Machine Learning Carbon Emission LCA Mapping Across Diverse Data
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Solution Overview
Problem
The oil and gas industry faces challenges in efficiently tracking and interpreting sustainability parameters across diverse data sources, leading to time-consuming manual data processing and resource inefficiencies in life cycle analysis.
Innovation Solution
Utilizing machine learning-based life cycle analysis techniques that incorporate large language models to automate and streamline the generation of life cycle analysis maps, providing comprehensive data interpretation and visual representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data processing methods are used for life cycle analysis, then data accuracy can be maintained through human review, but the analysis time and resource consumption increase significantly
Solution Approach 1:
The patent introduces machine learning models as an intermediary between raw sustainability data and life cycle analysis results. The ML models automatically process and interpret diverse data sources (enterprise facility data, financial data, GHG emission data), generating standardized sustainability reports that feed into the life cycle analysis workflow, thereby reducing manual processing time while maintaining accuracy through algorithmic consistency
Solution Approach 2:
The patent replaces manual mechanical data processing with automated machine learning-based processing systems. The ML models automatically aggregate, clean, and interpret sustainability data from multiple sources, substituting human manual review with automated computational processes that reduce time consumption while maintaining or improving data accuracy through systematic processing
2Loss of information
If comprehensive sustainability data from multiple sources is collected, then the completeness of life cycle analysis improves, but the complexity of data integration and processing increases
Solution Approach 1:
The patent implements a universal machine learning framework that handles multiple types of sustainability data (enterprise facility data, financial data, GHG emission data) through a single integrated processing system. The ML models are designed to universally process diverse data formats and sources, automatically adapting to different data types and integrating them into a cohesive sustainability assessment, thereby reducing the complexity of data integration while maintaining completeness
Solution Approach 2:
The patent transforms diverse sustainability parameters from different data sources into standardized formats through machine learning processing. The ML models automatically normalize and harmonize various data parameters (emission rates, energy consumption, waste generation) into consistent units and structures, making integration simpler while preserving the completeness of the original data through systematic parameter transformation
3Productivity
If automated machine learning methods are used for data processing, then processing speed and efficiency improve, but the complexity of the system increases
Solution Approach 1:
The patent implements self-service machine learning models that automatically process sustainability data without requiring extensive manual configuration or intervention. The ML models autonomously aggregate data from multiple sources, handle data cleaning and validation, generate sustainability reports, and identify improvement opportunities, thereby achieving high processing efficiency while minimizing the operational complexity through automated self-management
Solution Approach 2:
The patent incorporates preliminary action by pre-training and configuring machine learning models with sustainability data processing capabilities before deployment. The ML models are pre-loaded with knowledge of sustainability metrics, data relationships, and analysis methodologies, enabling them to immediately process incoming data efficiently without requiring complex real-time configuration, thus reducing operational system complexity while maintaining high productivity
Data Source
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AI summary
The embodiments presented herein include systems and methods for machine learning based life cycle assessment (LCA). For example, a method includes receiving a first set of inputs from one or more industry standard organizations, wherein the first set of inputs comprise data relating to LCA of greenhouse gas (GHG) emissions; using a first large language model (LLM) to generate a canonical mapping structure based on the first set of inputs; receiving a second set of inputs from one or more industrial organizations, wherein the second set of inputs comprise activity data relating to one or more industrial activities performed by the one or more industrial organizations; using a second LLM to output a dynamic mapping classifier based on the second set of inputs relative to the canonical mapping structure generated by the first LLM; and generating and presenting a visualization dashboard of outputs from the dynamic mapping classifier.