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BACKGROUND
1. THE IMPORTANCES OF CITRICACID PRODUCTION
Low toxic acidulant
Great demand Growing at the rate
35 %
In 2004:
1. Beverages 50%
2. Food 1520%
3. Soaps and detergents 1517%
4. Pharmaceutical / cosmetics 79%
5. Industrial 68%
Worldwide citric acid production is
around 1.4 million tones per year.
At current prices the market is worth
about $1.5 billion.
2. THE IMPORTANCE OF USINGSOLID STATE FERMENTATION
SSF) Used with agro-industrial residues
(E.g.: EFB) reduce
environmental problem regarding
disposal of solid waste
Lower energy requirement
Produce less wastewater
Use low volume equipment which
is lower in cost but can effectively
produce high concentrated
product
INTRODUCTION
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Fermentation process is always complex.
Many factors influence Citric Acid production
Must apply optimization to maximize the yield and profit
Common method used RSM
Lack usage of ANN
Comparisons between RSM and ANN
No available research used ANN to optimize Citric Acid
production by solid state fermentation
PROBLEMSTATEMENT
In this study.
ARTIFICIALNEURAL
NETWORK
1. Optimize medium composition
2. Optimizeprocessconditions
3. Predict the outputyield of citric acid
production
5. Compare
ANNs resultswith RSM
4. Validate the ANNmodel by running
experiment
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LITERATURE
REVIEWCITRIC ACID PRODUCTION
1. The cultures ofAspergillus nigerare fed on a
sucrose or glucose-containing medium to
produce citric acid.
2. In term of production type, the use of
submerged fermentation is still dominating.
3. But now, the solid-state fermentation is
creating new possibilities for producers.
4. The use of agro-industrial residues such as
EFB as support in solid-state fermentation is
economically important and minimizes
environmental problems.Rotary drum SSF
Oil palm empty fruit bunch fiberis a lignocellulosic waste from
palm oil mills.1. low cost,2. renewable and3. widespread sources of
sugars.
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OPTIMIZATION
To optimize the process parameters maximize productincrease
profit
Several factors influence fermentation process:
1. Medium compositions (sucrose, trace elements, simulator)
2. pH
3. Temperature
4. Agitation
5. Aeration
6. Moisture content - SSF
Methods used for optimization:
1. RSM
2. ANN
3. Others (Genetic algorithm, CCD, Factorial Design)
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WHAT IS RSM ?
1. Collection of mathematical and statistical techniques useful for the
modeling and analysis of problems in which a response of interest is
influenced by several variables and the objective is to optimize this
response.
2. Normally use quadratic relationship.
3. A first-order model with two independent variables can be expressed as:
y = 0+
1x1 +
2x2 + e
4. The approximating function with two variables is called a second-order
model: y = 0+
1x1+
2x2+
11x11
2+ 22x22
2 + 12x12+ e
5. Rapid and efficiently used with small amount of data,
6. The primary limitation of RSM occurs when the approximation offered by
the quadratic function is inadequate.
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WHAT IS ANN ?
Computational modeling system based
on the neural structure of the brain.
Consist of three layers:1. Input layer(s)2. Hidden layer(s)3. Output layer(s)
Resembles human brain in two respects:1. Learning from examples2. Stores knowledge
Major Components:1. Weighing factors2. Summation function3. Transfer function4. Scaling and limiting5. Output function6. Error function and back propagated value
7. Learning function
NEURONS
Output
LayerHiddenLayer
InputLayer
NEURAL NETWORKS
Dendrites
Cell Body
Axon
Applications in Engineering Field:1. Complex and non-linear
problems2. Prediction3. Classification4. Data Association5. Data conceptualization
6. Data filtering
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ProcessingElement
MajorComponents:1. Weighing
factors2. Summation
function3. Transfer
function4. Scaling and
limiting5. Output
function6. Error
function andbackpropagatedvalue
7. Learningfunction
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ARTIFICIALNEURAL
NETWORK
1. TYPES OFNEURAL NETWORKS
Single Perceptron
Multilayer Perceptron(MLP)
3. TYPES OF LEARNING
SUPERVISED LEARNING:with teacher
Provide correct output for
every inputpattern bydetermine and adjusting theweight to produce answer asclose as possible to known
correct answer .
