Real-Time Emission Optimization for Industrial Process 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 misaligned performance goals and inefficient operations.
Innovation Solution
A system utilizing real-time data and automated, closed-loop emission optimization models that integrate multi-variable planning algorithms to optimize carbon emissions by applying emission optimization models to real-time measurement data, incorporating internal and external factors, and adjusting operational parameters to satisfy emission constraints and objectives.
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 productivity decreases
Solution Approach 1:
The patent transforms static control measures into dynamic optimization by implementing a multi-variable planning algorithm that continuously adjusts operational parameters based on real-time emissions measurements. The system dynamically modifies process variables such as temperature, pressure, and flow rates to optimize emissions performance while maintaining productivity.
Solution Approach 2:
The system implements closed-loop feedback control where emissions are measured in real-time using sensors, the data is fed into the optimization model, and the model generates corrective actions that are applied back to the industrial process. This continuous feedback loop significantly improves emissions calculation accuracy and enables adaptive optimization.
2Reliability
If traditional systems rely on theoretical calculations and emission estimates, then the device complexity remains low, but the reliability of emissions management deteriorates and loss of information increases
Solution Approach 1:
The patent replaces theoretical calculation methods with actual physical measurements using emissions sensors and analytical instruments. Instead of relying on estimated models, the system directly measures emissions concentrations and uses these empirical data points to drive optimization decisions, significantly improving reliability.
Solution Approach 2:
The system changes the fundamental parameter from estimated emission values to measured emission concentrations. By transitioning from theoretical parameters to empirically measured parameters, the reliability of emissions management is enhanced while the system uses optimization algorithms to manage the increased complexity.
3Adaptability or versatility
If static control measures are used to manage emissions, then the ease of operation is maintained, but the adaptability to internal and external uncertainties deteriorates and loss of time increases
Solution Approach 1:
The patent implements dynamic adaptability by continuously monitoring emissions and automatically adjusting operational parameters in response to changing conditions. The multi-variable planning algorithm adapts to internal process variations and external factors such as feedstock changes or regulatory requirements, eliminating the need for manual reconfiguration.
Solution Approach 2:
The optimization system performs self-adjustment by automatically generating and implementing corrective actions based on real-time emissions data. The system serves itself by identifying optimization opportunities, calculating optimal parameter adjustments, and executing changes without requiring continuous human intervention, thereby maintaining ease of operation while enhancing adaptability.
4Productivity
If traditional industrial processes are executed without real-time optimization, then the device complexity is low, but the productivity and use of energy deteriorate
Solution Approach 1:
The patent optimizes productivity by dynamically changing operational parameters such as process temperature, pressure, flow rates, and residence time based on real-time emissions measurements. The multi-variable planning algorithm identifies optimal parameter combinations that maximize productivity while meeting emissions targets, thereby improving overall process efficiency.
Solution Approach 2:
The optimization system serves multiple functions simultaneously: it manages emissions, optimizes productivity, reduces energy consumption, and ensures compliance with regulations. By integrating these functions into a single unified optimization platform, the system achieves productivity improvements without proportionally increasing complexity.
Data Source
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.