ARTIFICIAL NEURAL NETWORK AND ITS APPLICATION IN CHEMICAL ENGINEERING

PRESENTED BY : ATANU KUMAR PAUL 11/CHE/411

GUIDED BY : PROFESSOR K. C. GHANTA

[1] Tambe, S. S., B. D. Kulkarni, P. B. Deshpande, Elements of Artificial Neural

Networks with Selected Applications in Chemical Engineering, and Chemical And

Biological Sciences, Simulation & Advanced Controls, Inc., USA, (1996)

What is ANN ?

An artificial neural network (ANN), usually called neural network (NN).

It is a mathematical model or computational model that is inspired by the structure and functional

aspects of biological neural networks. [1]

A neural network consists of an interconnected group of artificial neurons, and it processes

information using a connectionist approach to

computation.

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In most cases an ANN is an adaptive system that changes its structure based on external

or internal information that flows through

the network during the learning phase.

Modern neural networks are non-linear statistical data modeling tools. They are

usually used to model complex

relationships between inputs and outputs or

to find patterns in data.

CONTD......

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The original inspiration for the term Artificial Neural Network came from examination of central

nervous systems and their neurons, axons,

dendrites, and synapses, which constitute the

processing elements of biological neural networks

investigated by neuroscience.

In an artificial neural network, simple artificial nodes, variously called "neurons", "neurodes",

"processing elements" (PEs) or "units", are

connected together to form a network of nodes

similar to the biological neural networks hence

the term "artificial neural network".

Background of ANN

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Biological Neurons

The cell itself includes a nucleus (at the center).

From cell 2, the dendrites provide input signals to the cell 1.

From cell 1, the axon sends output signals to cell 2 via the axon terminals. These axon terminals merge with the dendrites of cell 2.

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A GENERAL ANN Architecture

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McCulloch and Pitts (1943): The model they created had two inputs and a single output. They noted that a neuron

would not activate if only one of the inputs was active.

Rosenblatt (1958): He developed perceptron. Selfridge (1958): Brought the idea of the weight space to

the perceptron.

Widrow and Hoff (1960): Developed a mathematical method for adapting the weights.

Werbos (1974): First developed the back propagation algorithm.

Anderson and Rosenfeld (1987): The electrochemical process of a neuron works like a voltage-to-frequency

translator

Kaastra and Boyd (1996): Developed neural network model for forecasting financial and economic time series.

History of Neural Networks

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Recent Development in ANN

Dewolf et al. (2000): Demonstrated the applicability of neural network technology

for plant diseases forecasting.

Sanzogni et al. (2001): Developed the models for predicting milk production from farm

inputs using standard feed forward ANN.

Gaudart et al. (2004): Compared the performance of MLP and that of linear

regression.

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Bhadeshia (2008): Applied neural network in materials science.

Rivero et al (2010): By using genetic programming they generate and simplified ANNs.

Nikbakht et all (2011): They modelled double CSTR by Radial Basis Function (RBF) & Multilayer

Perceptron (MLP) Neural networks.

Somsong et all (2011): Neural Network Modelling and Optimization for a Batch Crystallizer to Produce

Purified Terephthalic Acid.

CONTD......

Properties of ANNs

Adaptively: changing the connection strengths to learn things

Non-linearity: the non-linear activation functions are essential

Fault tolerance: if one of the neurons or connections is damaged, the whole

network still works quite well .

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They might be better alternatives than classical solutions for problems

characterised by:

Nonlinearities High dimensionality Noisy, complex, imprecise, imperfect

and error prone sensor data

A lack of a clearly stated mathematical solution or algorithm

CONTD......

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Artificial Intellect with

Neural Networks

Intelligent

Control

Technical Diagnistics Intelligent

Data Analysis and Signal Processing

Advance Robotics

Machine Vision

Image & Pattern

Recognition

Intelligent Security Systems

Intelligent Medicine Devices

Intelligent Expert

Systems

APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

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DEVELOPMENT OF NEURAL NETWORK

Step 1: Data Collection Step 2: Training And Testing Data

Separation

Step 3: Network Architecture Step 4: Parameter Tuning And Weight Initialization

Step 5: Data Transformation Step 6: Training Step 7: Testing Step 8: Implementation 13

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Different ANN Algorithms published in various literatures [3]

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Algorithm

Input

Layer

Transfer

Function

Output

Layer

Transfer

Function

No Of

Nodes

Average

Absolute

Relative

Error

(AARE)

Standard

Deviation R

Levenburg Marquardt logsig purelin 3 0.127 0.164 0.997

BFGS Algorithm tansig purelin 8 0.131 0.141 0.939

Fletcher Reeves Update tansig purelin 7 0.135 0.157 0.945

One Step Secant Algorithm radbas purelin 6 0.136 0.138 0.940

Powell Beale Restarts tansig purelin 3 0.140 0.164 0.943

Polak Ribire Update tansig purelin 8 0.142 0.164 0.944

Batch Gradient Descent logsig purelin 3 0.152 0.170 0.931

Resilient Back Propagation tribas purelin 6 0.152 0.189 0.924

Performance of Different ANN algorithm [3]

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Advantages of slurry transport

Pipeline transport has been a progressive technology for conveying a large quantity of

bulk materials.

