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Automatic Modeling for Online Process

(combined with Process NIR & Process NMR)

(정량분석 자동 모델링 소프트웨어)

Inteligent Model Gateway Software

System Architecture

What is Model Gateway software?


The Model Gateway software is used for automating the modeling process. All the user has to do is load Lab Data into the software. The rest is done automatically.


In a process environment, the Model Gateway software stands between the Analyzer and the plant DCS (Distributed Control System). 


How Model Gateway works automatically?

1. Installation


The ModelGateway software can be installed on any Windows Machine. It connects to any analyzer and handles all modeling tasks. From model building to Signal Processing, Prediction and Reporting.

2. Lab Value input

Lab values can be loaded into the system through Modbus communication or manually from the form displayed below. values are copied from an Excel spreadsheet, just use Ctrl-C & Ctrl-V to paste them.

3. Auto-Model builder


The modeling process runs an algorithm that searches for the best training set, under the limitation of the number of iterations and maximum number of factors.

Once the modeling process is completed, a new model file is created and saved to disk and the new model will be implemented automatically to predict process values.

3. Comparing a Model to Lab data


If you load lab data into the database for an analyzed property, the application displays a comparison between the lab data and the model predictions. This is useful to get a feeling of how accurate the model is and whether it is time to run another modeling iteration.


  • No User intervention required

  • Easy model maintenance

  • Fast Response to changes

  • Over Fitting is improved

  • Easy comparison between Process value vs. Lab value

  • Sililar interface for multiple analyzers

Linear regression

The ModelGateway Solution

Linear Modeling is used in the industry to define the relationship between an acquired spectrum and the Lab Value used as reference. This relationship enables predicting the expected value from an unknown spectrum.

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