UNSUPERVISEDLEARNING:
without teacherDoes not require a correct
answer associated with eachinput pattern in the training
data set. It explores theunderlying structure in the
data and organize categoriesbased on this data
HYBRID LEARNING:Combine supervised and
unsupervised learning
2. NEURAL NETWORKTOPOLOGIES
1. FeedForward Neuralnetworks
2. Recurrent neuralnetwork
4. FEED FORWARDBACKPROPAGATION NN
Compare
Input Output
Target
Adjust Weight
Neural Network,including connections
(weights) betweenneurons
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RESEARCH STUDIES
Karnik et al. ,2007A comparative study of the ANN andRSM modeling approaches forpredicting burr size in drilling.
Minimum absolute percentage errorfor ANN prediction is within range1% - 0.14% lower than RSM which is
12% - 4.8%.
Desai et al. ,2008Comparison of ANN and RSM in fermentationmedia optimization: Case study offermentative production of scleroglucan
Average percentage error of ANN is 6.5lower than RSM which is 20
Correlation coefficient of validation data for
ANN is 0.98 higher than RSM which is 0.89
Kandimalla et al. 1999Optimization of a vehicle mixture forthe transdermal delivery of
melatonin using ANN and RSM
ANN can easily handle more than 4input variables but for RSM, a largeno. of input variables lead to apolynomial with many coefficientthat involves tedious computation
Bagci and Isik, 2006Investigation of surface roughness in turningundirectional GFRP composites by using RSM
and ANN
It was found that the maximum test errorswere 6.30% and 6.36% by comparingroughness (Ra) values predicted from ANNmodel with those predicted RSM.
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PART 1: SOFTWARE APPLICATION
Materials and equipments
Building ANN for optimization
Sensitivity Analysis
MATERIALS AND EQUIPMENTS:1. Software = MATLAB Version 2008a
MATLAB:1. Command-line functions in M-file2. Toolboxes : nntool, nftool, nntraintool, nprtool
METHODOLOGY..
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nntool nftool
nprtool nctool
nntraintool
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1. Data:
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Run
number
Sucrose Mineral Solution Inoculum Citric acid yield (g/kg-dry EFB)
a b c
Experimental Predicted (RSM)(% w/w) (% w/w) (% w/w)
1 8 12 20 259.23 258.1
2 6 2 16 267.47 268.15
3 4 12 20 256.52 242.52
4 6 8 16 334.68 332.81
5 6 8 16 334.23 332.81
6 4 4 20 208.69 213.22
7 4 4 12 236.65 231.32
8 6 8 16 332.44 332.819 8 4 12 259.22 266.75
10 8 4 20 260.65 247.19
11 4 12 12 250.02 257.02
12 6 8 16 333.88 332.81
13 6 8 16 333.08 332.81
14 6 14 16 292.42 300.18
15 6 8 16 334.14 332.81
16 3 8 16 247.46 249.8
17 6 8 10 257.79 256.1218 8 12 12 285.06 274.06
19 9 8 16 278.9 288.06
20 6 8 22 217.41 230.58
a : 1% (w/w) = 33.3 g/kg-EFB
b: 1% (v/w) = Zn, 3; Cu, 3.3; Mn, 13.3 and Mg, 166.7 mg/kg-EFB
c: 1% (v/w) = 6.7 x 1010spores/kg-EFB
Table 2: Experimental design data using CCD with the experimental and predicted value
(using RSM) of citric acid production for medum compositions optimization.
