Chemical Process Prediction Using Reaction Rate Causality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing prediction systems face challenges in improving accuracy for processes involving reactions in chemical plants, particularly due to non-linear influences such as temperature, making it difficult to predict process data features like peak, difference, and integral values.
Innovation Solution
A prediction apparatus that processes data from chemical plants using causality information to generate a prediction model, incorporating reaction rate values as explanatory variables, and employing methods like the Arrhenius equation to determine frequency factors and activation energy, along with hierarchical prediction equations and sample size reduction techniques to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction methods using peak values, difference values, or integral values are used for chemical plant processes, then the prediction model can be constructed, but the prediction accuracy deteriorates due to non-linear influences such as temperature on reaction processes
Solution Approach 1:
The patent transforms the prediction approach by changing the fundamental parameter from conventional statistical values (peak, difference, integral) to reaction rate values that directly represent the chemical process dynamics. This parameter change allows the model to capture non-linear temperature influences while maintaining predictive accuracy.
Solution Approach 2:
The patent introduces reaction rate values as an intermediary variable that mediates between process conditions (such as temperature) and product characteristics. This intermediary captures the non-linear relationship in chemical reactions, enabling accurate prediction without requiring complex direct modeling of all non-linear effects.
2Measurement precision
If reaction rate values are used as explanatory variables to improve prediction accuracy, then the prediction accuracy improves, but the data processing complexity increases due to the need for advanced methods like Arrhenius equation and sample size reduction
Solution Approach 1:
The patent applies sample size reduction methods as a preliminary action to condense large volumes of process data into representative values before generating reaction rate values. This preliminary data processing simplifies subsequent calculations and reduces computational complexity while preserving essential process information.
Solution Approach 2:
The patent uses the Arrhenius equation to transform temperature and reaction time data into reaction rate values, changing the parameter representation to one that naturally captures non-linear chemical behavior. This transformation simplifies the modeling process by providing a physically meaningful parameter that directly relates to reaction dynamics.
3Ease of manufacture
If conventional statistical values (peak, difference, integral) are used for prediction, then the data processing is simpler, but the prediction accuracy deteriorates in processes with non-linear reactions influenced by temperature
Solution Approach 1:
The patent fundamentally changes the prediction parameter from conventional statistical measures to reaction rate values derived from process data. This parameter change enables the model to inherently capture non-linear temperature effects and reaction dynamics, significantly improving prediction accuracy for chemical processes.
Solution Approach 2:
The patent introduces reaction rate values as an intermediary that bridges process conditions and product characteristics. This intermediary variable naturally incorporates non-linear relationships through its derivation from process data, providing accurate predictions without requiring complex direct modeling of non-linear effects.
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 proposed solution significantly improves the accuracy of predicting characteristic values in chemical plant processes by effectively utilizing reaction rate data and advanced data processing techniques.
Implementation Method 1
the value corresponding to the reaction rate may be calculated by using an Arrhenius equation
Data Source
AI summary
To improve prediction accuracy of a process including a reaction in a chemical plant. A prediction apparatus includes a process data processing unit that performs a predetermined processing process on process data obtained from a chemical plant, and a prediction model generation unit that generates a prediction model having learned features of the process data obtained from the chemical plant, on the basis of causality information that defines a combination of first process data and second process data or a value corresponding to the second process data among the process data obtained from the chemical plant or the process data processed by the process data processing unit. The first process data is used as an explanatory variable. The second process data or the value corresponding to the second process data is used as a response variable. Furthermore, the process data processing unit obtains a value corresponding to a reaction rate of a processing target in a predetermined period by using the process data.