Selective Catalytic Reduction Control With ML Reactant Dosing
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Solution Overview
Problem
Existing selective catalytic reduction systems face challenges in optimizing reactant flow, leading to issues such as reactant slip and incomplete conversion of input compounds due to latency in feedback mechanisms and inability to adapt to changing system behaviors over time.
Innovation Solution
A machine-learned model is employed to predict optimal reactant flow based on past performance data, using self-adjusting control systems and continuous recalibration to adapt to system changes, reducing latency and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feedback control mechanisms are used to optimize reactant flow, then system stability is maintained, but latency in feedback response causes suboptimal reactant dosing leading to reactant slip or incomplete conversion
Solution Approach 1:
The machine learning model predicts optimal reactant flow rates in advance based on operating conditions, eliminating the need to wait for feedback from emissions sensors. The model proactively determines the correct dosing amount before the catalytic reduction process occurs, resolving the latency issue inherent in traditional feedback control.
Solution Approach 2:
The patent replaces traditional mechanical feedback control systems with a data-driven machine learning model. Instead of relying on physical feedback loops with inherent delays, the system uses computational algorithms to predict optimal reactant flow, substituting mechanical control with intelligent software-based control.
2Adaptability or versatility
If fixed reactant dosing strategies are used, then system operation is simple, but the system cannot adapt to changing behaviors over time leading to increased emissions
Solution Approach 1:
The machine learning model is designed to dynamically adapt to changing system conditions and behaviors over time. The model can learn from new data and adjust its predictions accordingly, transforming a static dosing system into a dynamic one that evolves with changing operating conditions without requiring manual reconfiguration.
Solution Approach 2:
The system performs self-optimization through the machine learning model, which automatically adjusts reactant dosing strategies based on learned patterns from operational data. This eliminates the need for external intervention or complex manual tuning, allowing the system to adapt itself autonomously to changing conditions.
3Productivity
If excessive reactant is provided to ensure complete conversion, then conversion efficiency is improved, but reactant slip increases causing emissions compliance issues
Solution Approach 1:
The machine learning model optimizes the reactant flow rate parameter dynamically based on specific operating conditions. Instead of using fixed or excessive dosing, the model adjusts the reactant amount parameter to achieve complete conversion while minimizing excess, thereby reducing reactant slip emissions while maintaining high conversion efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides rapid prediction of optimal reactant flow, reducing emissions and regulatory compliance costs by minimizing reactant slip and NOx emissions, with lower computational and data requirements.
Implementation Method 1
a reactant (e.g., ammonia, urea, etc.) can be used to convert one or more input compounds (e.g., gases such as NO, NO2, etc.) into one or more output compounds (e.g., common atmospheric gases such as pure nitrogen, water vapor, etc.)
Data Source
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AI summary
A method includes obtaining, by a computing system comprising a machine-learned model (104), input data comprising one or more input values (102). The method includes generating, by the machine-learned model (104) based on the input data (102), output data (106) indicative of an amount (106) of a reactant (110). The method includes providing a signal, by the computing system, to cause the amount (106) of the reactant (110) to be provided to a selective catalytic reduction system (112).