Insilico Desigh of Bio-reactors

8/7/2019 Insilico Desigh of Bio-reactors

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INSILICO DESIGH OF BIO-REACTORS

INSILICO DESIGH OF BIO-REACTORS

INTRODUCTION

Bioreactors are one of the most important parts of technological production

system. The choosing proper reactor and setting optimal parameters of its work

are crucial for optimum results during production. The complexity of the

bioreactor lies in the number of input parameters that exist in these cases when

compared to their simpler chemical counterparts. Conventional reaction

engineering techniques that are basically designed


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for chemical reactor design therefore cannot handle such enormous input

multiplicity both at the level of reactor designing as well as in the process controldesigning. Therefore there exists a need to take help of mathematical modeling

in the design of bioreactors. With various software now available

mathematical modeling it has become quite easy to apply these modeling

techniques with ease even though the designer has no much previous familiarity

with those modeling techniques.

MATHEMATICAL MODELLING THROUGH MATLAB

MATLAB is a high-performance language for technical computing. It integratescomputation, visualization, and programming in an easy-to-use environment

where problems and solutions are expressed in familiar mathematical notation.

Typical uses include Math and computation Algorithm development Da

acquisition Modeling, simulation, and prototyping Data analysis, exploration, and

visualization Scientific and engineering graphics Application development,

including graphical user interface building .MATLAB is an interactive system

whose basic data element is an array that does not require dimensioning. This

allows you to solve many technical computing problems, especially those with

matrix and vector formulations, in a fraction of the time it would take to write a

program in a scalar non-interactive language such as C or FORTRAN.

The name MATLAB stands for matrix laboratory. MATLAB was originally written

to provide easy access to matrix software developed by the LINPACK and


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EISPACK projects. Today, MATLAB engines incorporate the LAPACK and BLAS

libraries, embedding the state of the art in software for matrix computation.

MATLAB has evolved over a period of years with input from many users. In

university environments, it is the standard instructional tool for introductory and

advanced courses in mathematics, engineering, and science. In indust

MATLAB is the tool of choice for high-productivity research, development, and

analysis.

MATLAB features a family of add-on application-specific solutions call

toolboxes. Very important to most users of MATLAB, toolboxes allow you to learn

and apply specialized technology. Toolboxes are comprehensive collections of

MATLAB functions (M-files) that extend the MATLAB environment to solve

particular classes of problems. Areas in which toolboxes are available include

signal processing, control systems, neural networks, fuzzy logic, wavelets,

simulation, and many others.

MATHEMATICAL MODELING TECHNIQUES

Neural networks

Neural networks are composed of simple elements operating in parallel. These

elements are inspired by biological nervous systems. As in nature, the network

function is determined largely by the connections between elements. We can

train a neural network to perform a particular function by adjusting the values of

the connections (weights) between elements.

Commonly neural networks are adjusted, or trained, so that a particular input

leads to a specific target output. Such a situation is shown below. There, the

network is adjusted, based on a comparison of the output and the target, until the

network output matches the target. Typically many such input/target pairs are

used, in this supervised learning, to train a network.


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Batch training of a network proceeds by making weight and bias changes basedon an entire set (batch) of input vectors. Incremental training changes the

weights and biases of a network as needed after presentation of each individualinput vector. Incremental training is sometimes referred to as "on line" or"adaptive" training.

Bench scale reactor control modeling using neural networks

The main advantage with neural networks as discussed earlier is development of the model using dry lab simulation data obtained from previousmodels. One such example is as follows:

This is the basic design of the first neural network designed. It is the general

framework for long-range prediction for predictive control. (Z_ denotes the

signals z(_); z(_ + 1); z(_);Z 2 (U; ^ X; ^Y );u the manipulated input, y the output

measurements and x the system state. The predictive controller applies tentative

U-values and takes the ^XX and/or ^ Y values to optimize the process


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performance. In this example, u _ vf (t); y _ (pH, conductivity), x _ ([A]; [B]; [C];

[D]:)

Neural-Network Estimator-Predictor

All networks use a sigmoidal activation function for the hidden layer, a linear

activation function for the input and the output layers, and an additional bias node

in the input and hidden layers. All neural-network blocks contain five sigmoidal

hidden nodes. This network complexity was chosen based on extens

experience gained from simulations. Trialand- error design of the network

complexity, based on actual process runs, could lead to better networks, but

would require prohibitive experimental expense.

