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Related Experiment Video

Updated: Jul 6, 2025

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

A new prediction method for sodium aluminate solution evaporation integrating process knowledge and data-driven

Sen Xie¹, Yuyang Hua², Zhijiang Lou¹

¹Institute of Intelligence Science and Engineering, Shenzhen Polytechnic University, Shenzhen 518060, PR China.

ISA Transactions

|January 6, 2024

Summary

This study introduces a novel prediction method for alumina production, integrating process knowledge and data-driven models. The new approach enhances process control accuracy and reduces energy consumption for greener manufacturing.

Keywords:

Concentration prediction Data reconciliation Error compensation Evaporation process Integrated modeling

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Last Updated: Jul 6, 2025

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Published on: June 8, 2015

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Area of Science:

Chemical Engineering
Process Control
Materials Science

Background:

Alumina production's evaporation process is crucial for resource recycling and pollution reduction.
Current offline quality assessment leads to imprecise process control and high energy use.
Need for enhanced prediction methods to ensure green and efficient alumina production.

Purpose of the Study:

To develop and validate a novel prediction method for alumina production.
To integrate process knowledge with a data-driven spatial-temporal adaptive model.
To improve process control precision and reduce energy consumption in evaporation processes.

Main Methods:

Data reconciliation technology for preprocessing production data.
Mechanism modeling based on material and heat transfer principles.
Error compensation using double locally weighted kernel PLS with time difference and moving window models.
Integration of data-driven and process knowledge-based models.

Main Results:

The integrated model demonstrated feasibility in an industrial sodium aluminate solution evaporation case.
Achieved prediction accuracy exceeding 90% within a ±2% error range.
Effectively estimated actual product quality, enhancing prediction reliability.

Conclusions:

The developed hybrid model offers a robust solution for accurate quality prediction in alumina production.
This approach contributes to achieving green and efficient manufacturing practices.
The method effectively addresses limitations of traditional offline quality assessment.