Closed-Loop Decarbonization Pathways Under Multi-Variable Constraints
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Solution Overview
Problem
Organizations face challenges in determining an optimal decarbonization pathway due to complex and ever-changing data and models associated with carbon emissions reduction, leading to inefficient capital deployment and ineffective decarbonization efforts.
Innovation Solution
A computer-implemented method and system that utilizes multiple input variables, constraints, and system feedback to generate an optimization pathway for decarbonization, considering static and dynamic configuration inputs, uncertainty models, and real-time data to optimize carbon emissions across various assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis methods are used for decarbonization planning, then the process is simpler, but the accuracy and effectiveness of the decarbonization pathway is insufficient
Solution Approach 1:
The system segments the decarbonization analysis into multiple independent modules: data collection module, uncertainty modeling module, optimization model module, and pathway generation module. Each module handles specific aspects of the analysis, allowing complex multi-variable optimization to be broken down into manageable components while maintaining high accuracy through systematic processing of each segment.
Solution Approach 2:
The patent introduces an intermediary optimization model that acts as a mediator between raw input data and decarbonization pathways. This intermediary layer processes uncertain inputs through uncertainty models and transforms them into optimized pathways, enabling accurate decision-making without directly handling the full complexity of raw data.
2Adaptability or versatility
If multiple variables and constraints are considered in the optimization model, then the decarbonization pathway is more accurate, but the computational complexity increases
Solution Approach 1:
The optimization model dynamically adjusts its parameters and constraints based on input conditions. The system accepts variable inputs including uncertain data with associated uncertainty models, and dynamically configures the optimization parameters (such as weightings for different objectives like cost, emissions, and time) to adapt to specific organizational needs and constraints, making the model versatile without requiring separate models for each scenario.
Solution Approach 2:
The system changes parameters within the optimization model based on input conditions and uncertainty levels. By adjusting optimization parameters such as objective function weights, constraint thresholds, and uncertainty tolerance levels, the model can adapt to different scenarios and constraints while maintaining computational efficiency through parameter tuning rather than structural changes.
3Reliability
If uncertainty models are incorporated into the input data, then the reliability of the optimization pathway is improved, but the data processing complexity increases
Solution Approach 1:
The system performs preliminary processing of uncertain input data by attaching uncertainty models to input variables before the optimization process begins. This preliminary action characterizes the uncertainty (such as probability distributions or confidence intervals) of input data, allowing the optimization model to account for uncertainty without dealing with raw unprocessed uncertain data during the optimization computation, thereby improving reliability while managing complexity.
4Reliability
If real-time observed values are used to update the optimization pathway, then the pathway remains current and effective, but the computational load increases
Solution Approach 1:
The system updates the optimization pathway periodically based on observed values rather than continuously. The closed-loop approach allows the system to generate an initial optimization pathway, implement it, then periodically update the pathway based on observed performance data and changing conditions. This periodic updating maintains pathway currentness and reliability while avoiding the continuous computational load of real-time updates.
Data Source
AI summary
Embodiments of the present disclosure provide for generating improved, feasible, and optimized pathways for optimizing operation of an industrial plant or component thereof. Embodiments utilize a multi-variate optimization model that may utilize real-time data and any number of available dynamic and static configurations to optimize for multiple optimization parameters. The multi-variate optimization model outputs an optimization pathway, for example including any number of transformation action(s) representing decarbonization step(s), that enable configuration of the plant or processing unit(s) thereof in a manner that optimizes operations of the plant or processing unit(s). For example, the optimization pathway in some contexts is optimized to generate a pathway that reduces the impact of emissions generated by the plant or processing unit(s) in a feasible manner that is most cost efficient for a particular plant in a particular location in consideration with any number of other constraints or considerations.