Catalyst State Estimation via State Space Model
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Solution Overview
Problem
Existing methods for estimating catalyst properties in process control systems are time-consuming, unreliable, and expensive, as they typically require laboratory analysis of extracted catalyst samples, which are difficult to represent accurately due to their particle distribution nature and influence by various deactivation and poisoning mechanisms.
Innovation Solution
Incorporating hidden catalyst properties into a state space model and estimating them using measurable inputs and outputs, employing methods like Kalman filtering or sequential Monte Carlo techniques to solve for the hidden states, allowing for real-time and cost-effective estimation of catalyst properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laboratory analysis of extracted catalyst samples is used to estimate catalyst properties, then measurement precision may be improved, but loss of time and cost increase significantly
Solution Approach 1:
The patent replaces the mechanical/physical system of extracting catalyst samples and performing laboratory analyses with a computational/mathematical system. A state space model with Kalman filtering or sequential Monte Carlo methods estimates catalyst properties directly from process data, eliminating the need for physical sample extraction and laboratory testing, thereby resolving the contradiction between measurement precision and time loss
Solution Approach 2:
The patent introduces a state space model as an intermediary between measurable process inputs/outputs and hidden catalyst properties. This mathematical model acts as a mediator that translates easily measurable process variables into accurate estimates of catalyst state without requiring direct catalyst sampling, thus resolving the time-loss contradiction while maintaining precision
2Measurement precision
If laboratory analysis of extracted catalyst samples is used to estimate catalyst properties, then measurement precision may be improved, but cost increases significantly
Solution Approach 1:
The patent replaces expensive laboratory analysis infrastructure and procedures with a computational approach using state space models and filtering algorithms. This substitution eliminates costs associated with sample extraction equipment, laboratory testing, and manual analysis while maintaining or improving measurement precision through mathematical estimation
Solution Approach 2:
The patent creates a virtual copy or representation of the catalyst state through the state space model rather than physically handling actual catalyst samples. This virtual modeling approach provides the same information value as physical analysis at a fraction of the cost, resolving the contradiction between precision and manufacturing cost
3Measurement precision
If catalyst properties are represented as distributions rather than single values, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex problem of catalyst property estimation by representing catalyst state as a distribution across multiple discrete states or bins rather than a single continuous value. This segmentation allows the complex distribution to be handled through a series of simpler probability transitions in the state space model, resolving the contradiction between precision and complexity
Solution Approach 2:
The patent introduces dynamic state space models that evolve over time, where catalyst property distributions are updated recursively as new process data becomes available. This dynamic approach allows the complex distribution to be managed through sequential updates rather than simultaneous computation of all parameters, reducing effective complexity while maintaining precision
4Productivity
If hidden catalyst properties are included in state space model, then productivity is improved through real-time estimation, but device complexity increases
Solution Approach 1:
The patent replaces complex physical monitoring equipment that would be needed to directly measure hidden catalyst properties with a computational state space model. This substitution achieves real-time estimation capability through software-based filtering algorithms rather than hardware-based direct measurement, improving productivity while managing complexity through mathematical rather than mechanical means
Solution Approach 2:
The patent implements feedback mechanisms where the state space model continuously compares predicted catalyst states with actual process measurements and adjusts estimates accordingly. This feedback loop enables real-time adaptation and correction, improving productivity through accurate real-time estimation while managing complexity through systematic error correction rather than requiring overly complex predictive models
Data Source
AI summary
In a process control system, the hidden properties of a catalyst are estimated by including those properties in hidden states within a state space model and solving the state space model based on measurable inputs and outputs of the process. The process may include defining a state space model for a process having a catalyst state comprising a hidden catalyst property; defining a set of empirically measurable input variables for the state space model, defining a set of output variables for the state space model, measuring a set of input values corresponding to the set of input variables; measuring a set of output values corresponding to the set of output variables; and estimating the hidden catalyst property based on the input values, the output values, and the state space model.