The prediction horizon was chosen to be 30 sampling instants (one minute) fortraining all predictor networks. This choice seems to be a good compromise

between accurate

long-range prediction and reasonable training times. Further increase of the

training prediction horizon did not significantly improve the prediction accuracy,

but increased the training effort considerably. B. Feedforward Networks A

feedforward network is used to estimate the process state and output at sampling

instant (this is the Estimator block of Fig. 2). It takes as inputs the sum of the

moles of B fed up to the current sampling instant, the sampling instant, the

volume, and the current measurements of the pH and electrical conductivity. As

outputs, it yields the estimates at of the concentrations of A, B, C, and D. This

network has thus six input nodes (including the bias node), four output nodes,

and 54 weights.


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Another feed forward network is then used to predict the future concentrations.

The concentration estimates of the first network are passed to this network,

which takes as further inputs the sampling instant for which the estimates were

computed, the sampling instant for which the long-range prediction has to be

made, , the sum of the feed up to , and the volume at time . As output, it yields

the estimates for of the concentrations of A, B, C, and D. This network has nineinput nodes, four output nodes, and 69 weights. The use of the current sampling

instant as an input into the first network, and into the second, is necessary in

order to provide the network with some temporal context within the batch.

Without this input, the networks can not learn a reasonable mapping. The

sampling instant is not directly related to the state, but facilitates proper

interpretation of the other input information.

External-Feedback Networks

Input Representation:Equations used to develop this model are the algebraic

and differential equations of the known part of thesystem. A straightforward

realization of these equations asa network requires the concentrations of A, B, C

and D,4 the current feed rate, and the volume as network inputs. Dueto the

relatively high sampling rate, which is 2 s comparedto the reaction time of 1800


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s, the system can be simplifiedin the time-discrete description. Due to the

marginal dilution duringa sampling interval, the dilution effect on the reactioncan

be neglected, and the reaction and the dilution can bedecoupled. Thus the

concentrations and the current feed rate per volume of reaction mixture are used

as inputs to thenetwork, and the dilution due to the feed during the sampling

interval is computed only at the end of the sampling interval.This approach,

which is applied for all external-feedbacknetworks, with and without other prior

information, avoidsthe complexity of an additional network input. The errors

introduced by not taking the dilution during each samplinginterval into account

are negligible, and the model of the

volume itself (9) is accurate since no mixing nonidealities are present and the

amount of feed added is known accurately..

Design of control systems for CSTR using neural networks in MATLAB

Consider the case of a simple continuous stirred tank reactor as shown in thefigure below.

For the above reactor a neural network has been designed in MATLAB using theneural network toolbox. The highlighted region is the neural network (NN)predictive controller which receives inputs from the reference data as well asfrom the plant output in order to create an online/ adaptive training for control ofthe CSTR.


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The results of such a neural network can be simulated using Simulink software inthe MATLAB itself. The resultant Simulink of the above network is shown below.

The simulink results are generally considered 99% accurate if the data given isaccurate enough. And hence these results are considered more or equivalent to experimental results. The simulink results can be generated bygiving different input data so as to find out the best possible input for optimaloutput.

WAVELET- NARMAX MODELLING

Narmax modelNon-linear black box models are difficult to handle in general because the

spectrum of possible model descriptions is very wide. The area is quite diverse

and covers topics from mathematically approximation theory via narm

representations of many non-linear systems require only a few terms.

Wavelet modelA wavelet is a waveform of effectively limited duration that has an average value

of zero. Wavelet analysis is a new and promising set of tools and techniques for

analyzing these signals. Wavelets have scale aspects and time aspe

consequently every application has scale and time aspects. To clarify them we

try to untangle the aspects somewhat arbitrarily. For scale aspects, we present

one idea around the notion of local regularity. For time aspects, we present a list


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of domains. When the decomposition is taken as a whole, the de-noising and

compression processes are center points. Wavelets concepts can be applied for,

One-Dimensional Continuous Wavelet Analysis

One-Dimensional Complex Continuous Wavelet Analysis

One-Dimensional Discrete Wavelet Analysis

Two-Dimensional Discrete Wavelet Analysis

One-Dimensional Discrete Stationary Wavelet Analysis

Two-Dimensional Discrete Stationary Wavelet Analysis

One-Dimensional Wavelet Regression Estimation

One-Dimensional Wavelet Density Estimation

Wavelet naramax hybrid modelLocal function expansion based model structures including the wave

decomposition techniques provide a powerful tool for representing non-linear

signals, even severely non-linear signals with discontinuities. Wavele

decompositions outperform many approximation schemes and offer flexible

capability for approximating arbitrary functions. As a special form of the narmax

model, this hybrid model structure is referred to as the WANARMAX model.

