Industrial Emission Optimization Using Real-Time Closed-Loop Control
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Solution Overview
Problem
Traditional industrial control systems rely on historical data and static measures for managing emissions, leading to inefficient and undesirable execution of industrial processes due to inaccuracies in emissions calculations and failure to account for internal and external uncertainties, resulting in misalignment with performance goals and regulatory compliance.
Innovation Solution
A system utilizing real-time data and models for closed-loop, automated emission optimization, integrating multi-variable planning algorithms to optimize emissions across industrial processes, incorporating real-time and static data, and employing machine learning models to adapt to changing conditions and uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional industrial control systems use historical data and static control measures to manage emissions, then the system complexity is low and ease of operation is maintained, but the measurement precision of emissions calculations deteriorates and reliability of emissions management worsens
Solution Approach 1:
The patent transitions from static control measures to dynamic optimization by implementing real-time emission optimization models that continuously adjust operational parameters based on current process conditions, measurement data, and changing constraints, thereby improving emissions calculation accuracy without permanent system complexity
Solution Approach 2:
The system implements closed-loop feedback mechanisms where real-time measurement data from industrial processes is fed into optimization models that generate operational modifications, which are then applied and monitored continuously, creating a self-correcting system that improves measurement precision through iterative refinement
2Reliability
If traditional systems rely on theoretical calculations and emission estimates, then the ease of operation is maintained, but the reliability of emissions management deteriorates
Solution Approach 1:
The optimization system performs self-service by automatically collecting measurement data, processing it through emission optimization models, generating operational modifications, and implementing corrections without requiring manual intervention, thereby achieving high reliability while maintaining operational simplicity through automation
Solution Approach 2:
The patent replaces manual emissions management methods with automated computational models and algorithms that process data and generate optimization recommendations, substituting human-operated mechanical systems with intelligent software-based systems that improve reliability
3Productivity
If real-time optimization models are implemented with multi-variable planning algorithms, then the productivity of emissions optimization improves, but the device complexity increases
Solution Approach 1:
The complex optimization system is segmented into distinct functional modules including data collection components, emission optimization models, constraint management modules, and implementation interfaces, allowing each segment to be developed, maintained, and scaled independently while maintaining overall system productivity
Data Source
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AI summary
Various embodiments described herein relate to providing emission optimization for industrial processes. In this regard, a set of emission constraints associated with emission optimization for an industrial domain related to one or more industrial processes that produce one or more industrial process products is determined. Additionally, an emission optimization model is configured based at least in part on the set of emission constraints and at least one other non-emission constraint. In response to receiving an emission optimization request to optimize carbon emissions related to the one or more industrial processes, the emission optimization model is applied to real-time measurement data associated with the one or more industrial processes to determine one or more operational modifications for the one or more industrial processes that at least satisfy the set of emission constraints and optimize the at least one non-emission constraint.