Emissions Source Simulation Using Gradient Descent Under Constraints
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Solution Overview
Problem
Existing systems are unable to efficiently and accurately monitor and model emissions from large numbers of sources while predicting future emissions under various constraints, due to the complexity and emergent nature of emissions standards and reporting.
Innovation Solution
The use of a multi-variable objective algorithm, such as a modified gradient descent model, to generate action recommendations for modifying physical emissions sources by simulating different scenarios and determining modified target emissions values based on probability distributions representing source attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems are used to monitor emissions from large numbers of sources, then emissions measurement capability is limited, but system complexity and inability to model future emissions under multiple constraints increases
Solution Approach 1:
The patent introduces an emissions modeling system as an intermediary layer between raw emissions data and regulatory compliance analysis. This system uses surrogate models and machine learning algorithms to mediate the complex relationships between multiple emissions sources, operational parameters, and regulatory constraints, enabling accurate emissions prediction without requiring direct monitoring of every individual source.
Solution Approach 2:
The patent creates simplified surrogate copies of complex emissions sources and their relationships. Instead of directly modeling every physical emissions source with full complexity, the system generates computational surrogates that replicate the essential emissions behavior under various operational conditions, enabling efficient simulation and analysis of future emissions scenarios.
2Reliability
If conventional systems attempt to model future emissions under multiple constraints, then emissions prediction capability is insufficient, but computational complexity and processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-training surrogate models using historical emissions data and operational parameters before actual emissions prediction is needed. This advance preparation creates ready-to-use computational models that can quickly predict future emissions under various constraints without requiring complex real-time calculations, significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent transforms the emissions modeling problem by changing parameters from direct physical measurements to surrogate variables that capture the essential emissions behavior. By using dimensionless parameters and normalized operational variables, the system reduces computational complexity while preserving the ability to accurately predict emissions under multiple constraints and scenarios.
3Adaptability or versatility
If the number of emissions sources and constraints increases, then comprehensive emissions monitoring capability improves, but system complexity and difficulty of determination increases
Solution Approach 1:
The patent creates a universal emissions modeling framework that can handle multiple types of emissions sources, various operational constraints, and different regulatory scenarios through a single integrated system. The surrogate modeling approach and standardized computational methods enable the system to adaptively analyze diverse emissions problems without requiring separate specialized models for each source type or constraint, reducing overall system complexity.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for modifying physical emissions sources based on a plurality of simulations of different scenarios utilizing a modified gradient descent model. Specifically, the disclosed system utilizes the modified gradient descent model to generate emissions value modifications for physical emissions sources corresponding to an entity based on a set of constraints and target emissions values. The disclosed system runs a plurality of simulations to generate modified target emissions values, utilizing the modified gradient descent model, by modifying source attributes of the physical emissions sources according to a plurality of probability distributions representing source attributes of the physical emissions sources. The disclosed system then compares the initial target emissions values to the modified target emissions values determined from the simulations to generate action recommendations for modifying the physical emissions sources.