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Run Initial pH, A1Moisture content,
B1Incubation temperature, C1
Citric acid production
(g/kg-EFB)
Predicted Experimental
1 4 (-1) 78 (+1) 28 (-1) 229.68 241.30
2 6.5 (0) 70 (0) 32 (0) 368.16 368.61
3 6.5 (0) 58 (-2) 32 (0) 195.36 190.26
4 6.5 (0) 70 (0) 32 (0) 368.16 367.42
5 6.5 (0) 70 (0) 32 (0) 368.16 368.40
6 4 (-1) 78 (+1) 36 (+1) 256.88 241.24
7 4 (-1) 62 (-1) 36 (+1) 233.46 240.86
8 3 (-2) 70 (0) 32 (0) 315.96 316.42
9 6.5 (0) 70 (0) 38 (+2) 263.75 274.33
10 6.5 (0) 70 (0) 32 (0) 368.16 368.14
11 6.5 (0) 70 (0) 26 (-2) 220.80 210.05
12 6.5 (0) 82 (+2) 32 (0) 233.97 238.90
13 6.5 (0) 70 (0) 32 (0) 368.16 368.81
14 4 (-1) 62 (-1) 28 (-1) 161.96 158.10
15 10 (+2) 70 (0) 32 (0) 285.19 284.53
16 9 (+1) 78 (+1) 36 (+1) 194.35 198.38
17 9 (+1) 62 (-1) 28 (-1) 180.53 196.3218 6.5 (0) 70 (0) 32 (0) 368.16 367.91
Table 3: Experimental design using CCD with the experimental and predicted value
(using RSM) of citric acid production for process conditions optimization
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RunAeration,
A2(L/min)
Agitation,B2
(times/day)
Citric acid (g/kg-EFB)
Experimental Predicted
1 4 (-1) 1 (-1) 130.40 130.86
2 12 (+1) 1 (-1) 239.42 235.61
3 4 (-1) 5 (+1) 198.94 204.31
4 12 (+1) 5 (+1) 131.91 133.00
5 4 (-1) 3 (0) 268.36 262.53
6 12 (+1) 3 (0) 276.54 279.25
7 8 (0) 1 (-1) 234.14 237.49
8 8 (0) 5 (+1) 229.37 222.91
9 8 (0) 3 (0) 323.81 325.14
10 8 (0) 3 (0) 324.35 325.14
11 8 (0) 3 (0) 324.16 325.14
Table 4: Experimental design using CCD with the experimental and predicted value
(using RSM) of citric acid production for aeration and agitation optimization.
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Building ANNData Collection, Processing and Analysis
Determination of Number of Hidden Layers
Training
Model VerificationThe best model is selected according to :1. Multiple linear regression, R2. Mean Squared Error (MSE)
Combination of
input variables
Variations of the
number of
hidden neurons
Carry out
several training
Results: R, MSE,
number of
hidden neurons,
number of
utilized weight
Best model
selection
Methodology for training and validation
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4 ANN models will be created using 4 sets of data
Training
Prediction
ANN MODEL 1:1. Sucrose2. Sago Starch
3. Cassava Flour4. Urea5. Methanol6. KH2PO47. Fe8. Zn9. Mn10. Cu
11. Mg
ANN MODEL 2:1. Sucrose2. Mineral
solutions(Zn, Cu, Mn, Mg)
3. Inoculum
ANN MODEL 3:1. Initial pH2. Moisture
Content3. Temperature
ANN MODEL 4:1. Aeration2. Agitation
Experimental valueof Citric Acid
Production for eachset of data(g/kg-EFB)
Predicted Citric AcidProduction(g/kg-EFB)
Makecomparisonand get thevalue of R
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% Building Neural Network Model
% Define the input parameters and values% Sucrose with unit (% w/w)S =[8 6 4 6 6 4 4 6 8 8 4 6 6 6 6 3 6 8 9 6];% Mineral Solution with unit (% w/w)MS=[12 2 12 8 8 4 4 8 4 4 12 8 8 14 8 8 8 12 8 8];% Inoculum with unit (% w/w)I =[20 16 20 16 16 20 12 16 12 20 12 16 16 16 16 16 10 12 16 22];% Assigning the input parameters[inputs]=[S;MS;I];
% Define the target values which is the citric acid production (CA)targets_CA=[259.23 267.47 256.52 334.68 334.23 208.69 236.65 332.44 259.22 260.65 250.02 333.88 333.08 292.42 334.14 247.46 257.79 285.06 278.9 217.41];% Creating the networknet=newff(inputs,targets_CA,50,{},'trainbfg');
% Train the network[net,tr]=train(net,inputs,targets_CA);
% Predicted Outputpredicted_CA = sim(net,inputs);predicted_CA';plotregression(targets_CA,predicted_CA)
EXAMPLE OF COMMAND-LINE FUNCTION
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SENSITIVITY ANALYSISLinear Correlation in MATLAB (corrcoef)
Correlation coefficient quantifies the strength of a linear relationship between
two variables.
