This research article develops an inferential control scheme using an adaptive linear neural network (adaline) soft sensor for ethanol production in a continuous bioreactor. The proposed control method, named ran2pid, optimizes the 2-degree of freedom PID controller parameters with genetic algorithms and demonstrates superior performance compared to existing controllers. The study highlights the significance of soft sensors in controlling complex bioprocesses efficiently, particularly in terms of monitoring unmeasured variables like ethanol concentration.

1. Research article
Two degree of freedom PID based inferential control of continuous
bioreactor for ethanol production
Nikhil Pachauri n
, Vijander Singh, Asha Rani
Instrumentation and Control Engineering Division, Netaji Subhas Institute of Technology, University of Delhi, Sec-3 Dwarka, New Delhi 110078, India
a r t i c l e i n f o
Article history:
Received 20 August 2016
Received in revised form
13 December 2016
Accepted 21 March 2017
Available online 27 March 2017
Keywords:
Bioreactor
ADALINE
LMPNN
BRBNN
PID
2-DOF-PID
a b s t r a c t
This article presents the development of inferential control scheme based on Adaptive linear neural
network (ADALINE) soft sensor for the control of fermentation process. The ethanol concentration of
bioreactor is estimated from temperature profile of the process using soft sensor. The prediction accuracy
of ADALINE is enhanced by retraining it with immediate past measurements. The ADALINE and retrained
ADALINE are used along with PID and 2-DOF-PID leading to APID, A2PID, RAPID and RA2PID inferential
controllers. Further the parameters of 2-DOF-PID are optimized using Non-dominated sorted genetic
algorithm-II and used with retrained ADALINE soft sensor which leads to RAN2PID inferential controller.
Simulation results demonstrate that performance of proposed RAN2PID controller is better in compar-
ison to other designed controllers in terms of qualitative and quantitative performance indices.
& 2017 ISA. Published by Elsevier Ltd. All rights reserved.
1. Introduction
In a bioprocess large variety of products are manufactured with
the help of living organisms. It is one of the complex processes in
the field of process engineering because of its high dimensionality,
nonlinearity and dynamic characteristics. Bioprocesses have
gained an amazing interest of researchers in the last two decades
as it plays an important role for the production of vaccines and
antibiotics in pharmaceutical industries; beer, wine etc. in agro-
food industries and in the treatment of industrial wastewater.
Industrial fermentation processes have three modes of operations;
batch, fed-batch and continuous. In a continuous operation sub-
strate is added and product is removed continuously whereas in
batch operations substrate is added at initial stage of process and
final product is removed at the completion of the process. In case
of fed-batch operation feed rate profile is varied during the pro-
cess and final product is removed at the end of the process. The
automatic optimal control of a bioprocess is of considerable in-
terest to many fermentation industries, so as to decrease the
production cost and maintain the quality of final product at the
same time. However design of a control system for fermentation
process is not an easy task due to model uncertainties, nonlinear
nature of the system and slow response of the process. Further the
lack of suitable and robust hardware sensor for measurement of
biomass or product concentration is also a restriction for im-
plementation of efficient control of fermentation process. The
problem associated with the hardware sensors is the time delay
for measurement due to which online control is not feasible. These
sensors involve high cost, repeated calibration and regular main-
tenance. Therefore hardware sensors are not an effective solution
for precise online product quality measurement. Soft sensor is an
alternative to hardware sensor, through which some process
variables are measured online with an assessment algorithm in
order to evaluate the unmeasured variables, model parameters
and measurement delays [1].
Various researchers have extensively used soft sensors for
concentration estimation in biochemical processes. Ödman et al.
[2] used a partial least square (PLS) based soft sensor for the real
time prediction of important analyte concentration such as bio-
mass, glucose and ethanol. Experimental results show the effec-
tiveness of PLS for real time prediction. Han and Qiao [3] pro-
posed a self-organizing radial basis function (SORBF) neural net-
work model for prediction of sludge volume index. Results confirm
the robustness and effectiveness of SORBF as soft sensor. Warth
et al. [4] proposed a soft sensor integration with NIR probe and
high performance liquid chromatography (HPLC). The results ob-
tained show that integration of NIR and HPLC increases the overall
potential of soft-sensor for prediction of biomass concentration
and other process parameters. Chen et al. [5] examined the ap-
propriateness of Recurrent Neural Network (RNN) based online
sensor for estimation of biomass concentration. Feed rate, liquid
volume and dissolved oxygen are the input variables for sensor. It
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/isatrans
ISA Transactions
http://dx.doi.org/10.1016/j.isatra.2017.03.014
0019-0578/& 2017 ISA. Published by Elsevier Ltd. All rights reserved.
n
Correspondence to: Instrumentation and Control Engineering Division, Room
No-119/VI, Azad Hind Fauz Marg, NSIT, Dwarka Sec-3, New Delhi 110078, India.
E-mail addresses: nikhilpchr@gmail.com (N. Pachauri),
vijaydee@gmail.com (V. Singh), ashansit@gmail.com (A. Rani).
ISA Transactions 68 (2017) 235–250

