Customizable AI Agent for Industrial KPI Setpoint Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Smaller entities and those without defined use for AI systems face challenges in applying AI to optimize industrial processes due to lack of infrastructure and expertise, and existing AI systems are often ineffective as they lack knowledge from domain-level experts.
Innovation Solution
A customizable AI system that aggregates system defining information from end users, including KPIs, process variables, and system information, to determine the ability to optimize KPIs and set optimal process variable setpoints, using an artificial intelligence agent to optimize KPIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing AI systems are deployed to optimize industrial processes, then prediction capabilities are improved, but decision-making capabilities and domain expertise integration are insufficient
Solution Approach 1:
The patent combines prediction functionality and decision-making functionality into a single integrated AI system. The system merges the capabilities to predict KPI values with the capability to generate actionable decisions, allowing domain experts to configure both prediction parameters and decision rules within one platform, thereby resolving the limitation of existing systems that can only predict but not decide.
Solution Approach 2:
The AI system is designed to perform multiple functions: it can predict KPI values, generate decisions based on predicted values, and adapt to different industrial processes through configurable parameters. This multi-functional design allows the same system to serve both prediction and decision-making needs across various industrial applications, enhancing versatility without requiring separate systems.
2Ease of operation
If AI systems are made accessible to smaller entities, then accessibility and ease of use are improved, but infrastructure requirements and expertise barriers remain
Solution Approach 1:
The system enables domain experts to self-configure AI optimization for their specific industrial processes through an intuitive interface. Experts can define their own KPIs, input process parameters, and configure decision rules without requiring deep AI expertise or extensive infrastructure setup. The system handles the complex AI model training and deployment automatically, allowing smaller entities to access AI capabilities with minimal infrastructure investment.
Solution Approach 2:
The patent introduces an intermediary layer between the domain expert and the complex AI infrastructure. This intermediary interface translates simple user inputs (KPI definitions, process parameters) into complex AI model configurations and deployments. By mediating between user-friendly input and complex backend processing, the system hides infrastructure complexity while maintaining ease of use for smaller entities.
3Productivity
If domain experts configure AI systems with detailed process knowledge, then optimization effectiveness is improved, but system complexity and configuration time increase
Solution Approach 1:
The configuration process is segmented into distinct, manageable components: KPI definition, process parameter input, and decision rule configuration. Each segment handles a specific aspect of the optimization problem, allowing domain experts to provide detailed process knowledge in organized chunks rather than overwhelming monolithic configurations. This segmentation maintains optimization effectiveness while reducing perceived complexity.
Solution Approach 2:
The system performs preliminary actions by automatically generating AI model configurations and training pipelines based on user-provided process parameters. Instead of requiring experts to manually configure complex model architectures and hyperparameters, the system pre-processes the input information and prepares the optimization framework in advance, reducing configuration time and complexity while preserving the ability to incorporate detailed domain knowledge.
Data Source
AI summary
Methods and systems are disclosed for determining a plan to optimize key performance indicators (KPIs) of an industrial process. Such a plan is determined based on generating a query for information associated with the KPIs and based on receiving user-provided object information corresponding to the KPIs. The method includes receiving, at a user interface, one or more KPIs associated with an industrial process. The method includes generating, based on the one or more KPIs, at least one query for information associated with the KPI. The method includes receiving, at the user interface, a response to the at least one query. The method includes determining, by an artificial intelligence agent, a plan for optimizing the KPI.