The correlation coefficients range from -1 to 1, where
1. Values close to 1 suggest that there is a positive linear relationship between
the data columns.
2. Values close to -1 suggest that one column of data has a negative linear
relationship to another column of data (anti-correlation).
3. Values close to or equal to 0 suggest there is no linear relationship between
the data columnsThis command function will also give the p-value of each relationship. Each p-
value is the probability of getting a correlation as large as the observed value by
random chance, when the true correlation is zero. Small p-value give better
correlation (normally less than 0.05).
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% Sensitivity analysis using Linear Correlation% The response is the of Citric Acid Production in (g/kg-EFB)
Output=[30.56 77.49 5.995 7.537 5.267 64.84 23.16 66.12 7.577 18.83 128.9 13.11];
% The 11 independent variables as input variable are defined as follows:
S=[3 3 0 0 3 3 0 3 0 0 3 0]; % Sucrose - unit is [%(w/w)]
% Calculate the correlation and p-value.% If p(i,j) is less than 0.05, then the correlation r(i,j) is significant.
'S'[r,p]=corrcoef(S,Output)
EXAMPLE OF COMMAND-LINE FUNCTION
ans =
Sr =
1.0000 0.66650.6665 1.0000
p =1.0000 0.01790.0179 1.0000
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PART 1: EXPERIMENT
Preparation of materials, media and equipment
Experimental procedure for solid state bioconversionUsing optimal conditions obtained from ANN models
Harvesting and extraction of citric acid
Determination of citric acid
However, the experiment will only be run if theoptimum value of the parameters of each set are
different from the optimum value obtained from RSM
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PRELIMINARY RESULTS
Graph of correlation coefficient value (r)for each media constituent
Graph of correlation coefficient value (r)for each parameters of media optimization
SENSITIVITY ANALYSIS
RESULTS..
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Number of Hidden Neurons: 20 Number of Hidden Neurons: 30
Number of Hidden Neurons: 50
METHOD ANN MODEL RSM
No. of
hidden
neurons
20 30 50
R value 0.8464 0.94987 0.93218
0.985
ANN MODEL 2
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EXPECTED RESULTS
1. ANN Models more stable2. The value of R should be higher than the value of R
obtained by RSM
3. The best model can be selected from the best model of
each set of data that give highest value of R4. The optimum value of each parameter can be obtained
from the best model
5. The optimum output can be predicted using the optimum
value of parameters
6. The results of ANN cab be compared with the result from
RSM
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CONCLUSION
1. ANN can be used to optimize fermentation process.
2. Data from existing studies were used for training ANN model.
3. ANN can be used with multiple parameters as input.
4. ANN can be retrain with different number of hidden neurons
and other parameter to obtain better result.
5. MATLAB provide command-line functions and neural network
toolboxes that help to build ANN model.
6. Model from the preliminary work is still unstable and need
improvement
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No Tasks
2009 2010
November January February March April
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
1
Revise and improve
the existing preliminary
ANN model.
2Obtain the best model for
optimization
3
Get the optimum
parameters and predicted
output from the firstmodel
4
Build ANN models for
other data and perform
optimization
5
Determine the optimum
parameters and predicted
output from the ANN
models
6Plan and run experiment
for validation if necessary
7Analyse results and write
final report
8Preparation for FYP 2
presentation
GANTT CHART
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