufacturing of pharmaceuticals products. The system permits a user to create a chemical process design based on the user's input data and retrieved process library data which includes material data, process data, and equipment data. The system includes software objects defining operations sequences, and processing operation parameters including materials flows and balances, cycle time, constraints, equipment, generic equipment capability requirements, specific equipment capability requirements, and actual capacity analysis. A graphical user interface allowing multiple views of the chemical process design, including one or more of a design view, process flow view, time cycle view, and instructions view.