Chemical Plant Production Prediction Using Sensor Data Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Chemical plants face challenges in accurately predicting production rates of chemical products in real-time due to the complexity and chaotic nature of their processes, making it difficult to establish deterministic simulative models using traditional methods, even with extensive sensor data.
Innovation Solution
The implementation of machine learning algorithms that utilize historical production and sensor data to develop predictive models, such as the Production Index (PI) model, which interpolates and filters data to provide accurate real-time predictions by identifying correlations between sensor parameters and production rates, and optimizes controllable parameters for maximizing production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional simulative techniques are used to predict production data, then the model can be established based on physical principles, but the prediction accuracy is insufficient due to the complexity and chaotic nature of chemical plant processes
Solution Approach 1:
The patent replaces traditional physics-based simulative models with machine learning algorithms. Instead of using deterministic physical equations to model chemical plant processes, the system employs data-driven approaches including neural networks, support vector machines, and random forests to predict production data, achieving higher accuracy without requiring explicit physical model formulation
Solution Approach 2:
The patent transforms the modeling approach by changing from fixed physical parameters in traditional models to adaptive learned parameters in machine learning models. The system continuously learns optimal parameters from historical data, allowing the model to adapt to changing plant conditions and improve prediction reliability
2Measurement precision
If extensive sensor data is collected to improve prediction accuracy, then more information is available for modeling, but the data contains noise and abnormalities that reduce prediction quality
Solution Approach 1:
The patent extracts and removes noise and abnormality components from sensor data before feeding it into prediction models. Data preprocessing steps include outlier detection, noise filtering, and validation mechanisms that separate useful signal from harmful noise, improving the quality of input data for machine learning algorithms
Solution Approach 2:
The patent introduces intermediary data processing layers between raw sensor data and prediction models. These intermediaries include data cleaning modules, normalization layers, and feature selection mechanisms that mediate the transformation of raw data into high-quality input features, enhancing prediction accuracy
3Reliability
If many sensor parameters are used in the predictive model, then the model captures more process variables, but the computational complexity and data processing requirements increase significantly
Solution Approach 1:
The patent segments the large set of sensor parameters into relevant and irrelevant groups using feature selection techniques. Machine learning algorithms identify and retain only the most influential parameters for prediction, discarding redundant features. This segmentation reduces computational complexity while maintaining prediction accuracy by focusing on critical process variables
Data Source
AI summary
Direct measurement and simulation of real-time production rates of chemical products in complex chemical plants is complex. A predictive model developed based on machine learning algorithms using historical sensor data and production data provides accurate real-time prediction of production rates of chemical products in chemical plants. An optimization model based on machine learning algorithms using clustered historical sensor data and production data provides optimal values for controllable parameters for production maximization.