This model has been applied in the case of stochastic identification of bioreactor

process exhibiting input multiplicity [Rishi Amrit and Prabirkumar Saha]. The aim

of their work was to address the stochastic modeling issues related to bioreactor

process. They had used all the possible modeling techniques viz., block oriented

narmax model, bootstrap structure detection for narmax model and wavelet

narmax model of which they found wavelet narmax model to be the best of the

pick. Their work provides a lead to understanding input multiplicity in bioreactors

and the ways and means to solve the entire problem of simulation designs for

such complex reactors.

ACTIVATED SLUDGE PROCESS (ASP) MODELING AND SIMULATION


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In Figure given above is the basic layout for the considered activated sludgeprocessis shown: from the secondary settler, the sludge is partially recirculated tothe bioreactor (Returned Activated Sludge, RAS) and partially wasted as excesssludge (Waste Activated Sludge, WAS). The ASP is a biological process in whichmicroorganisms oxidize and mineralize organic matter. The microorganisms inthe activated sludge are mainly bacteria, which can be found also in the rawwastewater incoming into the plant. The composition and the species depend notonly on the influent wastewater but also on the design and operation of thewastewater treatment plant.Given below is the basic mechanism along with the necessary equations thathave been used in the design of the activated sludge process.

The software currently in market that can be used for the simulation of the above

mentioned process are :

WEST (Wastewater treatment plant Engines for Simulation and Training):

an interactive dynamic simulator. It is developed mainly at the

University of Gent, Belgium and current information about the software


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can be found on http://www.hemmis.com/.

SIMBA (Simulation programms fur die Biologische Abwasserreinigung):

developed at the Institut fur Automation und Kommonikation

(IFAK) in Germany. It can be considered a custom made version

of Simulink for wastewater treatment applications. A more extensively

compend about the simulator can be found on http://simba.

ifak-md.de/simba/.

EFOR is a stand-alone software package for the simulation of complete

wastewater treatment plant. It is developed mainly at the Danish

Technical University. The present progress can be found on http:

//www.dhisoftware.com/efor/.

Using MATLAB for ASP modeling

the above case was modeled in MATLAB as shown below:


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The simulation using simulink is as shown above. We can observe inputmultiplicity in this case also. Earlier we had seen it while discussing the wavelet narmax model.

The results of simulink simulation are shown below:

CONCLUSION

The various modeling techniques discussed till now come to prove how easy it isto model and simulate various bioreactors and their control systems usingvarious modeling & simulation techniques and also by using various modelingsoftware with MATLAB being the best suited in most of the cases. The widerunderstanding of these mathematical models and modeling software would make


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biotech engineers better equipped to handle the complex tasks of bioreactordesign and control of those bioreactors.

REFERENCES

B. Schenker and M. Agarwal, Robust predictive control usineuralnetworkmodels, in AIChE Annu. Meet. St. Louis, MO, Nov. 1993,Paper 147j.

D. E. Seborg, T. F. Edgar, and S. L. Shah, Adaptive control strategiesfor process control: A survey, AIChE J, vol. 32, no. 6, pp. 881913,1986.

Predictive Control of a Bench-Scale Chemical Reactor Based on Neural-Network Models

Benedikt Schenker and Mukul Agarwal

P. Terwiesch, D. Ravemark, B. Schenker, and D. W. T. RSemibatchprocess optimization under uncertainty: Theory and experiments,Computers Chem. Eng., vol. 22, pp. 201213, 1998.

K. Singh and J. Hahn. On the use of empirical Gramians for controllabilityand observability analysis. In Proceedings of the AmericanControl Conference, June 8-10 2005. Portland, USA.

M. Mulas and S. Skogestad. Control structure analysis for an activatedsludge process. In Proceedings of ICheap7, Chemical Engineer-ing Transaction, pages 173178, 2005.

Process control and monitoring: http://www.bioreactors.net/eng/processcontrol and monitering.html

Nonlinear adaptive control for bioreactors with unknown kinetics-Ludovic Mailleret;, Olivier Bernard, Jean-Philippe Steyer

Predictive Control of a Bench-Scale Chemical Reactor Based on Neural-

Network Models: Benedict Schenker and Mukul Agarwal

Neural network based modeling and control of batch reactor:M. Mujtaba, N. Aziz, and M. A. Hussain.

Dynamic simulator for yeast fermentation bioreactor: Zoltan K. Nagy.

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Insilico Desigh of Bio-reactors

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