The modern way of pipelining prefers the concentrated slurries since hydraulic

transport of dense hydro-mixtures can bring

several advantages.

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Compared to a mechanical transport, the use of a pipeline ensures a dust free

environment, demands substantially less

space, makes possible full automation and

requires a minimum of operating staff.

On the other hand, it brings higher operational pressures and considerable

demands for a high quality of pumping

equipment and control system.

CONTD......

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In solidliquid multiphase flow, the separate phases move at different average velocities and the in situ

concentrations are not same as the concentrations in

which the phases are introduced or removed from the

system.

The variation of in situ concentrations from the supply concentrations is referred to as hold-up phenomenon.

The hold-up effect is measured by the hold-up ratio, given by the ratio of the average in situ concentration

and mean discharge concentration:

Hold Up Ratio

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Transfer

Function Node-1 Node-2 Node-3 Node-4 Node-5 Node-6 Node-7 Node-8 Node-9 Node-10

traingdm 0.007927 0.006512 0.00863 0.007665 0.010365 0.010908 0.009283 0.011281 0.012347 0.014219

traingda 0.002227 0.0019 5.56E-04 0.003659 0.001214 6.04E-04 0.003803 0.001032 0.002219 9.43E-04

trainbfg 0.001225 0.011836 0.00239 0.001146 0.002847 0.003317 7.52E-04 0.001714 0.004518 6.56E-04

traincgp 0.001603 0.001177 0.001428 5.93E-04 5.16E-04 0.003119 4.38E-04 0.001253 4.43E-04 6.07E-04

traingdx 7.08E-04 0.003339 0.002992 0.004041 8.97E-04 0.001035 0.001412 0.001668 0.006168 0.001416

trainoss 0.004139 0.00412 0.001275 0.008528 0.001165 0.001349 0.002171 0.001366 0.00205 0.001161

trainlm 1.27E-04 3.68E-04 4.55E-04 8.22E-05 0.003833 2.61E-04 1.46E-04 6.54E-04 1.86E-04 2.57E-04

trainr 0.002642 5.77E-04 5.69E-04 6.36E-04 4.39E-04 5.80E-04 0.00918 0.001271 0.001008 5.43E-04

traincgb 3.55E-04 0.002754 0.001771 4.25E-04 1.34E-04 3.76E-04 4.31E-04 9.09E-04 5.52E-04 5.51E-04

trainrp 0.004059 0.005213 0.003364 0.002913 0.001135 4.30E-04 7.05E-04 0.006367 0.001048 0.002184

trainc 3.93E-04 0.00201 0.003713 6.67E-04 6.00E-04 7.27E-04 5.14E-04 3.50E-04 5.33E-04 5.62E-04

traincgf 0.004139 0.002698 9.29E-04 0.001207 7.46E-04 4.45E-04 0.001863 0.001024 0.002795 0.002099

trainb 0.004646 0.005271 0.003376 0.006689 0.009102 0.004574 0.005323 0.001448 0.002555 0.004726

trainru 0.014015 0.023642 0.054308 0.012482 0.037854 0.042394 0.02013 0.020234 0.018043 0.013641

traingd 0.010893 0.014854 0.008741 0.008333 0.005302 0.019992 0.00968 0.005564 0.022081 0.009663

trainscg 5.75E-04 0.004944 0.001014 9.81E-04 5.00E-04 4.66E-04 0.0013 0.001291 0.001926 0.001628

MSE for Different Node Generated by ANN

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Regime identification is important for slurry pipeline

design as it is the prerequisite to apply different

pressure drop correlations in different regimes.

Four distinct regimes were found existent in slurry flow

in a pipeline depending upon the average velocity of

flow.