2. is confirmed from the results that RNN is a powerful tool for im-
plementing an online biomass soft sensor for fermentation pro-
cess. Gustavsson and Mandenius [6] employed a soft sensor ap-
proach for controlling the metabolic overflow for mixed acid fer-
mentation and glucose overflow metabolism in fed-batch culti-
vation. Two control strategies have been defined; first is control-
ling the specific rate of both overflows with fixed preset target
values, secondly concentration at a set level is controlled. The
second control strategy is found to be more efficient in comparison
to the first. Liu et al. [7] proposed a novel approach for optimi-
zation of support vector machine model based on Genetic simu-
lated annealing algorithm (GSA) and Akaike Information Criterion
(AIC). Simulations show good performance of the proposed
method for fermentation process. Tian et al. [8] used a neural
network model based approach for the development of optimal
control policies in which neural network is used for building a
prediction model of fed batch bioreactor. Results show that pro-
posed methodology overcomes the problems associated with first
principle model. Veloso et al. [9] used an asymptotic observer as a
soft sensor for monitoring of Escherichia coli fed batch fermen-
tation. Experimental results verify the improved performance of
proposed observer in comparison to extended Kalman filter. A
surface plasma resonance (SPR) biosensor is employed (Vostiar et
al.) [10] to monitor the profile of heat shock protein and hetero-
logous protein to map the dynamics of cellular stress response in
Escherichia coli. Results demonstrate the feasibility of using SPR in
two channel protein array for intra cellular component monitor-
ing. Two approaches are proposed by Ward et al. [11] for pre-
diction of total alkalinity of anaerobic digester. These soft sensors
are based on multiple regression algorithm and Near Infra-red
spectroscopy (NIRS). Results reveal that NIRS method produces
best model during validation of new data. Sharma and Tambe [12]
developed a soft sensor based on genetic programming (GP) ex-
tracellular production of lipase enzyme and bacterial production of
3-hydroxybutyrate-co-3 hydroxyvalerate. Performance of GP
based soft sensor is then compared with MLP and SVR soft sensors.
Results show the superiority of proposed soft sensor. Sagmeister
et al. [13] proposed a novel soft sensor for investigation of mixed
feed bioprocesses for control of the specific uptake rates of pri-
mary and secondary substrate via amalgamation of inline spec-
troscopy and rate base soft sensor. The performance of proposed
soft sensor is then evaluated on a recombinant Escherichia coli
pBAD mixed feed process. Results demonstrate that uptake rate
increases more than 1 g/gh and adaptation time to L-arabinose is
also reduced to 10 min. Wechselberger et al. [14] used soft sensor
based on first principle for the real time estimation of biomass and
specific growth rate for recombinant fed batch process. The pro-
posed approach is superior in terms of gross error and sensor
failure w.r.t other state of art methods. Results show the accuracy
of developed sensor for biomass and specific growth rate estima-
tion in real time. Vinod et al. [15] simulated a model of biode-
gradation process in a fluidized bed bioreactor (FBR) using genetic
algorithm trained feedforward neural network (FFNN). Pseudo-
monas sp. micro-organism is used in the study. Results reveal that
the diffusivities of phenol and oxygen in biofilm estimated from
simulation are comparable with literature values. Other papers
describe soft sensor based control of bioprocess for production of
antibiotics in pharmaceutical industry (Bravo et al.) [16], online
estimation of culture conditions and monitoring of recombinant
protein in fully automated multistage production process (Frickle
et al.) [17].
It is revealed from the literature that inferential or soft sensing
techniques have gained tremendous attraction as a sustainable al-
ternative to hardware sensors. Therefore the present research work
focuses on the development of soft sensor for inferential control of
ethanol concentration in a bioreactor. Diverse combinations of soft
sensor based on multilayer perceptron neural network (MLPNN),
genetic programming based neural network (GP-NN), partial least
square method (PLS) etc., are developed for bioprocesses. These soft
sensors are used to estimate the important variables based on
secondary measurements, but these are not utilized for the control
purpose. Further it is also revealed from literature that final product
concentration of bioreactor is controlled indirectly by controlling
the reactor temperature [18–20] using different control schemes.
The problem associated with such type of control is that ethanol
concentration depends not only on temperature of the reactor but
also on other factors such as pH, dissolved oxygen content, sub-
strate feed flow, specific growth rate of biomass etc. Therefore
without the measurement of ethanol concentration the efficient
control cannot be achieved. Due to the advancement in the field of
soft sensor, it is possible to control and simultaneously monitor
such parameters which are not readily available for measurement.
Asha et al. [21] designed inferential control scheme based on
ADALINE soft sensor and TL tuned PID controller for composition
control of distillation column. The performance of ADALINE soft
sensor is further improved by designing a dynamic ADALINE using
past measurements. However in the present work conventional
PID is replaced by non-dominated sorted genetic algorithm-II
(NSGA-II) tuned 2-DOF-PID along with retrained ADALINE which
leads to RAN2PID control scheme. The previously suggested in-
ferential control scheme [21] is also applied for monitoring and
control of ethanol concentration in bioreactor. It is observed that
the performance of the proposed control scheme is better than the
one available in literature. A rigorous literature survey also reveals
that it is the first time RAN2PID control scheme is used for mon-
itoring and control of fermentation process.
The paper is organized as follows: A detailed mathematical
modelling of bioreactor is discussed in Section 2. In Section 3
development of soft sensor using four types of neural network is
discussed. Section 4 describes the inferential control scheme using
developed soft sensor for control of ethanol concentration, Sec-
tions 5 and 6 includes the discussion and conclusion of the re-
search work respectively.
2. Mathematical model of continuous fermentation bioreactor
Any real process can be represented in the form of a mathe-
matical model. It encourages the control engineers to evaluate the
performance of any new algorithm without affecting the real
process. However to design mathematical model few assumptions
are considered in order to make mathematical model realizable
and computationally less complex. The control algorithms tested
on the model may be implemented directly on the real process. It
serves as an alternative to the real process and facilitates [22] the
control engineers for the evaluation of controller tuning, de-
termining the effects of disturbances, optimizing plant operation
and investigating potential safety problems without disturbing the
actual process. Thus dynamic mathematical models are essential,
efficient and powerful tools for testing new control schemes. After
getting the desired results on simulated model, the next step is to
implement the scheme on the real time process.
In a continuous fermentation bioreactor feed is continuously
added to the system. The speed agitator for gentle stirring is
chosen so as to keep cells in suspension, to provide sufficient
mixing and to avoid extreme shear forces that may cause muti-
lation of the cells. A product stream is removed continuously from
the bioreactor. Fermentation of alcohol is one of the most popular
and important bioprocess because of its product, ethanol. Ethanol
is used as partial substitute for gasoline and is thus an alternate
energy source. The bioreactor under consideration is a CSTR with
following assumptions [18–20].
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250236

3. Perfect mixing
Constant stirring speed
pH of the bioreactor is constant
Constant substrate feed flow, the outlet flow from the reactor
containing the product
Input concentration of substrate and biomass are constant
Bioreactor has three main constituents; biomass a suspen-
sion of micro-organism (Saccharomyces cerevisiae, yeast);
substrate, a solution of glucose on which microorganisms feed
and the product (ethanol) is taken out continuously along with
other reactor components. The dilution rate (Fe/V) for bior-
eactor should be lower in comparison to production rate of
biomass. The cell kinetics of the present model is based on
modified Monod equations according to Michaelis-Menten
kinetics presented by Aiba et al . [23].
μ μ=
+ ( )
−S
K S
e
1o
s
K P1
Fermentation processes have slow dynamics and inorganic
salts are added to the yeast. These salts are responsible for the
formation of co-enzymes and equilibrium concentration of
oxygen is greatly affected along with reactor temperature.
The mass balance equations for biomass, product, substrate and
dissolved oxygen are as follows:
μ=
+
−
( )
−dC
dt
C
C
K C
e
F
V
C
2
X
X X
s
s s
K C e
X
P P
Where the maximum specific growth rate μX
depends on the
temperature and heat denaturation
( )μ = − ( )
−( ( + )) − ( + )
A e A e 3X
E R T E R T
1
/ 273
2
/ 273a r a r1 2
μ=
+
−
( )
−dC
dt
C
C
K C
e
F
V
C
4
P
P X
s
s s
K C e
P
1
P P1
In the above equations, first terms signify the quantity of bio-
mass and product during fermentation, whereas the second terms
represent the quantity of yeast and ethanol leaving the reactor
respectively.
( )
μ μ= −
+
−
+
+ −− −
5
dC
dt R
C
C
K C
e
R
C
C
K C
e
F
V
C
F
V
C
1 1s
SX
X X
s
s s
KPCP
SP
P X
s
s s
KP CP i
s in
e
s
1
1
,
Where first two terms represent the quantity of substrate gobble
up by biomass for the progress and ethanol production respec-
tively. Third term denotes the amount of glucose inflow to the
reactor with new substrate feed, and last term is the amount of
glucose exit from the reactor.
The energy balance equation for reactor and jacket temperature
are given as follows
( )( ) ( )
( )
Δ
ρ ρ
= + − + + +
−
6
dT
dt
F
V
T
F
V
T
r H
C
K A T T
V C
273 273
32
r i
in
e
r
o r
r heat r
T T r ag
r heat r, ,
2
( )
( )
ρ
= − +
−
( )
dT
dt
F
V
T T
K A T T
V C 7
ag ag
j
in ag ag
T T r ag
j ag heat ag
,
,
The concentration of dissolved oxygen depends on stirring
speed and reactor temperature. As the stirring speed is as-
sumed to be constant, so the variation in dissolved oxygen
concentration is only due to the reactor temperature as shown
in Eqs. (8)–(12).
In the reaction medium concentration of dissolved oxygen is
given by
( )= ( ) * − −
( )
dC
dt
k a C C r
8
O
l O O O
2
2 2 2
The equilibrium concentration of oxygen in the liquid phase is
( )* = − + − * ( )
∑−
C T T T14.6 0.3943 0.00714 0.0000646 10 9O r r r
H I2 3 i i
2
The global effect of ionic strengths are given as follows
⎛
⎝
⎜⎜
⎞
⎠
⎟⎟
∑ = + +
+ + +
+ + ( )
− −( − )
HI H
m
M
M
V
H
m
M
M
V
H
m
M
M
V
H
m
M
m
M
M
V
H
m
M
M
V
H H
0.5 2 2
0.5 2 2
0.5 10 0.5 10 10
i i Na
NaCl
NaCl
Na
Ca
CaCO
CaCO
Ca
Mg
MgCl
MgCl
Mg
Cl
NaCl
NaCl
MgCl
MgCl
Cl
CO
CaCO
CaCO
CO
H
pH
OH
pH14
3
3
2
2
2
2
3
3
3
3
The mass transfer coefficient for oxygen and rate of oxygen
consumption are given by Eqs. (11) and (12) respectively
( ) = ( ) ( ) ( )
−
k a k a 1.024 11l l
T
0
20r
μ= * * *
+ ( )
r
Y
C
C
K C
1
12
O O
O
X
O
O O
2 2
2
2
2 2
where
Cs ¼ Glucose concentration
CP¼ Ethanol concentration
Fag¼ Flow rate of cooling agent
Tin¼ Temperature of input flow
Tr¼ Temperature of bio-reactor
Te¼ Temperature of outflow
Fi ¼ Input flow
Fe ¼ Exceeding flow
Tag¼ Temperature of cooling agent
CO2
¼ Dissolved concentration of oxygen
The coolant flow rate (Fag) changes the reactor temperature (Tr),
thereby changes the biomass concentration (CX) (Eq. 4). The
ethanol concentration CP changes due to variation in CX (Eq. 4),
hence it is observed that CP is indirectly affected by temperature
profile of bioreactor. In a reaction medium the concentration of
dissolved oxygen is also important for the growth of biomass,
which directly affects the product concentration (CP). The funda-
mental model parameters and nominal operating values are given
in Appendix A and B respectively. The mathematical model dis-
cussed above is simulated on Intels CoreTM
i5 CPU with 2.4 GHZ
frequency, 4 GB RAM in MATLAB version 8.0.1.604. Coolant flow
rate of jacket is varied randomly and corresponding temperature
and concentration variations are obtained. The simulation data
thus obtained is used by soft sensors to model the input–output
behavior of the system.
3. Development of soft sensor
Soft sensor or virtual sensor is an ordinary name for software
based sensors where various measurements are performed to-
gether like fault detection along with estimation of unmeasured
variables. The interaction of signals can be utilized for estima-
tion of new quantities that cannot be computed. A soft sensor is
a conceptual device whose inferred variable can be represented
in terms of other parameters that are applicable to equivalent
process. Neural network, Kalman filter, neuro-fuzzy are few
examples of soft sensors. However an intelligent sensor involves
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 237

