Computer Modeling for Chemical Composition Design
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Solution Overview
Problem
Customized chemical composition formulation on a commercial scale is complex and time-consuming, often requiring numerous laboratory experiments to validate designs that meet client operational requirements, especially in environmentally sensitive areas where component availability and governmental restrictions apply.
Innovation Solution
A method utilizing computer modeling and statistical algorithms to generate optimized composition designs based on defined operational parameters, reducing the need for laboratory tests by predicting performance and allowing for user-guided selection and optimization of composition components, including wellbore fluids, cement compositions, and other industrial applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If numerous laboratory experiments are conducted to validate composition designs, then design reliability is improved, but time consumption and complexity increase
Solution Approach 1:
The patent applies preliminary action by using computer modeling and statistical algorithms to predict composition performance before conducting physical laboratory experiments. The system performs virtual simulations and optimizations in advance, allowing designers to evaluate multiple composition variants computationally before selecting the most promising candidates for actual lab testing, thereby reducing the number of experiments needed while maintaining validation reliability
Solution Approach 2:
The patent employs copying by creating virtual replicas of physical experiments through computer modeling. Instead of repeatedly conducting physical laboratory experiments, the system uses computational models that replicate experimental conditions and outcomes, allowing for numerous virtual iterations without the time and resource costs of physical copying, thus reducing time consumption while preserving the ability to validate design reliability
2Manufacturing precision
If numerous laboratory experiments are conducted to validate composition designs, then design accuracy is improved, but process complexity increases
Solution Approach 1:
The patent applies mechanics substitution by replacing the mechanical process of conducting physical laboratory experiments with computational algorithms and statistical modeling. The system uses computer-based optimization routines and predictive models to achieve composition design accuracy without requiring complex physical experimental setups, thereby reducing process complexity while maintaining or improving design precision
Solution Approach 2:
The patent utilizes parameter changes by systematically varying composition parameters within the computational model to optimize design accuracy. The system adjusts chemical concentrations, ratios, and formulation parameters through algorithmic exploration rather than manual experimental adjustment, enabling precise control over composition variables and achieving high manufacturing precision with simpler procedural steps
3Loss of time
If computer modeling is used to predict composition performance, then time consumption is reduced, but measurement precision may be compromised
Solution Approach 1:
The patent applies feedback by using statistical modeling techniques that incorporate iterative refinement and validation. The computer model is trained on historical composition data and continuously improved through feedback loops where predicted outcomes are compared with actual experimental results, allowing the system to maintain high measurement precision while benefiting from the speed of computational prediction
Solution Approach 2:
The patent employs partial action by using computer modeling to evaluate only the most critical performance parameters rather than conducting exhaustive physical experiments on all possible composition variants. The system performs sufficient computational analysis to achieve adequate measurement precision for decision-making, avoiding the time consumption of complete physical validation for every possible scenario
Data Source
AI summary
Methods may include defining operational parameters for an initial composition design; generating an initial composition design from the defined operational parameters; predicting the performance of the initial composition design using a statistical model; comparing the performance of the initial composition design with the operational parameters; optimizing the initial composition design according to the defined operational parameters; and outputting a final composition design. Methods may also include defining operational parameters for an initial composition design for a wellbore fluid; generating an initial composition design from the defined operational parameters; predicting the performance of the initial composition design using a statistical model; comparing the performance of the initial composition design with the operational parameters; optimizing the initial composition design according to the defined operational parameters; and outputting a final composition design.