Sliding bed Saltation heterogeneous suspension Homogeneous suspension

Regime identification

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Transfer

Function Node-1

Node-

2

Node-

3

Node-

4

Node-

5

Node-

6

Node-

7

Node-

8

Node-

9

Node-

10

traingdm 1.01E+00 9.65E-01 9.09E-01 9.79E-01 1.05E+00 9.22E-01 1.45E+00 1.04E+00 1.87E+00 1.18E+00

traingda 1.09E+00 1.01E+00 1.01E+00 0.804162 0.95323 1.02E+00 0.951856 1.45E+00 0.856645 8.34E-01

trainbfg 8.88E-01 0.937544 0.889618 1.30E+00 0.982814 8.14E-01 8.89E-01 0.948371 1.265041 1.11E+00

traincgp 0.920743 0.90874 0.897977 1.74E+00 7.97E-01 0.918543 1.23E+00 0.934043 7.46E-01 7.99E-01

traingdx 1.08E+00 1.01E+00 1.05E+00 9.57E-01 8.69E-01 1.12E+00 1.49E+00 1.29E+00 8.86E-01 1.07E+00

trainoss 0.939558 1.048902 1.52E+00 0.932179 0.830731 0.615084 0.840119 0.798551 0.928933 0.806577

trainlm 8.97E-01 9.24E-01 9.19E-01 9.54E-01 0.966175 2.23E+00 9.12E-01 1.57E+00 1.31E+00 1.51E+00

trainr 0.956817 1.49E+00 8.84E-01 9.92E-01 1.09E+00 6.32E-01 1.008749 1.385658 1.274897 1.92E+00

traincgb 1.13E+00 1.052034 0.971989 1.01E+00 9.80E-01 8.37E-01 8.01E-01 9.67E-01 9.23E-01 8.43E-01

trainrp 0.842406 1.01232 1.023502 1.009231 0.912786 7.35E-01 8.65E-01 0.865856 1.081044 0.850224

trainc 7.68E-01 7.73E-01 7.98E-01 5.57E-01 1.33E+00 1.82E+00 9.26E-01 5.23E-01 1.40E+00 9.83E-01

traincgf 9.82E-01 9.24E-01 9.22E-01 8.88E-01 1.02E+00 9.42E-01 9.65E-01 1.18E+00 6.83E-01 8.00E-01

trainb 9.27E-01 1.02E+00 1.08E+00 9.72E-01 1.04E+00 1.11E+00 1.06E+00 9.27E-01 1.18E+00 2.62E+00

trainru 1.201813 1.05E+00 1.248647 1.504672 1.941665 1.173197 6.465297 2.434561 2.619652 7.829315

traingd 1.025887 0.971142 1.039606 0.961899 1.062214 0.990724 0.821197 1.309626 0.742879 1.050498

trainscg 1.03E+00 0.948015 0.990479 9.85E-01 8.30E-01 7.74E-01 1.062524 0.82514 0.940529 1.305016

MSE for Different Node in Regime Identification

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From this entire study it may be concluded that Artificial Neural Network (ANN) is

applied in chemical process engineering very

significantly.

ANN calculates and generates process data very accurately.

Application of ANN in different fields of Engineering other than data prediction, fault

diagnosis, process control etc. can be

explored .

Conclusion

1. Tambe, S. S., B. D. Kulkarni, P. B. Deshpande, Elements of

Artificial Neural Networks with Selected Applications in Chemical

Engineering, and Chemical And Biological Sciences, Simulation &

Advanced Controls, Inc., USA, (1996).

3. Lahiri, S. K., K. C. Ghanta, Development of An Artificial Neural

Network Correlation for Prediction of Pressure Drop of Slurry

Transport in Pipelines, International Journal of Mathematics,

Science & Engineering Applications (IJMESE), Vol. 2, No. 1, pp. 1-

21, (2008).

References

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2. Lahiri S.K. and Ghanta K.C. ,Development of an artificial neural

network correlation for prediction of hold-up of slurry transport in

pipelines, Chemical Engineering Science 63,1497 1509 (2008)

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CONTD...... 4. Lahiri, Sandip K. and Ghanta, Kartik Chandra , Development of a

hybrid support vector machine and genetic algorithm model for regime

identification of slurry transport in pipelines, Asia-Pac. J. Chem. Eng.

,Published online in Wiley InterScience, (www.interscience.wiley.com)

DOI:10.1002/apj.410 (2009).

5. Agarwal, M., Jade A. M., Jayaraman V. K. and Kulkarni B. D. (2003),

Support vector machines: A useful tool for process engineering

applications, Chem. Engg. Progr., 57-62.

6. Vapnik V. (1995), The Nature of Statistical Learning Theory, Springer

Verlag, New York.

7. Vapnik V. (1998), Statistical Learning Theory, John Wiley, New York.

Thank You