4. various intelligent functions for example self-testing, self-
adaptation, self- identification etc. or it constitutes a sensing
element and signal processor on a single chip [24]. In the pre-
vious works Levenberg–Marquardt perceptron neural network
(LMPNN) and Back Propagation neural network (BPNN) are used
to develop soft sensor for chemical process. In [12] LM based
soft sensor is used for comparing the prediction performance of
proposed genetic programming based soft senor and Singh
et al. [25] developed an LM based estimator for estimation of
methanol concentration in binary distillation column. BPNN
neural network is also suggested by Assis and Filho [26] for
online state estimation in bioreactor.
BPNN uses gradient descent method for training purpose. It
suffers from slow convergence due to selection of step size, which
should be suitable to gradient and secondly due to unsymmetrical
error surface. The problem with BPNN is eliminated by LMPNN and
BRBNN. The LMPNN executes a combinatorial training process
which includes steepest descent and Gauss-Newton algorithm
around complex curvature. Firstly LM algorithm shifts to steepest
descent method and then to Gauss newton algorithm to speed up
convergence.
Further multilayer Bayesian Regularization back propagation
based neural network (BRBNN) is used in the field of stock
market forecasting [28] also designed for comparing the perfor-
mance of proposed soft sensor. In BRBNN [27,29], Bayes rules are
implemented to train the neural network in order to optimize
regularization. In this neural network Gauss Newton method is
applied to Hessian matrix and executed within the frame work of
LM algorithm to reduce the computational complexity. In this
paper a single layered neural network is used to build a soft
sensor in order to predict the ethanol concentration by using
easily measured process variables. In the procedure of building
inferential measurement system an introductory set of input
variables are selected on the basis of prior knowledge about the
process. Two input variables are selected to infer the ethanol
concentration.
( )= ( )C f T T, 13P r ag
where
CP ¼ Ethanol concentration
Tag ¼ Jacket temperature
Tr ¼ Reactor temperature
Adaptive linear neural network or single layered neural net-
work introduced by Bernard Widrow and Marcian Hoff in 1960,
uses Least Mean Square learning algorithm instead of perceptron
learning rule [30]. The perceptron rule provides guaranteed con-
vergence to a solution that accurately classifies the training data,
the trained network may become sensitive to noise. However the
LMS algorithm minimizes mean square error, which shifts the
decision boundaries far from the training patterns.
Output of ADALINE network can be written as
= ( * + ) = * + ( )x purelin p W b p W b 14
where Purelin is the linear transfer function which gives linear
relationship between input and output of neural network. W is the
weight matrix, b is the bias and p is the input data. In the present
work ADALINE based soft sensing technique is compared with
LMPNN, BRBNN and BPNN for prediction of ethanol concentration
from temperature measurements
3.1. (a) Training and Testing of soft sensors
The bioreactor is simulated by varying the flow rate of cooling
agent to generate 3500 samples for training and testing of neural
network. The data consists of reactor temperature (Tr), jacket
temperature (Tag) of the bioreactor and the corresponding ethanol
concentration (CP). ADALINE, LMPNN, BRBNN and BPNN are
trained and tested with generated samples, and a predictive neural
model is constructed. The desired prediction capability of soft
sensors is achieved by minimization of the error between the
predicted and the target value. Several suitable performance
measures suggested in literature are used like root mean square
error (RMSE), Mean absolute error (MAE), Relative square error
(RSE), MSE, MPAE, MARE etc. However the comparison of three
soft sensor models is carried out using MSE, MAPE and MARE. The
usefulness and brief description of these error measures is given
below:
Mean square error (MSE) averages the squares of various errors.
Due to squaring more weightage is given to large errors than
smaller ones. Therefore MSE is used where large errors are pre-
sented whose negative consequences are proportionately bigger as
compared to smaller ones.
=
∑ ( − )
( )
MSE
Y P
Y 15
2
Mean absolute percentage error (MAPE) is defined as a per-
centage of actual data and it is a relative measure.
=
∑ −
×
( )
MAPE
Y P
Y
100%
16
Mean absolute relative error (MARE) gives an indication of the
average deviation of the predicted values relative to actual values.
=
∑ ( − )
( )
MARE
Y P Y
Y
/
17
( − )Y P Y/ Gives the value of absolute error relative to actual
values; where Y and P are actual and predictive values respectively.
Thus MSE provides the predictive accuracy of soft sensor w.r.t
large errors whereas the biggest advantage of MAPE is its simpli-
city and non-rational way of judging the extent of error. MARE is
not contaminated by outliers present in the samples, lower the
values of MARE, higher will be the predictive accuracy of soft
sensor. All the performance measures are evaluated to measure
the accuracy of the designed soft sensors in all aspects.
The performance of any neural network heavily depends on the
selection of hidden layers and number of neurons in each hidden
layers etc., literature does not suggest any thumb rule for deciding
the number of hidden layers and neurons in different layers.
However several authors have proposed certain methods to eval-
uate the number of hidden nodes but all are system specific
[31,32]. The structure of the soft sensor models in the present
work is optimized by following a systematic procedure. Initially
trial and error is used to evaluate the neural structure for LMPNN,
BPNN, and BRBNN. Two hidden layers are chosen first and number
of neurons in each hidden layer is varied and network with
minimum MSE is chosen for LMPNN, BPNN and BRBNN. Table 1
shows the MSE variation for different neurons in hidden layers for
LMPNN, BPNN and BRBNN.
The LMPNN architecture (2–20-20-1) with 2 neurons in input,
20 neurons in each hidden layer and 1 neuron in output layer is
chosen as the number of neuron increases additionally MSE does
not change much, further for BPNN (2–28-28-1) and BRBNN (2–24-
24-1) architecture are chosen. Whereas ADALINE is a single
layered neural network for which the number of neurons depend
upon the number of outputs.
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250238

