Reactor Kinetic Linear Models for Accurate Yield Forecasting
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Solution Overview
Problem
Existing methods for calculating linear process models in chemical plants, petrochemical plants, and refineries introduce errors that affect the accuracy of yield forecasting, necessitating the development of more precise models.
Innovation Solution
A system and method utilizing reactor kinetic equations, sensors, and data analysis platforms to generate linear process models based on real-time yield and composition data, feed properties, and processing conditions, optimizing reaction rate coefficients to minimize deviation and enhance forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods for calculating linear process models are used, then the calculation process is simple, but the accuracy of yield forecasting deteriorates
Solution Approach 1:
The patent transforms the traditional linear process model parameters by incorporating reactor kinetic equations and reaction rate coefficients. This changes the fundamental parameters from simple empirical relationships to scientifically-based kinetic parameters, thereby improving yield forecasting accuracy while maintaining computational feasibility through systematic calculation procedures
Solution Approach 2:
The patent introduces reaction rate coefficients as intermediary parameters that bridge the gap between feed composition and yield outcomes. These coefficients serve as mediators that capture the complex chemical transformation processes, enabling more accurate forecasting without requiring direct complex simulation of all reaction pathways
2Productivity
If linear process models are used to forecast yield data, then operational efficiency is improved, but errors in model calculation reduce reliability
Solution Approach 1:
The patent implements a feedback mechanism where the linear process model continuously receives updated feed composition data and operating conditions, recalculates reaction rate coefficients, and adjusts yield forecasts accordingly. This closed-loop approach ensures that the model adapts to changing conditions and maintains reliability while supporting operational efficiency
Solution Approach 2:
The patent performs preliminary calculation of reaction rate coefficients based on feed composition and operating conditions before actual processing occurs. This advance preparation allows the system to have reliable yield forecasts ready before production decisions are made, enhancing both reliability and operational efficiency
Data Source
AI summary
Methods, systems, and apparatuses for developing linear process models to improve performance of components that make up operations in a plant are described herein. In some arrangements, a system may leverage one or more sensors and/or measurement devices to identify rates and compositions of feed and yield. The system may use one or more stoichiometric matrices and/or differential equations to identify molar and mass solutions for each feed component and predict the yield for reaction rates on a component-by-component basis. The system may further adjust the reaction rate coefficients to minimize the deviation between the yield results and the yield identified by system sensors and/or measuring devices. The resulting linear process models may be utilized to optimize plant processes in order to minimize reaction waste and maximize reaction yield.