Chemical Formulation Multi-Criteria Optimization With Pareto Modeling
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Solution Overview
Problem
Current methods for optimizing chemical formulations in complex production processes are inefficient, relying on trial-and-error and empirical approaches, which are time-consuming and limited in scope, especially when dealing with multidimensional spaces and objectives such as stability, safety, and environmental considerations.
Innovation Solution
A computer-implemented method for producing chemical formulations that receives input data, performs multicriterial optimization using a computational model, and provides optimization signals to control the production process, reducing the need for lab experiments and enabling systematic optimization and transparent decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If trial-and-error and empirical approaches are used to optimize chemical formulations, then the process can handle complex multidimensional parameters, but the time consumption and number of experiments increase significantly
Solution Approach 1:
The system performs preliminary computational optimization using machine learning models and Pareto optimization algorithms before actual lab experiments. This preliminary action identifies promising formulation candidates and optimal parameter combinations, reducing the need for extensive trial-and-error experiments in the laboratory.
Solution Approach 2:
The system creates virtual copies of the chemical formulation process through computational models and simulations. These digital twins allow optimization to be performed in silico, copying the behavior of actual chemical systems without requiring physical experiments for every parameter combination.
2Ease of manufacture
If conventional optimization methods are used, then the approach is simple to implement, but the scope of optimization is limited and cannot provide systematic exploration of the full solution space
Solution Approach 1:
The optimization system is designed to handle multiple objectives simultaneously (product quality, safety, environmental impact, cost) and can accommodate various types of chemical formulations and processes. The Pareto optimization framework provides a universal approach that works across different chemical systems while maintaining systematic exploration of the solution space.
Solution Approach 2:
The system systematically varies multiple parameters simultaneously using design of experiments (DoE) and response surface methodology, rather than changing one parameter at a time. This allows comprehensive exploration of the multidimensional parameter space while maintaining manageable complexity through statistical design principles.
3Reliability
If empirical iterative optimization is performed, then solutions can be found that fulfill requirements, but no guarantee on optimality can be given and interesting solutions may be overlooked
Solution Approach 1:
The system implements feedback loops where experimental results are fed back into the machine learning models to refine predictions and update the optimization landscape. This iterative learning process ensures that the system continuously improves its understanding of the formulation space and can identify optimal solutions with greater confidence.
Solution Approach 2:
The optimization system dynamically adapts its search strategy based on the explored solution space and identified patterns. The Pareto frontier is dynamically updated as new information becomes available, allowing the system to shift focus between different regions of the solution space and ensure comprehensive coverage without missing important solutions.
Data Source
AI summary
The present invention relates generally to the field of chemical formulations in a chemical production facility, and more particularly to providing assistance for producing a chemical formulation in a chemical production facility. In detail, the present invention relates to a computer-implemented method for providing assistance for optimizing chemical formulations, comprising: (a) receiving input data, preferably via an input unit, of at least one set of experimental data comprising formulation data and/or process data, key physicochemical properties of the formulation and a target product profile, TPP, comprising a minimum product requirement, (b) performing multicriterial optimization based on a computational model based on experimental data via a processing unit and (c) providing optimization signal, preferably via an output unit, wherein the optimization signal is configured to control and/or monitor, preferably via a control unit, the production process of the chemical formulation