5. The next step in the design of soft sensors is to optimize the
size of training and testing set. It is revealed from the literature
(Table 2) that no empirical rule is suggested for deciding the
training and testing data set. Therefore different combinations
of training and testing samples are tried and tested for the
present work.
Various error functions used for quantitative analysis of ADA-
LINE, LMPNN, BPNN and BRBNN are given in Table 3 for different
testing samples. As the training samples increase, the prediction
accuracy of soft sensors also increases. The main aim of the soft
sensor is to provide the better prediction with minimum error.
Therefore 3450 samples are used for training and 50 samples for
testing i.e. 98-2%. The predicted concentration by different soft
sensors and the desired concentration are shown in Fig. 1. It is
clearly observed from results that ADALINE estimated concentra-
tion is almost coinciding with the desired concentration. It is also
observed that the error between the target and estimated value
are higher in case of and LMPNN, BPNN, and BRBNN as compared
to ADALINE for testing samples. The good performance of ADALINE
is further verified from the lower values of error functions in
comparison to other neural networks. This is due to the reason
ADALINE is a single layer neural network and uses a powerful
learning algorithm, LMS. In this work two variables i.e. reactor
temperature and jacket temperature are chosen as input to the
soft sensor in order to infer the ethanol concentration. The MSE
obtained after testing the soft sensor for these two inputs is
0.2596. The other interlinked parameters i.e. concentration of
Dissolved oxygen (DO) and time are also considered as input
variables along with the two temperature inputs to the soft sensor.
The MSE with reactor temperature, jacket temperature and DO as
inputs is 0.2592 and MSE after addition of time to the input is
0.2583. It is observed that MSE obtained does not show significant
change. This is due to the reason that range of DO is very less and
change in product concentration is almost linear with respect to
DO. Therefore in the present work only two temperature inputs
are considered so as to reduce the memory requirement and ex-
ecution time.
3.2. (b) Validation of soft sensors
Apart from training and testing, validation of soft sensors is
also carried out. The previously designed soft sensors are validated
for new dataset. Therefore 200 samples are chosen for validation
purpose, which are different from training and testing data set
used in the previous section. It is clearly observed from Fig. 2 that
prediction of ADALINE for new samples is better and thus error is
less in comparison to LMPNN, BPNN, and BRBNN. Table 4 shows
the quantitative analysis of soft sensor for validation data set. It is
revealed from the table that prediction accuracy of ADALINE is
higher in comparison to LMPNN, BPNN and BRBNN.
3.3. (c) Robustness testing of soft sensors for real time data
Soft sensors are extensively used in biochemical industries
for prediction of unmeasured variables. In Section 3.1(a) and
(b) the performance of soft sensors is tested and validated on
the data obtained from simulation model. The bioreactor
model under consideration is semi rigorous due to the as-
sumptions considered for simulation. Further in order to relate
the presented soft sensor to real time environment, the effec-
tiveness of soft sensors is tested on real time data of industrial
scale fed-batch fermentation process [37]. The mixture of lac-
tose and yeast with some minerals and soybean oil are sup-
plied to the fermenter. The agitator rotates and mixes the
whole mixture properly. Soybean oil is utilized as antifoaming
agent and additional carbon supplier. The flow rate of sugar
and soybean is controlled using successive batch control,
which uses a predetermined optimal profile. The vessel tem-
perature and pH of the mixture should be maintained at 298 K
and 6.5 respectively. The coolant and acid/base flow are the
manipulating variables to control the temperature and pH. The
fermentation process comprises 100,000 l bioreactor with
2.1 m radius and 100 rpm stirring speed. Various hardware
sensors for continuous measurement of temperature, pH and
dissolved oxygen concentration are mounted. Cooling coils are
installed internally through which coolant flows [38]. The data
utilized for evaluating the robustness of soft sensors consists of
Table 1
MSE variation for LMPNN and BRBNN.
No. of neurons in each hidden
layer
LMPNN (MSE) BPNN (MSE) BRBNN (MSE)
5 1.1120 1.5320 1.6032
8 0.9219 1.4780 0.8420
10 0.8591 1.3923 0.8431
15 0.8429 1.2143 0.9840
20 0.8324 1.1942 0.7892
22 0.8335 1.1732 0.5928
24 0.8334 1.1238 0.5616
26 0.8323 1.0982 0.5618
28 ……… 1.0523 0.5620
30 ……… 1.0643 ………
32 ……… 1.0624 ………
Table 2
Size of training and testing data set in different literatures.
Author's Training
sample
Testing
sample
Training and testing
percentage
Sharma and Tambe
2014 [12]
119 51 70–30%
Asha et al. 2013 [21] 370 30 92–8%
Raghavan et al. 2011
[33]
3000 Not specified Not specified
Kenako and Funatsu
2013 [34]
100 1000 9–91%
Wu et al. 2015 [35] 480 80 85–15%
Bahar and Ozgen 2010
[36]
Not specified Not specified Not specified
Table 3
Quantitative analysis of soft sensors for different training and testing data set.
Training and testing data ADALINE LMPNN (2–20-20-1) [12,25] BPNN (2–28-28-1) [26] BRBNN (2–24-24-1) [28]
MSE MAPE MARE MSE MAPE MARE MSE MAPE MARE MSE MAPE MARE
70-30% 0.7858 6.4720 0.0639 0.9654 8.6432 0.0924 1.2342 8.6431 0.0945 0.8324 7.5320 0.0739
80-20% 0.6435 5.8306 0.0571 0.9061 7.5013 0.0752 1.1623 7.9102 0.0823 0.7902 6.4283 0.0615
90-10% 0.5327 4.9413 0.0428 0.8847 6.0415 0.0631 1.1434 7.5243 0.0731 0.6651 5.8291 0.0543
95-5% 0.3628 3.7805 0.0345 0.8520 5.8950 0.0580 1.0928 6.7840 0.0672 0.5923 5.0240 0.0461
98-2% 0.2596 3.2021 0.0302 0.8324 5.345 0.0532 1.0529 6.5741 0.0657 0.5616 4.6216 0.0462
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 239

6. 2 inputs i.e. temperature, and dissolved oxygen concentration,
whereas Penicillin concentration is the output. The data con-
sists of 1580 samples out of which 1550 patterns are used for
training the soft sensor and 30 for testing. Fig. 3(a) shows the
estimated penicillin concentration by all the designed soft
sensors. The MSE of prediction obtained from all the soft
sensors are shown in Fig. 4. It is revealed from results that the
prediction performance of ADALINE soft sensor is more effi-
cient in comparison to other designed sensors, which moti-
vates for its use in inferential control scheme as discussed in
the next section.
4. Soft sensor based inferential control of bioreactor
In any chemical process, the quality of final products is an es-
sential parameter to be controlled. It is observed from open loop
response of the model that concentration of ethanol takes large
time (approximately 200hrs) to settle. The objective is to achieve
the desired concentration as early as possible in the presence of
disturbances and noise. Further the measurement of product
quality using composition analyzers is a time delayed as well as
uneconomic task. Therefore soft sensor based inferential mea-
surements can be used in feedback configuration for process
control. In pharmaceutical industries, food processing and bev-
erages industries etc., inferential control is an increasingly used
methodology that allows process quality to be inferred from sec-
ondary measurements such as pressure, temperature, and flow etc.
Benefits of inferential measurements are fast retrieval of in-
formation, more consistent production as human involvement is
reduced and process is optimized. The implementation of this
method requires the selection of appropriate secondary variables.
In this article inferential control scheme comprises of soft sensor,
error detector and controller. As discussed earlier ADALINE soft
(a) Predicted Ethanol Concentration (b) Error between target and predicted values
0 10 20 30 40 50
-3
-2
-1
0
1
2
3
No.of samples
error
ADALINE
LMPNN
BRBNN
BPNN
0 10 20 30 40 50
11
11.5
12
12.5
13
13.5
14
14.5
15
15.5
No. of samples
Cp(ehanolconcentrationg/l)
Desired Concentration
ADALINE Predicted
LMPNN Predicted
BRBNN Predicted
BPNN Predicted
Fig. 1. (a) Predicted Ethanol Concentration. (b) Error between target and predicted values.
(a) Predicted Ethanol Concentration for validation dataset (b) Error between target and predicted values
0 50 100 150 200
10.5
11
11.5
12
12.5
13
13.5
14
No. of samples
)l/gnoitartencnoClonahtE(pC
80 90 100 110 120
11
12
13
14
Desired concentartion
ADALINE
LMPNN
BRBNN
BPNN
0 50 100 150 200
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
No. of samples
Error
ADALINE
LMPNN
BRBNN
BPNN
Fig. 2. (a) Predicted ethanol concentration for validation dataset. (b) Error between target and predicted values.
Table 4
Quantitative analysis of soft sensors for validation samples.
Soft sensor MSE MAPE MARE
ADALINE 0.3098 3.4370 0.0344
LMPNN 0.8452 5.8060 0.0581
BRBNN 0.5723 4.7528 0.0475
BPNN 0.9252 5.9012 0.0610
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250240

