ML Resource Prediction for Industrial Emissions
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Solution Overview
Problem
Traditional computing systems are unable to accurately quantify resource reduction potential, identify drivers of resource consumption, or scale resource optimization across multiple facilities with diverse assets, leading to inefficiencies in resource utilization and increased emissions.
Innovation Solution
A machine learning-based system that uses Temporal Fusion Transformers (TFT) and hierarchical aggregation to predict resource baseline values at various levels, from organizational to asset levels, enabling accurate tracking and optimization of resource consumption and emissions by analyzing historical data and real-time sensor measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computing systems are used for resource tracking, then system simplicity is maintained, but the ability to quantify resource reduction potential and identify consumption drivers is lost
Solution Approach 1:
The patent introduces machine learning models as intermediary components between traditional computing systems and resource optimization goals. These models process historical data and sensor measurements to generate predictions about resource consumption and reduction potential, enabling precise measurement without requiring complete system redesign
Solution Approach 2:
The patent replaces traditional rule-based and manual resource tracking mechanisms with machine learning-based predictive systems. This substitution enables automated identification of consumption drivers and quantification of reduction potential that were previously impossible with conventional approaches
2Loss of information
If traditional computing systems are used for resource optimization, then implementation simplicity is maintained, but the ability to identify correlations between drivers and resource outputs is lost
Solution Approach 1:
The patent employs machine learning models as intermediaries that analyze relationships between operational drivers and resource outputs. These models process complex data patterns to identify correlations and generate actionable insights about consumption drivers that traditional systems cannot detect
Solution Approach 2:
The patent transforms raw operational data into meaningful parameters and features that reveal relationships between drivers and resource consumption. By changing how data is represented and processed, the system uncovers hidden correlations without requiring complex physical modifications
3Adaptability or versatility
If traditional computing systems are used for resource optimization, then system simplicity is maintained, but scalability across multiple facilities and assets is impossible
Solution Approach 1:
The patent creates a universal machine learning platform that can be deployed across multiple facilities and asset types. The system uses standardized data processing pipelines and models that adapt to different contexts, enabling scalable implementation without requiring facility-specific customizations
Solution Approach 2:
The patent divides the resource optimization problem into manageable segments that can be processed independently at each facility while contributing to organization-wide insights. This segmentation enables parallel processing and scalable deployment across multiple locations
4Measurement precision
If machine learning-based prediction is implemented, then resource optimization accuracy is improved, but computational energy consumption increases
Solution Approach 1:
The patent performs computational work in advance by training machine learning models on historical data before deployment. Once trained, the models require significantly less energy for real-time predictions, reducing ongoing computational energy consumption while maintaining high accuracy
Data Source
AI summary
Aspects of this disclosure are directed to enterprise systems and methods that provide machine learning and artificial intelligence (AI) driven software that generates baseline predictions and optimizations for production capacity to reduce waste and harmful byproducts. Baseline predictions can include resource baseline predictions that can help estimate (or, predict) how much lower or higher an asset's resource inputs (e.g., fuel) and/or outputs (e.g., emissions) could be in comparison to the asset's current resource inputs and/or outputs. AI generated baseline predictions can be broken down into several different levels (e.g., facility level down to the asset level) so that it is clear where the most significant opportunities for resource savings lie, and the system can perform optimizations (e.g., trigger corrective actions) to achieve those resource savings.