Predictive Modeling for Industrial Process Parameter Optimization
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Solution Overview
Problem
Current industrial engineering applications lack predictive and optimization capabilities, relying on spreadsheet-based modeling that is not effective in simulating and continuously optimizing industrial processes, leading to inefficiencies and loss of knowledge when key personnel leave.
Innovation Solution
A computer-implemented method using a multivariate statistical approach to generate a data model of industrial processes, which is then used by a predictive algorithm to identify optimal parameter values for improving process outcomes, enabling continuous optimization and simulation across various industrial applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If spreadsheet-based modeling is used to describe and report results for industrial applications, then individual performance results can be noted and cost/value estimates can be calculated, but the system lacks predictive or optimization capability and cannot provide solid conclusions
Solution Approach 1:
The patent replaces traditional spreadsheet-based mechanical modeling with a physics-based simulation engine that uses fundamental physical laws and equations to model industrial processes. This substitution enables predictive capabilities by calculating actual process outcomes based on physical principles rather than relying on historical data trends or manual estimates, thereby resolving the contradiction between ease of report creation and predictive reliability
Solution Approach 2:
The patent introduces a physics-based simulation engine as an intermediary between input parameters and output predictions. This simulation engine acts as a mediator that uses fundamental physical laws to transform input variables into predicted process outcomes, providing a reliable predictive layer that bridges the gap between simple data collection and accurate process optimization recommendations
2Measurement precision
If physical simulations or models are used for product simulations with fine-tuned formula coefficients, then key performance indicators can be described, but the approach requires formula and coefficient experts to maintain the models and does not have predictive capability for new relations
Solution Approach 1:
The patent replaces complex, expert-maintained formula-based physical models with a simulation engine grounded in fundamental physics principles. By using universal physical laws rather than empirical formulas requiring expert tuning, the system achieves comparable or superior measurement precision while eliminating the need for specialized formula and coefficient management expertise
Solution Approach 2:
The patent creates a universal simulation engine based on fundamental physics principles that can model diverse industrial processes without requiring process-specific expert knowledge. This universal approach allows the same core simulation technology to handle different applications (e.g., grinding, machining, thermal processes) by adjusting input parameters rather than requiring separate expert-maintained formula sets for each process type
3Loss of information
If physical simulations require user-defined formulas describing core process relations, then current knowledge can be modeled, but new learning information cannot be separated from outliers and predictive capability is limited to known relations
Solution Approach 1:
The patent implements a feedback mechanism where simulation results are continuously compared with actual process data. This feedback loop enables the system to learn from discrepancies between predicted and actual outcomes, automatically updating and refining the physics-based models. This allows the system to capture new learning information while maintaining accuracy, resolving the contradiction between preserving current knowledge and adapting to new insights
4Ease of operation
If three to five years are needed to become an experienced Industrial Application Engineer, then performance information and operations knowledge can be learned on the job, but this knowledge is lost when key personnel leave the company
Solution Approach 1:
The patent creates a digital copy of expert knowledge embedded within the physics-based simulation engine. By encoding operational expertise and performance information into the simulation's physical models and parameters, the system captures and preserves critical knowledge that would otherwise reside only in the minds of experienced engineers. This digital replication ensures knowledge retention regardless of personnel changes
Solution Approach 2:
The patent enables the simulation system to serve as a self-contained knowledge repository that provides guidance and predictions without requiring human experts. The physics-based models automatically encode and apply operational knowledge, allowing the system to function independently of specific personnel and eliminating the risk of knowledge loss when employees leave the organization
Data Source
AI summary
A system and method of simulating and optimizing industrial and other processes includes a computer that performs multivariate analysis of input variables and output variables to generate a data model of the operation of the process. For industrial applications, the input variables include process variables and the output variables include result variables from the operation of the industrial process. The data model determines contributions to changes in the output or result variables by the respective input or process variables and is provided to a predictive algorithm to identify parameter values for input or process variables expected to have a most significant impact on the output or result variables during performance of the process. The outputs of the predictive algorithm are parameter values that are provided as input or process variables to the industrial process for simulation or performance optimization or product recommendations/optimizations.