7. sensor provides accurate and fast measurements and therefore it is
used for the control purpose. The training of soft sensor is per-
formed offline with the help of simulation data obtained from the
open loop simulation of continuous bioreactor. The trained soft
sensor is then utilized in the closed loop control to provide the
composition estimation from the temperature profile of the pro-
cess. Error detector finds the error between reference and con-
trolled variable whereas two types of controllers PID and 2-DOF-
PID are designed to control final product quality of bioreactor.
4.1. PID control
Traditional PID control scheme is widely employed in almost
every process industry due to its simple structure and ease of
implementation. The Laplace transform of PID controller is given
by [39]
( ) = ( ) { + + } ( )M s E s K K s sK/ 18p i d
Where E(s) is the error between reference and process output and
Kp, Ki and Kd are proportional, integral and derivative gains of PID
controller. The controller may be effectively used only if these
gains are selected properly. The gains of the PID controller are
designed on the basis of tracking error. The basic block diagram of
PID controller is shown in Fig. 5.where ( )M s is the manipulating
variable given to the process, ( )Y s is the output obtained from
system and ( )R s is the reference input. PID controller takes the
error between R(s) and Y(s) as input and it generates the suitable
manipulated signal for the process in order to minimize the error
between R(s) and Y(s).
4.2. Two degree of freedom PID (2-DOF-PID) Control
The Laplace transform of classical PID controller (Eq. 18) is also
termed as one degree of freedom PID which can give better per-
formance for a single task i.e. to enhance the step response or for
load disturbance rejection [40]. Two degree of freedom PID (2DOF-
PID) is defined by the following equation.
⎛
⎝
⎜⎜
⎞
⎠
⎟⎟
( )
( ) = ( ) ( ( ) − ( )) + ( ( ) − ( )) +
( ( ( ) − ( )))
( + ) 19
M s E s K bR s Y s
K
s
R s Y s
K s cR s Y s
sK K N1 /
p
i d
d p
The above equation can be rewritten as
( ) = ( ) ( ) − ( ) ( ) ( )M s R s L s Y s H s 20
Where
( ) = + +
( + ) ( )
H s K
K
s
sK
sK K N1 / 21
p
i d
d p
(a) Predicted Penicillin Concentration for real time data set (b) Error between target and predicted values
15.5 16 16.5 17
34.75
34.8
34.85
34.9
34.95
35
0 5 10 15 20 25 30
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
No of Samples
EstimatedError
0 5 10 15 20 25 30
34.6
34.8
35
35.2
35.4
35.6
35.8
36
No of Samples
)L/g(noitartnecnoCnillicinePdetamitsE
Fig. 3. (a) Predicted Penicillin Concentration for real time data set. (b) Error between target and predicted values.
0
0.005
0.01
0.015
ADALINE LMPNN BRBNN BPNN
7.62E-5 6.86E-4 6.26E-4
1.43E-2
MSE
Fig. 4. MSE of estimation for all the soft sensors.
Y(s)M(s)
R(s) PID G(s)
E(s)
Fig. 5. Block diagram for PID controller in closed loop.
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 241

8. ( ) = + +
( + ) ( )
L s K b
K
s
sK c
sK K N1 / 22
p
i d
d p
Where Kp,Ki, and Kd are proportional, integral and derivative gain
respectively. ( )E s is the error signal, ( )M s is the control signal, ( )Y s
is the process output (CP), ( )R s is the set point (CPset), N is derivative
filter, c and b are the weights that affect the set point. The ( )L s and
( )H s functions not only maintain a desired output response but
also provide a good regulatory performance. Fig. 6 shows the block
diagram for 2-DOF-PID.
Values of all the gains and derivative filter are same for L(s) and
H(s) but appropriate values of b and c should be chosen in such a
way that they do not interact with each other for efficient per-
formance of the system. Two variants of soft sensors i.e. ADALINE
and Retrained ADALINE (R-ADALINE) are used for inferential con-
trol scheme of continuous bioreactor as discussed below.
4.3. ADALINE based inferential control scheme
The chemical processes have large time delay, therefore the
effect of temperature on the final concentration is detected after a
large time. This causes delay in the control of desired ethanol
concentration. Inferential control scheme reckoned in this article
uses reactor temperature (Tr), and jacket temperature (Tag) to
predict the product concentration. Thus value of concentration is
anticipated and required manipulation is done before the product
quality is actually affected. The proposed control scheme being
anticipatory may lead to an efficient and fast control. The devel-
oped ADALINE soft sensor is used to control ethanol production of
continuous bioreactor inferentially. Schematic diagram for con-
tinuous bioreactor with inferential controller is shown in Fig. 7. Eq.
(6) shows that reactor temperature depends on temperature of
cooling agent (Tag) which in turn depends on flow rate of cooling
agent, therefore reactor temperature is controlled by manipulating
the flow rate of cooling agent.
As discussed in Section 3.1 the soft sensors are trained offline
using 3450 samples and 50 samples are used for testing purpose.
The trained sensor is used to measure the ethanol concentration in
the feedback loop of bioreactor process. The ADALINE sensor uses
Tr and Tag as input and infer the ethanol concentration online. The
predicted concentration (CP) is compared with the desired con-
centration (CPset) in the error detector to generate the error signal,
which is further manipulated by various controllers. The ma-
nipulated variable changes the position of actuator or control valve
and hence the flow rate of cooling agent (Fag). Two types of con-
trollers are used to control product concentration inferentially
with ADALINE soft sensor i.e. PID (APID) and 2-DOF-PID (A2PID).
The Tyreus-Luyben (TL) tuning method is used to tune all the gain
parameters Kp, Ki and Kd of PID. The gain parameters Kp, Ki and Kd
for 2-DOF-PID are first evaluated using conventional tuning
method (TL) by considering N, b and c parameters values equal to
1, secondly the values of gain parameters are fixed while N, b and c
parameters are varied. The variation of b and c affects the over-
shoot and response of the system in transient state, whereas the
variation in the value of N affects the susceptibility of noise in
derivative action. The values of all governing parameters are given
in Table 5.
It is observed from Fig. 8 that ADALINE with 2-DOF-PID gives
better control of ethanol concentration. The overshoot and settling
time is significantly less for A2PID as compared to APID. The re-
actor temperature has a great impact on other process variables
and is thus a very important parameter for bioreactor. The in-
ferential control scheme not only controls the final product con-
centration but also optimally adjusts the reactor temperature and
L(s) G(s)
H(s)
R(s)
M(s)
Y(s)
D(s)
Fig. 6. Block diagram for 2-DOF-PID controller.
Error
F , C , T
Fe, Cs, CX , CP
,Tr
,Co2
Soft sensor
CP,set
Tr
Tag
CP,m
PID / 2-DOF-PID
Fag
Inferential control scheme
C = Cell Concentration
C = Glucose Concentration
C = Ethanol Concentration
C = Dissolved oxygen concentration
T = Reactor Temperature
T = Jacket temperature
F = Flow rate of cooling agent
Manipulated variable, m
pH =6
Fig. 7. Schematic diagram for inferential control of continuous bioreactor.
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250242

