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A Review on Control System Applications in Industrial Processes
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ICCRDA 2020
IOP Conf. Series: Materials Science and Engineering 1022 (2021) 012010
IOP Publishing
doi:10.1088/1757-899X/1022/1/012010
1
A Review on Control System Applications in Industrial
Processes
P K Juneja1, S K Sunori2, A Sharma3, A Sharma4, H Pathak5, V Joshi6, P Bhasin7
1 Department of ECE, Graphic Era University, Dehradun, India
2 Department of ECE, Graphic Era Hill University, Bhimtal, Nainital, India
3 Department of PDP, Graphic Era University, Dehradun, 248001, India
4 Department of PDP, Graphic Era Hill University, Dehradun, India
5,6,7,Department of EE, Graphic Era University, Dehradun, India
abhinav_sharma2008@yahoo.com
Abstract. Present paper attempts to review the literature related to design of P-I-D control for
time delayed complex industrial process for single as well as for multivariable process with
interaction considerations, their decoupler design and time delay compensators. General
instrumentation of the industrial feedback control systems along with control system analysis
has been covered. Also it covers, control features of some paper mill sub-processes like
headbox operation, basis weight and retention.The importance of eliminating the effects of
interactions, among the process control loops inside a multi input multi output industrial
control system, has been discussed with the help of literature study. The importance of the
process dynamics knowledge for designing a control system has also been investigated. This
paper also investigates the significance and effectiveness of PID controllers through various
literature studies. The problems of the classical PID controllers such as constraints, presence of
disturbances etc.) can be removed by designing in combination with soft computing
techniques. Moreover, possibility of further enhancements in the PID controller with the
utilization of various schemes available, has been presented. The present status of control
systems in industrial processes in terms of various control parameters such as stability, dead
time compensation etc. has been presented and the future improvements have been stated.
1. Introduction
The For any process industry such as chemical industry, the designing of optimal control system is
extremely important. The kind of automation that is being employed in such industries is not very
efficient as it is based on classical approaches such as PID controllers. The undesirable effect of this is
twofold. One is failure to produce appropriate product quality and second is the consumption of
massive quantity of chemicals, water and other energy sources.
So, there is a need to be more focused in this direction in order to improve the productivity and
economic aspect of the process industries by minimizing the requirement of more chemicals, water
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and man power.This can be achieved by optimizing various design parameters by using various
modern control system techniques.
2. Instrumentation and Control Status
A prominent parameter which significantly degrades the performance by delaying the response of a
feedback-based control system is the dead time. Therefore, compensating this dead time is highly
required. Various techniques such as Smith predictor can be used
for this dead time compensation.
The response of a conventional PID control structure may be further enhanced with the use of
internal model control (IMC) technique.
This approach has exhibited a remarkable betterment in the output of process industries as far as
the efficient use of resources, productivity and economy is concerned.
Advanced control systems based on on-line sensors have shown a tremendous up-gradation in the
performance. In general, the sub processes which are involved in any process industry are not so
simple.Actually, they are complex in nature in the sense that many input and output variables are
associated with them with a severe interaction among them.This results in the formation of multiple
input-output loops in that process.
An efficient control system can only be designed for any process industry only when we have the
knowledge about its process dynamics. But the unfortunate part is that the availability of this dynamics
is very rare. It enforces the need of developing mathematical models of the considered process starting
from the very basic principles based on balance of mass and energy leading to a conceptual analytical
model of that process. This modeling of the processes having single input and single output (SISO) is
comparatively easy but very challenging for the processes having multiple inputs and multiple outputs
(MIMO). Generally, complex to solve and complicated models are provided by the basic methods,
therefore determination of parameters by performing simulation on such complex models is a
challenging work. Obtaining data set from the process industry, while the process is in running mode,
is not possible. This leads to non-availability of any primary data to initiate the design work. Few data
of abroad based mills is available in the literature but its non-reproducibility and non-practicality
restricts us to apply it in the designing. So, the ultimate solution to cope up with this problem is the use
of some advanced system identification technique.
3. Control System Analysis for SISOand MIMO Industrial Processes
The ‘dead time’ involvement is very common in the dynamics of any process industry. It makes the
designing of control structure a very tough task. As there is a high degree of interaction between input-
output variables of a MIMO process, therefore the loops need to be, first of all, decoupled.
But the unfortunate part is that the chemical industries hardly takes care of this aspect and are only
dependent on the suppliers of the control systems. The chemical process industries are generally based
on DCS (Distributed Control System).The customers have no knowledge of the design architecture of
DCS .Classical control systems are based on tuning of all the loops in the process by hit and trial
method.
The knowledge of process dynamics is a very critical parameter for an effective control system
design for a process with either interactive or non-interactive input-output variables. Unfortunately,
the estimation of the dead time of high value is a very challenging task. Moreover, determining the
values of various parameters involved in the process dynamics, such as peak overshoot, settling time,
rise time, dead time etc., cannot be determined by doing experiment in the process industry.
