Predictive Process Modeling for Industrial Parameter Optimization
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Solution Overview
Problem
Current industrial engineering practices rely on spreadsheet-based modeling and physical simulations, which lack predictive capabilities and are not easily maintainable, leading to inefficiencies and loss of knowledge when key personnel leave, and there is a need for continuous optimization of industrial processes independent of application engineers or sales representatives.
Innovation Solution
A multivariate statistical approach is used to develop a predictive modeling tool that performs data analysis on industrial processes, generating a data model to identify key parameter values for optimizing output variables, and provides recommendations for machine settings and product selection through a web-based interface, enabling continuous optimization and knowledge retention.
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 it is easy to implement and store data locally, but it has low predictive or optimization capability and cannot provide solid conclusions
Solution Approach 1:
The patent replaces traditional spreadsheet-based mechanical modeling systems with a neural network-based intelligent system. The neural network learns from historical data and provides predictive capabilities, transforming the system from purely descriptive to predictive and prescriptive, thereby resolving the contradiction between ease of implementation and predictive capability.
Solution Approach 2:
The patent transforms the modeling approach by changing from static spreadsheet formulas to dynamic neural network parameters that continuously learn and adapt from data. This parameter transformation enables the system to maintain ease of use while gaining sophisticated predictive capabilities through automated learning from historical industrial data.
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 it requires formula and coefficient experts to maintain the models and does not have predictive capability for new information
Solution Approach 1:
The neural network system performs self-learning and self-maintenance by automatically learning from historical data without requiring expert intervention. The system autonomously updates its internal parameters and adapts to new information, eliminating the need for formula and coefficient experts while maintaining high measurement precision.
Solution Approach 2:
The patent creates a virtual copy of the industrial process through the neural network that replicates the behavior of physical systems without requiring physical prototypes or complex analytical models. This virtual model captures essential relationships and can predict outcomes for new scenarios without requiring expert maintenance.
3Loss of time
If spreadsheet-based approaches are used to note actual performance results and calculate cost estimates, then reports can be generated and stored locally, but the approach is not useful in reaching solid conclusions and requires 3-5 years to become an experienced Industrial Application Engineer
Solution Approach 1:
The neural network system continuously learns from feedback in the form of historical performance data and outcome results. This automated feedback mechanism captures and retains organizational knowledge, eliminating the need for lengthy training periods and preventing knowledge loss when engineers leave, while rapidly generating informed conclusions.
Solution Approach 2:
The system performs preliminary learning and analysis by continuously processing historical data in the background, so that when queries are made, predictions and recommendations are immediately available. This preliminary action eliminates the time required for human experts to analyze data and prevents knowledge loss by encoding expertise in the neural network.
4Productivity
If a key Industrial Application Engineer leaves a company, then the position is filled, but the performance information and operations knowledge learned on the job is typically lost
Solution Approach 1:
The neural network creates a digital copy of the expert engineer's knowledge and experience by learning from historical data and decision patterns. This copied knowledge resides in the system and can be accessed by any user, eliminating knowledge loss when engineers leave and enabling immediate productivity without lengthy training periods.
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.