9. the yeast concentration according to the desired ethanol con-
centration. The performance of ADALINE is further enhanced by
continuous online training as discussed in the next section.
4.4. R-ADALINE based inferential control scheme
ADALINE based inferential control scheme uses neural network
as soft sensor. The neural network builds input-output relation-
ship in terms of layer weights on the basis of training samples. If
there is a sudden change in the process parameters due to some
disturbance or external noise in the system, inputs of the sensor
may deviate from the range of training samples, leading to errors
in predictions. In order to overcome such problems, the neural
network must be retrained with past measurements and latest
information of system dynamics may be added instantaneously.
The ADALINE trains fast as it is a single layered neural network
and works efficiently with large training data, this capability may
be utilized to make it adaptive towards parameter variations.
Therefore ADALINE is retrained dynamically which leads to re-
trained ADALINE soft sensor. The added information allows the
soft sensor to adapt any change in the input variables of the pro-
cess. In this inferential control scheme again PID and 2-DOF-PID
are used for control of ethanol concentration with dynamic or
retrained ADALINE which leads to two inferential controllers
named as R-ADALINE with PID inferential controller (RAPID) and
R-ADALINE with 2-DOF-PID (RA2PID). The PID and 2-DOF-PID
controllers are tuned using the same method as in APID and A2PID
controllers discussed previously. The tuning parameters of the two
controllers are given in Table 6.
Fig. 9(a) shows the ethanol concentration of bioreactor con-
trolled by various inferential controllers. The R-ADALINE sensor
can predict the concentration more accurately as its training data
set is increased and thus any change in input process variable may
be adapted efficiently and effectively. The mean square error of
prediction for ADALINE is 0.2596 and for retrained ADALINE is
0.1096 which verifies the improvement in efficiency of prediction
by R-ADALINE. It is observed from the Fig. 9 that the RAPID and
RA2PID maintain the concentration at the set point more
Table 5
Tuning parameters of controller for APID and A2PID.
Controllers Kp Ki Kd N B c
APID 20.85 0.856 220.88 – – –
A2PID 17.28 0.826 228.99 4.486 0.9684 0.9947
(a) Ethanol concentration (b) Yeast concentration
(c) Reactor temperature (d) Jacket temperature
0 100 200 300 400 500 600 700 800 900 1000
12.5
13
13.5
14
14.5
15
15.5
Time (hr)
Ethanolconcentration-Cp(g/l)
set point
APID
A2PID
0 100 200 300 400 500 600 700 800 900 1000
0.9
0.95
1
1.05
1.1
1.15
1.2
1.25
1.3
1.35
1.4
Time (hr)
Yeastconcentration-Cx(g/l)
APID
A2PID
0 100 200 300 400 500 600 700 800 900 1000
29
30
31
32
33
34
35
36
Time (hr)
).itneceerged(rT-erutarepmeTrotcaeR
APID
A2PID
0 100 200 300 400 500 600 700 800 900 1000
27
28
29
30
31
32
33
34
35
36
Time (hr)
).itneceerged(gaT-erutarepmeTtekcaJ
APID
A2PID
Fig. 8. Inferential control of continuous bioreactor by APID and A2PID.
Table 6
Tuning parameters of controller for RAPID and RA2PID.
Controllers Kp Ki Kd N b C
RAPID 26.47 0.628 232.25 – – –
RA2PID 14.079 0.429 142.83 3.862 0.935 0.8934
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 243

10. accurately with minimum settling time and overshoot in com-
parison to APID and A2PID controllers. Further the comparison
also shows that introduction of 2-DOF-PID controller also im-
proves the performance. This shows the capability of the 2-DOF-
PID controller to maintain the desired profile in the presence of
major or minor changes. Substrate concentration, reactor tem-
perature and jacket temperature are also controlled efficiently
which have great impact on ethanol production. Table 7 shows the
performance indices comparison of all the controllers.
From Table 7 it is revealed that the overshoot, settling time and
other parameters are reduced significantly by the use of retrained
ADALINE sensor along with 2-DOF-PID controller. Thus RA2PID
controls the ethanol concentration in a better way in comparison
to other designed controllers. It is also revealed that the perfor-
mance indices of A2PID are better as compared to RAPID, the
reason is that the parameters b and c in 2-DOF-PID simultaneously
improve the transient and steady state performance of the system.
Tyreus-Luyben method is used to tune the gain parameter for
2-DOF-PID and rigorous experimentation is done to calculate the
optimum value of derivative filter N and two weight factors b and
c. In order to improve the performance of RA2PID a multi-objec-
tive optimization technique called Non sorted genetic algorithm-II
(NASGA-II) is used to obtain the optimum values for proportional
gain (Kp), Integral gain (Ki), derivative gain (Kd), derivative filter (N)
and two weight factors b and c of 2-DOF-PID controller, which
leads to R-ADALINE with NASGA –II tuned 2-DOF-PID controller i.e.
RAN2PID.
4.4.1. Non-dominated sorting genetic algorithm-II
Srinivas and Deb [41] proposed a modification to simple Ge-
netic Algorithm (GA) called as Non-Dominated Sorting Genetic
Algorithm (NSGA) for multi-objective optimization, which is suc-
cessfully applied to different problems. But the main cause of
criticism is its high computational complexity, lack of elitism and
choice of optimal parameter values. Deb et al . [42] further sug-
gested a non-dominated sorting-based multi-objective Evolu-
tionary Algorithm (MOEA), called non-dominated sorting genetic
algorithm II (NSGA-II), which overcomes all the above difficulties.
In NSGA-II the population size N is initialized similar to GA. After
initialization of population, all the individuals are arranged based
on non-domination. Each individual or solution is assigned rank
values equal to its non-domination level. Fitness value of 1 is given
(a) Ethanol concentration (b) Yeast concentration
(c) Reactor temperature (d) Jacket temperature
0 100 200 300 400 500 600 700 800 900 1000
12.5
13
13.5
14
14.5
15
15.5
Time (hr)
Ethanolconcentration-Cp(g/l)
set point
APID
RAPID
A2PID
RA2PID
0 100 200 300 400 500 600 700 800 900 1000
0.9
0.95
1
1.05
1.1
1.15
1.2
1.25
1.3
1.35
1.4
Time (hr)
Yeastconcentration-Cx(g/l)
APID
RA2PID
A2PID
RA2PID
0 100 200 300 400 500 600 700 800 900 1000
29
30
31
32
33
34
35
36
Time (hr)
).itneceerged(rT-erutarepmeTrotcaeR
APID
RAPID
A2PID
RA2PID
0 100 200 300 400 500 600 700 800 900 1000
27
28
29
30
31
32
33
34
35
36
Time (hr)
).itneceerged(gaT-erutarepmeTtekcaJ
APID
RAPID
A2PID
RA2PID
Fig. 9. RAPID and RA2PID based control for Ethanol concentration of bioreactor process.
Table 7
Performance indices of designed controllers for ethanol concentration.
Controllers % Overshoot Settling time (hr) Rise time (hr) Steady state error g/l
APID 8.0839 104.4261 13.1512 0.2428
A2PID 5.8833 87.8710 12.8090 0.1303
RAPID 7.5443 103.6240 12.9389 0.0582
RA2PID 3.6452 71.6281 12.7130 0.0328
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250244