The estimation of performance of various process loops and the design of a robust control system is
seriously not possible without knowledge of values of these parameters. Therefore, in order to design
such systems, the design engineers go for using the data simulated by computers.
4. Modeling and Design of Control System
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Various tuning methods for PID controllers with their applications are proposed within the literature
such as tuning method given by Cohen–Coon [1], Direct Synthesis approach [2], IMC approach [3],
minimization of integral error criteria based tuning rules [4], Chidambaram [5], Shinskey [6], Kinney
[7], Milosawlewitsch-Aliaga M. et. al. [8] and Lengare et al. [9]
A deep study on ANN, based on knowledge base for finding practically feasible solutions of
several challenging problems of linear models, has been done by Scott et al. [10]. A comparison of
classical PID controllers with ANN controllers has been presented.
A detailed analysis on several sub processes involved in a paper mill and various operations (such
as improvement in energy efficiency, refining efficiency of centrifugal cleaner, simulation and
modeling) done on them has been performed by Banerjee et al. [11].
A detailed explanation, on consistency of properties of paper across the entire width of paper, has
been given by David wood [12].He explored the possibility of achieving this consistency of properties
by locating the slice very close to parallel. This is done by stabilization of the head box and slice lip
mounting against fluctuations in the temperature. He has also suggested that the control of head box
can be improved by the use of adjustable slice lip.
Wei Tang et al. [14] proposed an algorithm for auto-tuning of PID/PI controller which can control
large time delay processes.
Honghai Wang et al. [15] illustrated the issue of attaining the completed stabilizing set of PI
controllers for SOPDT model.
Cheng-qiang Yin et al. [16] proposed a method to control a class of time delayed unstable
processes through a modified version of Smith predictor.
C.B. Kadu and C.Y. Patil [17] presented PID controller tuning for FOPDT model (reduced) which
is comparatively easy and reliable, and can be determined by dual locus diagram. The resultant
controller obtained by this method offers effective tracking of set point and also rejection of the
disturbance.
P. Juneja et al. [18] studied a suitable process model which has been governed by mass balance
equations and which further corresponds to the basis weight and paper machine retention.
P.Juneja et al. [19] proposed a method of controlling and maintaining the liquor feed quality for
raw materials pulp production at the time of cooking in digesters, which in turn helps the paper
industry in acquiring a sustainable quality of paper production and converting this waste to wealth by
minimizing the solid waste disposal problem.
M.Chaturvedi et al. [20] suggested that most of the processes of chemical industry or a process
industry can be represented by FOPDT model. The errors due to delay time can be compensated by
applying suitable techniques.
M. Joshi et al. [21] performed the research considering a process of order 1 containing time delay.
The SOPDT & TOPDT representations were obtained from the FOPDT representation of the process
by the use of Skogestad’s half rule.
M. Chaturvedi et al. [22] emphasized that the dead time is mostly viewed in the industrial processes
as the energy or material is propagated frequently in such processes. The presence of dead time is
entirely undesirable for the process industry. It was concluded that the system stability decreases
because of effect of dead time.
J.Uniyal et al. [23] in their study suggested that a robustness based controller is required to
overcome the effects of disturbances in the process industry.
S.K.Sunori et al.[13] and S.Singh et al. [24] in their research work, considered an industrial process
with significant time delay. The improvement in the response has been achieved by incorporating the
dead time compensation technique in the control structure.
P. Kholia et al. [25] proposed that various industrial processes can be modeled as IPDT process
model. A hydraulic control, with inherent delay, implemented in application of position control, is
taken to study. With the use of multiple tuning methods, PID controllers were designed for
approximated process model. The stability and disturbance rejection capacity were improved by
approximating the delay applying Padé approximation of first order.
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S.K.Sunori et. al.[26] reduced a pH control FOPDT model of 16th order to 2nd order by balanced
truncation method and also designed controllers based on IMC, ZN, GA-IMC techniques for reduced
model. In their results, the performance of control system is found to be upgraded using GA technique.
S.K. Sunori et. al. [27] designed and studied a neuro-fuzzy controller for a MIMO process with
high multivariable interaction.
P Saini et. al. [28] designed a classical PI controller based on PSO technique for consistency of
stock in the paper making process. It was found that proposed approach, in comparison to most of the
ZN-PI, Tyreus-Luyben (TL-PI), and IMC-PI, offers comparatively good optimal performance relating
to the response of overshoot, relative stability, low-performance index and frequency values.
S.K.Sunori et. al. [29] considered a cane carrier system. They designed and compared the ARMAX
polynomial model with the ARX polynomial model for the considered process and concluded that
ARMAX model more closely maintains comparatively more similarities to the initial real process than
ARX.
