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Comparison of support vector regression- and neural network-based soft sensors for cement plant exhaust gas composition

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Abstract

Application of accurate gas sampler and analyzers is the most reliable method for measuring cement kiln stack gas composition; however, both are very prone to frequent maintenance due to the presence of very fine dust particles in kiln stack gas. Reliable soft sensors are necessary for online concentration estimation of required components during down time of sampler or analyzer. Since accurate first principal models for cement kiln are very complex and time consuming to solve, artificial neural networks and support vector regression are applied as modeling tools. These tools are used to develop four soft sensors for prediction of O2, CO, NO and CH4 in kiln stack gas. A data set consisting of 29,600 data points on 25 process variables collected during a period of 7 month is used for soft sensor development. To have a meaningful comparison on performance, the same methods for plant data processing, feature variable selection and model optimization are applied for both artificial neural networks- and support vector regression-based soft sensors. Refined data set after data processing is divided into training, test and validation groups that contain 70%, 10% and 20% of data set, respectively. Both average absolute error and average absolute relative error calculated for soft sensor predictions revealed that support vector regression-based soft sensors are more accurate compared to corresponding artificial neural networks-based soft sensors.

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Abbreviations

AAE:

Average absolute error

AARE:

Average absolute relative error

AE:

Absolute error

ANN:

Artificial neural networks

DG:

Dividing gate

EP:

Electrostatic precipitator

GA:

Genetic algorithm

HAD:

Hoisting air damper

ID:

Induced draft

KB:

Kiln burner

MAD:

Median absolute deviation

PCA:

Principal component analysis

RBF:

Radial basis function

SVR:

Support vector regression

b :

Bias value

b j :

Bias

C :

Regularizing parameter

F :

Function

f j :

Activation function

J :

Objective function

\(K\left( {x_{i} ,x_{j} } \right)\) :

Kernel function

n :

Number of data point

R :

Reference

s j :

Weighted sum

X :

Data matrix

\(x_{i}\) :

Feature vector

\(\tilde{x}_{{{\text{Exp}}.}}\) :

Normalized value

\(x_{\hbox{min} }\) :

Minimum value

\(x_{\hbox{max} }\) :

Maximum value

\(x_{{{\text{Exp}}.}}\) :

Experimental data

\(\bar{x}\) :

Mean

Y :

Future outputs

YΣ:

Transformed data matrix

y i :

Target value

σ:

Standard deviation

φ:

Transformation of feature vector

\(\xi_{i}^{{}}\) :

Slack variable

\(\xi_{i}^{ *}\) :

Slack variable

\(\alpha_{i}^{{}}\) :

Lagrange multipliers

\(\alpha_{i}^{ *}\) :

Lagrange multipliers

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Acknowledgements

Financial support from the Vice Chancellor for Research Affairs of Shiraz University is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Chemical Engineering, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz, 71345, Iran

    A. Khosrozade & N. Mehranbod

Authors
  1. A. Khosrozade
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  2. N. Mehranbod
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Correspondence to N. Mehranbod.

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Editorial responsibility: J Aravind.

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Khosrozade, A., Mehranbod, N. Comparison of support vector regression- and neural network-based soft sensors for cement plant exhaust gas composition. Int. J. Environ. Sci. Technol. 17, 2865–2874 (2020). https://doi.org/10.1007/s13762-019-02564-4

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  • DOI: https://doi.org/10.1007/s13762-019-02564-4

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