11. to an individual whose non domination level is best and fitness
value of 2 is assigned to the second non dominant individual and
so on. A new factor called crowding distance is calculated for each
individual apart from the fitness value. The crowding distance is a
criterion based on the comparison of congestion around a solution
and is used in the selection of parents for a new individual and the
new population. A greater crowding distance is preferred in order
to maintain the diversity of the solutions. Then a children popu-
lation of size N is created by applying the genetic operator i.e.
binary tournament selection based on the rank and crowding
distance, recombination and mutation. The population of parents
and children are reunited forming a temporary population of size
2N. The new population is again sorted according to non-Pareto
front on the basis of rank and crowding distance method. The
whole process is repeated until no front is accommodated. Gov-
erning parameters of NSGA-II are given in Table 8.
As discussed in Section 4.4, RA2PID inferential controller shows
significantly better control performance in comparison to the
other designed controllers. The gain parameters of 2-DOF-PID
controller are obtained from Tyreus-Luyben tuning method
whereas remaining parameters are obtained from rigorous ex-
perimentation. There is still a scope of improvement in the per-
formance of RA2PID which can be achieved by optimizing the
parameters of 2-DOF-PID. The optimization problem minimizes
two objective functions i.e. rise time and settling time. The lower
and upper limit of decision variables for 2-DOF-PID
are: ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤K K K N b12 18,0.1256 0.6781,138 146,1 5,0 1p i d
≤ ≤cand 0 1 Pareto-optimal sets of optimization problem after
30 generations are shown in Fig. 10 for rise time and settling time.
The optimum values of NSGA-II tuned 2-DOF-PID are, Kp¼16.1695,
Ki ¼ 0.4856, Kd¼142.714, N¼3.492, b¼0.904 and c¼0.926.
The performance comparison of RAN2PID with other designed
controllers for ethanol concentration control is given in Fig. 11.
RAN2PID improves the transient as well as steady state performance
of the process. The dissolved oxygen concentration is an important
parameter which depends on reactor temperature (Tr). Eq. (8) shows
the dissolved oxygen concentration in reaction medium with con-
stant volume, which depends on two important variables i.e. equi-
librium concentration of oxygen in the liquid phase ( *CO2
) and mass
transfer coefficient ( )k al . From Eq. (9) and Eq. (11) it is clearly seen
that both the parameters vary according to the reactor temperature.
The RAN2PID not only controls the ethanol concentration and re-
actor temperature precisely but also controls the DO and other in-
terlinked parameter effectively in minimum time. The improvement
in performance of RAN2PID is verified from the quantitative analysis
given in Table 9 in comparison to other designed inferential con-
trollers. Thus it is obvious from the results that NSGA-II tuned
2-DOF-PID controller provides better performance for concentration
control of bioreactor process.
4.5. Robustness analysis
A controller must be robust enough to provide satisfactory
control performance in the facets of disturbance and changes in
set point. The designed controllers are tested for set point tracking
and disturbance rejection in order to verify the robustness.
4.5.1. Set-point tracking
In case of nonlinear process, change in set-point may com-
pletely change the behavior of the process. The controller once
tuned at some fixed operating point should not require the re-
tuning for changes in set point. The robustness of controller may
be tested by its application on some set points for which it is not
tuned. Testing the controller for different set-points, also verifies
the robustness of the controller to handle uncertainties in the
process. For this purpose the designed controllers are tested for
positive and negative changes in the set point. The ethanol con-
centration is changed from 14 g/l to 15 g/l, 15 g/l to 13 g/l and 13 g/
l to 14 g/l at intervals of 300 h. It is observed from the results
shown in Fig. 12 (a) that RAN2PID reduces the overshoot and
settling time significantly in comparison to other designed con-
trollers. In this case also the RAN2PID outperforms the other de-
signed controllers. The corresponding variations of manipulating
variable are shown in Fig. 12 (b) and found in acceptable range.
The comparison of Integral square error (ISE) and Integral absolute
error (IAE) for various designed controllers for entire time axis are
shown in Fig. 13. It is obvious from the results that proposed in-
ferential control scheme adapts the changes in the set-point quite
efficiently.
4.5.2. Disturbance rejection
The main problem with all chemical processes is the sudden
change in input variable or generation of some external noise in the
system which affects the output significantly. These external dis-
turbances affect the final product of bioreactor. So controller must
be robust which not only rejects the disturbance but at the same
time maintains the product quality as well. As discussed earlier
temperature is a very important and sensitive parameter for any
chemical process and has a great impact on process operation.
Therefore a 75% change in input temperature of feed is taken as
disturbance for the process. Fig. 14 (a) and (b) shows the dis-
turbance rejection capability of all the designed controllers for po-
sitive and negative changes in the input temperature. Table 10
shows the quantitative analysis of all the controllers for disturbance
rejection in terms of mean absolute error (MAE). It is revealed from
the analysis that RAN2PID is the most efficient controller in re-
jecting disturbances among the designed controllers.
Disturbance may also be introduced into the system in the form
of noise, hence random white noise is added to the process in the
Table 8
Governing parameters of NSGA-II.
Parameter Method and value
Number of objective function 2
Number of design variables 6
Population size 20
Maximum Generation 30
Tournament pool size 2
Mutation method Polynomial
Crossover method Simulated binary crossover (SBC)
55 60 65 70 75 80
8
10
12
14
16
18
20
Settling Time
RiseTime
Fig. 10. Pareto-optimal set of optimization between settling time and rise time.
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 245

12. feedback path (sensor noise) [18]. The amplitude of white noise
considered varies from À1 to þ1, and its mean value is zero. The
original tuning parameters of the APID, A2PID, RAPID, RA2PID and
RAN2PID inferential controllers are kept unchanged. Fig. 15 (a) and
(b) show the comparison of process variables and that of control
outputs with noise, respectively.
From Fig. 15 (a), it is found that when the same noise is added
to two different set-points in the system, the control performance
of RAN2PID is much better in comparison to the other designed
controllers, as the concentration of ethanol is less oscillatory.
Fig. 15 (b) reveals that controlled output of RAN2PID inferential
control scheme is the most stable among designed controllers. The
(a) Ethanol concentration (b) Yeast concentration
(c) Reactor temperature (d) Jacket temperature
0 100 200 300 400 500 600 700 800 900 1000
12.5
13
13.5
14
14.5
15
15.5
Time (hr)
Ethanolconcentration-Cp(g/l)
set point
APID
RAPID
A2PID
RA2PID
RAN2PID
0 100 200 300 400 500 600 700 800 900 1000
0.9
0.95
1
1.05
1.1
1.15
1.2
1.25
1.3
1.35
1.4
Time (hr)
Yeastconcentration-Cx(g/l)
APID
RA2PID
A2PID
RA2PID
RAN2PID
0 100 200 300 400 500 600 700 800 900 1000
29
30
31
32
33
34
35
36
Time (hr)
).itneceerged(rT-erutarepmeTrotcaeR
APID
RAPID
A2PID
RA2PID
RAN2PID
0 100 200 300 400 500 600 700 800 900 1000
27
28
29
30
31
32
33
34
35
36
Time (hr)
).itneceerged(gaT-erutarepmeTtekcaJ
APID
RAPID
A2PID
RA2PID
RAN2PID
(e) Dissolved oxygen concentration
0 100 200 300 400 500 600 700 800 900 1000
2.5
2.6
2.7
2.8
2.9
3
3.1
3.2
Time (hr)
)l/gm2oC(noitartncnocnegyxodevlossiD
APID
RAPID
A2PID
RA2PID
RAN2PID
Fig. 11. Comparison of RAN2PID with other designed controllers for Ethanol concentration.
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250246

