NEURAL NETWORK CONTROLLER FOR CONTINUOUS STIRRED TANK REACTOR Mrs. S.SHARANYA, SUPRIYO DEY, ROHIT DASGUPTA, ANUBHAB BISWAS Department of Electronics and Instrumentation SRM Institute of Science and Technology, Chennai, India. Email id: [email protected]Abstract - The aim of the project is to design and train a neural network for controlling the exothermic operational parameters of a CSTR - Continuous Stirred Tank Reactor, one of the most common reactor in chemical industrial world. The paper is a comprehensive study of using this method to control the CSTR operation. We have compared the system parameters of a network controlled by MPC (Model Predictive Control) and conventional PID, through the development of input - output relationships giving some drawbacks of the system and developed an intention to go through the training and development of multiple layered feed forward neural network using Back-Propagation algorithm reducing the operational errors through weighted adjustments. The mathematical model is developed through mass balance and energy balance equations of the reactor through state space analysis and design of the hardware for our project have also been included. Mathematical model design and simulation are done using MATLAB. Keywords - Neural network, CSTR, MPC, PID, Back-Propagation algorithm, Mathematical model, MATLAB. I. INTRODUCT ION CSTR is a complex, nonlinear system, is one of the common reactors in chemical plant. Artificial Neural Networks (ANN) will be used to model the CSTR incorporating its non-linear characteristics. Usually the industrial reactors are controlled using linear PID control configurations and the tuning of controller parameters is based on the linearization of the reactor models in a small neighborhood around the stationary operating points. If the process is subjected to larger disturbances and/or it operates at conditions of higher state sensitivity, the state trajectory can considerably deviate from the aforementioned neighborhood and consequently,deterioratesthe performance of the controller. The modeling and control of an isothermal CSTR using neural networks. Multiple layer feed forward neural network with back- propagation algorithm is used. CSTR operation is in steady state but any condition change and temporary shutdown leads to transient. Even the feed rate input is also transient by nature.The use of neural networks in chemical engineering field offers potentially effective means of handling three difficult problems: Complexity, non-linearity and uncertainties. The three steps involved in the ANN model development are -Generation of input-output data -Network Architecture selection -Model validation II. MATHEMATICAL MODELLING OF CSTR The following assumptions are made to obtain the simplified modelling equations of an ideal CSTR: A. Perfect mixing in the reactor and jacket. B. Constant volume reactor and jacket. International Journal of Pure and Applied Mathematics Volume 118 No. 20 2018, 3415-3421 ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu Special Issue ijpam.eu 3415
8
Embed
NEURAL NETWORK CONTROLLER FOR …NEURAL NETWORK CONTROLLER FOR CONTINUOUS STIRRED TANK REACTOR Mrs . S.SHARANYA, SUPRIYO DEY, ROHIT DASGUPTA, ANUBHAB BISWAS Department of Electronics
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Abstract - The aim of the project is to design and train a
neural network for controlling the exothermic operational
parameters of a CSTR - Continuous Stirred Tank Reactor,
one of the most common reactor in chemical industrial
world. The paper is a comprehensive study of using this
method to control the CSTR operation. We have compared
the system parameters of a network controlled by MPC
(Model Pred ictive Control) and conventional PID, through
the development of input - output relationships giving some
drawbacks of the system and developed an intention to go
through the training and development of multip le layered
feed forward neural network using Back-Propagation
algorithm reducing the operational errors through weighted
adjustments. The mathemat ical model is developed through
mass balance and energy balance equations of the reactor
through state space analysis and design of the hardware for
our project have also been included. Mathematical model design and
simulation are done using MATLAB. Keywords - Neural network, CSTR, MPC, PID,
Back-Propagation algorithm, Mathematical model,
MATLAB.
I. INTRODUCTION
CSTR is a complex, nonlinear system, is one of the
common reactors in chemical plant. Artificial Neural
Networks (ANN) will be used to model the CSTR
incorporating its non-linear characteristics. Usually the
industrial reactors are controlled using linear PID control
configurations and the tuning of controller parameters is
based on the linearizat ion of the reactor models in a small
neighborhood around the stationary operating points. If the
process is subjected to larger d isturbances and/or it operates
at conditions of higher state sensitivity, the state
trajectory can considerably deviate from the
aforementioned neighborhood and
consequently,deterioratesthe performance
of the controller.
The modeling and control of an isothermal
CSTR using neural networks. Mult iple layer
feed forward neural network with back-
propagation algorithm is used.
