Mineral Foam Formulation Prediction for Density and Strength
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Solution Overview
Problem
Existing methods for producing mineral foams face challenges in determining and controlling the amounts of water and gas, leading to varying performance and increased need for lab testing, making it difficult to achieve desired properties such as dry mineral foam density and mechanical strength.
Innovation Solution
A computer-implemented method using a predictive model to determine formulation and process parameters based on input parameters related to desired performance, chemical and physical properties of constituents, and environmental conditions, allowing for precise control of mineral foam production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to produce mineral foams with manual formulation and lab testing, then production reliability is low with varying performance, but implementing a predictive model system increases device complexity and requires more advanced technology infrastructure
Solution Approach 1:
The patent replaces manual trial-and-error formulation methods and physical lab testing with a computer-based predictive model system. The system uses software algorithms to calculate optimal formulation parameters and process settings, substituting mechanical/manual processes with computational ones. This digital substitution enables consistent, reliable production by eliminating human variability and extensive physical prototyping.
Solution Approach 2:
The predictive model creates a virtual representation of the mineral foam production process that mirrors real-world behavior. By copying the complex interactions between formulation parameters, process conditions, and final product properties into a computational model, the system can predict outcomes without repeated physical experiments. This virtual copying enables reliable parameter determination while reducing the need for extensive lab testing infrastructure.
2Manufacturing precision
If extensive lab testing is conducted to determine optimal formulation parameters, then manufacturing precision improves, but productivity decreases due to time-consuming trial and error processes
Solution Approach 1:
The predictive model performs preliminary calculations of optimal formulation parameters and process settings before actual production begins. By pre-determining the correct water amount, gas amount, mixing parameters, and curing conditions through computational prediction rather than trial-and-error testing, the system achieves high manufacturing precision while eliminating time-consuming experimental iterations. This preliminary computational action replaces sequential physical testing with parallel computational analysis.
Solution Approach 2:
The system substitutes manual formulation development and physical lab testing with automated computational prediction. The predictive model rapidly calculates optimal parameters based on input requirements, replacing the slow mechanical process of mixing, testing, measuring, and adjusting formulations in the lab. This substitution maintains high precision while dramatically increasing production speed by eliminating iterative physical experimentation.
3Adaptability or versatility
If the mineral foam formulation is adjusted to meet changing performance requirements, then adaptability improves, but the need for repeated lab testing increases complexity and time consumption
Solution Approach 1:
The predictive model system is designed to dynamically adjust formulation parameters and process settings in response to changing performance requirements. When new product specifications or environmental conditions arise, the system recalculates optimal parameters instantly rather than requiring physical retesting. This dynamic computational adaptation enables the formulation to respond flexibly to varying demands while eliminating the time loss associated with repeated lab testing cycles.
Solution Approach 2:
The system enables rapid parameter changes by computationally predicting the effects of different formulation and process parameter combinations. When adaptability to new requirements is needed, the predictive model evaluates how changes in water content, gas content, mixing speed, temperature, or other parameters will affect final product performance. This computational parameter exploration allows instant adaptation without the time-consuming process of physically testing each parameter variation in the lab.
Data Source
Figure 1
AI summary
The present invention relates to computer implemented methods and systems to determine, based on given constituent parameters and environmental parameters, at least one formulation parameter and/or at least one process parameter of a mineral foam, suitable to achieve a given performance parameter.