13. results are further verified from ISE (Fig. 16) for noise suppression.
Hence, the ability of noise suppression of the RAN2PID is much
better than the other controllers.
It is revealed from the above analysis that RAN2PID outper-
forms the other designed controllers. This is due to the fact that
this control scheme uses retrained ADALINE and optimized 2-DOF
PID controller. The retrained ADALINE uses added information of
immediate past so as to adapt the changes in the input variables.
Further optimized two degree of freedom PID has two extra
parameters b and c which enhance its performance. Thus RAN2PID
proves to be an efficient and robust controller for control of con-
tinuous bioreactor.
5. Discussion
Bioreactors are highly nonlinear and time delayed systems
and take several days (e.g. 5 days or 10 days) or hours (e.g. 200 h.)
to reach the desired set point [43–45]. It is due to the fact that
micro-organism takes time to grow, initially the output obtained
is less but as the specific growth rate of biomass increases with
temperature, the product concentration tends to the desired set
point. The objective of control schemes is to regulate the system
parameters (Tr, Tag), which highly affect the growth rate of micro-
organism and provides the favorable environmental conditions to
grow. Real time bioprocesses run continuously for long hours or
days and desired output is obtained after a long time. From Ta-
ble 9 it is observed that PID based inferential control schemes
take more than 100 h to settle. This is the simulation study
however when related to real time process, the settling time will
be of the same order [44]. Therefore the proposed inferential
control scheme, RAN2PID regulates the system parameters in
such a way that desired ethanol concentration is achieved within
60 h or within 2.5 days. Sampling time is a very important issue
in discrete time control systems. The value of sampling time is
suitably selected so that number of samples generated per second
should be enough for analysis. It directly affects the system re-
sponse. If the samples are obtained at large time intervals then
reconstructed signal gets distorted. In this work the Euler alge-
braic solver is used with sampling time of 0.01 seconds. Further
the trained soft sensor models the input-output relationship and
therefore requires very less convergence time (0.143 s) for pre-
diction of concentration. The rise time and settling time of the
controller (12.2350, 60.4982) are the measures of speed of the
controller response.
Transients are generated in a system in the presence of varying
inputs. The inputs vary during startup, sudden change in the value
of input (set point change), external disturbance and noise. This
transient behavior of the system lasts for a very short period if the
controller is suitably designed. It is revealed from the results that
proposed RAN2PID controller has lesser settling time i.e. transients
die out earlier in contrast to other designed inferential control
schemes. Further RAN2PID also shows acceptable transient per-
formance during set point tracking and disturbance rejection. The
presence of sensor noise disturbs the concentration profile and
affects the transient performance of designed controllers. The
proposed controller is able to reject the sensor noise of amplitude
ranging from À0.8 to þ0.8. In case of larger amplitude noise the
transient performance of all the controllers including the proposed
one is degraded. Generally suitable filters are employed to reduce
such sensor noise.
The semi-rigorous mathematical model of bioreactor process
considered for the present work does not represent the actual
system completely because of modelling assumptions. As dis-
cussed in Section 2 mathematical model facilitates the control
engineer to get an insight about the behavior of controller in the
Table 9
Performance indices comparison of RAN2PID with other designed controllers.
Controllers % Overshoot Settling Time
(hr)
Rise time (hr) Steady state error
(g/l)
APID 8.0839 104.4261 13.1512 0.2428
A2PID 5.8833 87.8710 12.8090 0.1303
RAPID 7.5443 103.6240 12.9389 0.0582
RA2PID 3.6452 71.6281 12.7130 0.0328
RAN2PID 2.7432 60.4982 12.2350 0.0257
(a) Ethanol concentration (b) Controller’s output
0 100 200 300 400 500 600 700 800 900 1000
11.5
12
12.5
13
13.5
14
14.5
15
15.5
16
Time (hr)
EthanolConcentrationCp(g/l)
setpoint
APID
RAPID
A2PID
RA2PID
RAN2PID
0 100 200 300 400 500 600 700 800 900 1000
0
20
40
60
80
100
120
140
Time (hr)
ControllerOutput
APID
RAPID
A2PID
RA2PID
RAN2PID
Fig. 12. Set-point tracking comparison for all designed controllers.
IAE
ISE
0
100
200
300
400
APID A2PID RAPID RA2PID RAN2PID
330.91
260.24 285.01
224.93 210.13
289.46
232.12 249.68
199.75 184.21
IAE ISE
Fig. 13. IAE and ISE analysis of set-point tracking for different controllers.
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 247

14. presence of disturbance, noise etc. without employing it on the
real process. While designing the model of any real process few
assumptions need to be considered. For instance, if the dy-
namics of pH are included in the mathematical model, it will
affect the dissolved oxygen concentration (Eqs. 8, 9, and 10)
which further affects the ethanol concentration. Similarly if
variable stirring speed is considered then due to non-uniform
mixing, growth rate of micro-organism will be affected which
consequently perturbs the final product concentration. There-
fore previously tuned inferential controller will not be able to
control ethanol concentration precisely. In order to improve the
performance of inferential control scheme, the soft sensor needs
to be retrained with the help of new dataset generated from
modified bioreactor model and parameters of 2-DOF-PID con-
troller must be re-tuned.
6. Conclusion
The present work deals with soft sensor based concentration
estimation and control of a continuous bioreactor. ADALINE
based soft sensor is designed for the purpose and its prediction
performance is found better than LMPNN, BRBNN, and BPNN.
Two inferential control schemes based on ADALINE with PID
(a) + 5% change in Tin (b) -5% change in Tin
0 100 200 300 400 500 600 700 800 900 1000
12.5
13
13.5
14
14.5
15
15.5
16
16.5
Time (hr)
EthanolConcentrationCp(g/l)
APID
RAPID
A2PID
RA2PID
RAN2PID
0 100 200 300 400 500 600 700 800 900 1000
12.5
13
13.5
14
14.5
15
Time (hr)
EthanolConcentrationCp(g/l)
APID
RAPID
A2PID
RA2PID
RAN2PID
Fig. 14. Comparison of designed controllers for positive and negative disturbance rejection.
Table 10
Quantitative analysis of all the controllers for disturbance rejection.
Controllers þ5% change in Tin À5% change in Tin
MAE MAE
APID 0.5683 0.4571
A2PID 0.5432 0.4318
RAPID 0.5524 0.4382
RA2PID 0.5241 0.4334
RAN2PID 0.4306 0.3834
(a) Noise suppression by various controllers (b) Controller output
0 100 200 300 400 500 600 700 800 900 1000
12
12.5
13
13.5
14
14.5
15
15.5
16
Time (hr)
EthanolConcentrationCp(g/l)
setpoint
APID
RAPID
A2PID
RA2PID
RAN2PID
0 100 200 300 400 500 600 700 800 900 1000
0
20
40
60
80
100
120
140
Time (hr)
Controllersoutput
APID
RAPID
A2PID
RA2PID
RAN2PID
Fig. 15. (a) Noise suppression by various controllers. (b) Controller output.
0
100
200
300
APID A2PID RAPID RA2PID RAN2PID
290.42
245.16 252.76 237.33 223.14
ISE
Fig. 16. Comparison of ISE for noise suppression.
N. Pachauri et al. / ISA Transactions 68 (2017) 235–250248

15. (APID) and 2-DOF-PID (A2PID) controllers are suggested. It is
revealed form the results that A2PID performs better than APID
due to the additional controller parameters. Further the perfor-
mance of ADALINE is improved by retraining it with past mea-
surements. The retrained ADALINE used with PID and 2-DOF-PID
leads to RAPID and RA2PID inferential control schemes. It is re-
vealed form the results that RA2PID controls ethanol concentra-
tion precisely with minimum overshoot and settling time. The
performance of RA2PID inferential controller is further enhanced
by optimizing the parameters of 2-DOF-PID with NSGA-II leading
to RAN2PID controller. The control performance of RAN2PID
controller is found better than other designed controllers for set-
point tracking, disturbance rejection and noise suppression.
Hence it is concluded from the results that retrained soft sensor
along with optimized 2-DOF-PID controller provides robust and
efficient control of bioreactor.
Appendix A. Fundamental model parameters
A1¼9.5 Â 108
(kla)0 ¼38 hÀ1
MMg¼24 g/mol
A2¼2.55 Â 1033
KO2 ¼8.86 mg/l MMgCl2 ¼95 g/mol
AT ¼1 m2
KP¼0.139 g/l MNa¼25 g/mol
Cheat,ag ¼4.18 JgÀ1
KÀ1
KP1 ¼0.07 g/l MNaCl¼58.5 g/mol
Cheat,r ¼4.18 JgÀ1
KÀ1
KS ¼ .03 g/l R¼8.31 J molÀ1
KÀ1
Ea1 ¼55,000 J/mol KS1¼1.68 g/l RSP ¼0.435
Ea2 ¼220,000 J/mol KT ¼3.6 Â 105
J hÀ1
mÀ2
KÀ1
RSX ¼0.607
HCa¼ À0.303 mCaCO3¼100 g YO2¼0.97 mg/mg
HCl ¼0.844 mMgCl2 ¼100 g ΔHr ¼518 kJ/molO2
HCO3¼0.485 mNaCl¼500 g μO2 ¼.5 hÀ1
HH ¼ À0.774 MCa¼40 g/mol μP ¼1.79 hÀ1
HMg¼ À0.314 MCaCO3¼90 g/mol ⍴ag¼1000 g/l
HNa¼ À0.550 MCl ¼35.5 g/mol ⍴r ¼1080 g/l
HOH ¼0.941 MCO3¼60 g/mol
Appendix B. Operating conditions of the process
Parameter Description Values
Fi Input Flow 51 l/h
Fe Output Flow 51 l/h
Tin Input flow temperature 25 °C
Te Output flow temperature 25 °C
Tin,ag Temperature of input cooling agent 15 °C
Cs,in Concentration of glucose input flow 60 g/l
kla Mass transfer coefficient for oxygen 38. (1024)Tr-20
V Total volume of reaction medium 1000 l
Vj Volume of the jacket 50 l
pH Potential of Hydrogen 6
Fag Flow rate of cooling agent 18 l/h
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