CSTR operation is in steady state but any
condition change and temporary shutdown leads
to transient. Even the feed rate input is also
transient by nature.The use of neural networks
in chemical engineering field offers potentially
effective means of handling three difficult
problems: Complexity, non-linearity and
uncertainties. The three steps involved in the ANN model
development are -Generation of input-output
data -Network Architecture selection -Model validation
II. MATHEMATICAL MODELLING OF CSTR
The following assumptions are made to obtain
the simplified modelling equations of an ideal
CSTR:
A. Perfect mixing in the reactor and jacket. B. Constant volume reactor and jacket.
International Journal of Pure and Applied MathematicsVolume 118 No. 20 2018, 3415-3421ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu
3415
C. Liquid density and heat capacity are constant. D. Consider simple exothermic, first order reaction. E. Reactor perfectly insulated. F. No energy balance consideration for jacket.
The mathematical model for this process is formulated
by carrying out mass and energy balances, and
introducing appropriate consecutive equations. Component Mass Balance Equation V*ΔCa = q/V*(Caf – C) - k0 * e^(-Ea/RT) * Ca Energy Balance Equation ρ*Cp*V*ΔT = q*ρ*Cp (Tf - T) + ΔV*ΔH* k0*e^(-Ea/RT)
* Ca + UA(Tc - T)
Parameters
Tc = Temp. of cooling jacket (K)
q = Volumetric flow rate (m^3/s)
V = Volume of CSTR (m^3)
ρ = Density of (A-B) mixture
Cp = Heat capacity of (A-B) mixture (J/kgK)
ΔH = Heat of reaction for A-B (J/mol)
k0 = Pre-exponential factor (/s)
UA = Overall heat transfer co-efficient
R = Universal gas constant
Caf = Feed concentration (mol/m^3)
Tf = Feed temp. (K)
Ea = Activation energy (J)
Ca = Conc. of A in CSTR (mol/m^3)
T = Temp. in CSTR (K)
Function Variables : Ca, T Fixed Values: V, ρ, Cp, ΔH, k0, UA Manipulated Variables : Tc, q, Caf, Tf State and Control Variables : Ca and T respectively
Linearization: The non-linear equations are linearized and
cast into the state variable form as follows: x= Ax + Bu; y =
Cx; where matrices A and B represent the Jacobian matrices
corresponding to the nominal values of the state variables and
input variables and x , u and y represent the
deviation variables. The output matrix is
represented as C.
FIG. SIMULINK MODEL
III. FEED FORWARD NEURAL
NETWORK
FIG. STRUCTURE OF NEURAL NETWORK
A collection o f neurons connected together in a
network can be represented by a directed graph:
Nodes represent the neurons, and arrows
represent the links between them. Each node has
its number, and a link connecting two nodes will
have a pair of numbers (e.g. (1,4) connecting
nodes 1 and 4). Networks without cycles
(feedback loops) are called a feed-forward
networks (or perceptron).
Input and Output Nodes: Input nodes of the
network (nodes 1, 2 and 3) are associated with
the input variables (x1,...,xm). They do not
compute anything, but simply pass the values to
the processing nodes. Output nodes (4 and 5) are
associated with the output variables (y1,...,yn).
International Journal of Pure and Applied Mathematics Special Issue
3416
Hidden Nodes and Layers --- A neural network may have
hidden nodes — they are not connected directly to the
environment (‗hidden‘ inside the network):
We may organise nodes in layers: input (nodes 1,2 and 3),
hidden (4 and 5) and output (6 and 7) layers. Neural networks
can have several hidden layers.
Training algorithms: The process of finding a set of weights such that for a given
input the network produces the desired output is called
training.
Algorithms for train ing neural networks can be supervised
(i.e . with a ‗teacher‘) and unsupervised (self-organising).
Supervised algorithms use a train ing set- a set of pairs (x,y)
of inputs with their corresponding desired outputs.
An outline of a supervised learning algorithm:
1. Initially, set all the weights wij to some random values
2. Repeat (a) Feed the network with an input x from one of
the examples in the training set (b) Compute the network‘s
output f(x) (c) Change the weights wij of the nodes 3. Until the error c(y,f(x)) is small.
IV. APPLICATIONS
1. Pattern Classification
The set of all input values is called the input pattern, and the
set of output values the output pattern x = (x1,...,xm) → y =
(y1,...,yn). A neural network ‗learns‘ the relation between
diff erent input and output patterns. Thus, a neural network
performs pattern classification or pattern recognition (i.e.
classifies inputs into output categories).
2. Time Series Analysis The aim of the analysis is to learn to predict the future
values[x(t1),x(t2),...,x(tm)].
We may use a neural network to analyze time series.
Input: Consider (m) values in the past x(t1),x(t2),...,x(tm) as