P Verma et. al.[30] considered a MIMO paper machine model and designed models with eight
different methods including ZN continuous cycling, McAvoy, TL, ZN, Cohen-Coon method, IMC,
quarter decay ration, Chien, Hrones and Reswich method. They concluded that for SISO loops, IMC
controller is best for both the steady state and transient responses.
S.K Sunori et. al.[31] considered 2x2 MIMO system of a boiler turbine with high multivariable
interaction and designed controllers for it using conventional PID and MPC technique by first
investigating the stability using Niederlinski index and interaction analysis using RGA
recommendations for the selected plant.
Sunori et. al. [32] suggested that for sugarcane crushing mill process which is nonlinear and
complex in nature, MPC technique offers fast response with absence of steady state error, very good
transient response and absence of overshoot in comparison to PID and fuzzy controllers.
D K Kumar et. al. [33] implemented a mixed method for system with uncertain parameters. It
offers the advantages of being simple and achieving stability of ROM for the stable original system.
They suggested that method of moment matching may not guarantee to be good results alone, instead
obtains effective results when implemented in combination with some other method.
Sunori et. al. [34] investigated the robustness of a MPC based boiler turbine process. For the
testing, they used perturbed models of the system by changing time delays and time constants and
suggested that the system is robust and also the performance is very much close to the actual system.
Beerten et. al. [35] proposed a cable model order reduction for system interoperability literature
studies. The state-space form of the presented model can be minimized without losing the accuracy of
the converter interactions.
J Uniyal et. al. [36] in their study, suggested that a robustness-based controller is required to
overcome the effects of disturbances in the process industry.
Sambariya et. al. [37] (2016), used the stability equation method for reduction of the transfer
function of higher order into low order model, based on pole-zero patterns. They found the proposed
method to be very simple to implement and effective in terms of preserving the stability.
P Juneja et al. [38] performed the research for a process of first order system with delay (FOPDT).
The second order SOPDT and third order TOPDT models were derived, from FOPDT model of the
process by the use of Skogestad’s half rule.
S K Sunori, P K Juneja [39] selected lime kiln process with high multivariable interaction. They
analysed index of Niederlinski and RGA analysis for its stability consideration and interaction
respectively. The comparative analysis was performed between the performances of the designed PID
technique-based controllers and that of Fuzzy logic controllers. After decoupling of the system, the
composite MIMO system responses were compared with the closed loop step responses. They
concluded that lime kiln though an significant industrial process but offers various challenges, due to
being multivariable, complex, interactive and delayed process.
P Juneja, A K Ray, R Mitra [40] stated that artificial Intelligence, an intelligent rule-based
technique can be implemented to design the control system of lime kiln, which is significantly used in
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various process industries viz. cement, paper, glass, sugar, and leather and ceramics etc. They
presented two important artificial intelligence techniques, fuzzy logic control system-based techniques
and neural network adopted to control limekiln process.
S Sunori, M C Lohani, P Juneja, G S Jethi [41] considered a lime kiln process with frequent
occurrence of disturbances and complex multivariable interactions. They designed a prediction-based
controller and also investigated the disturbance rejection performance, under multiple prediction
horizon, control horizon and sampling interval values.
S K Sunori and P K Juneja [42] mentioned that the MPC technique is one of the popular techniques
and is stated to be the finest among various other existing ones of control of the constrained
multivariable dynamic plants. They utilized a linearized model obtained with the use of Taylor series
expansion about the operating point to control lime kiln process using MPC technique.
For a lime kiln delayed control system, MPC can offer more stable operations. MPC strategy, if
implemented can easily handle the constraints of manipulated and controlled variables [43].
P Juneja , A K Ray, R Mitra [44] reviewed a neural network approach and fuzzy logic control to
control the industrial limekiln process and concluded that for industrial processes with multivariable
and strongly nonlinear models like lime kiln, conventional control methods have comparatively poor
performances than modern control techniques.
P Juneja, A Ray and R Mitra [45] presented the analysis of the effects of constraints for a limekiln
process on manipulated variables and controlled variables. They explored the effects of control and
prediction horizon variations. The comparative results were presented for various weights and rate of
weights of significant variables. For the designed controllers, the disturbance rejection capabilities set-
point tracking and robustness were explored.
S Sunori, P K Juneja, A K Ray [46] stated that limekiln is a tedious to operate due to multivariable
nature, complex dynamics, long transportation lags and non linear kinetics. It is hazardeous in nature if
misoperated beyond set points. But MPC technique offers comparatively better solution for control
and optimization of this process.
In control of multiple-loop, MIMO systems are considered and operated as a collective set of
multiple number of single loops [47].
The area of control of distillation column, in the process industry is mostly benefitted by the
Decoupling control technique [48].
Xiong [49] proposed effective relative gain array (ERGA) method for reducing interaction in
MIMO processes by establishing the ratios of transmission of energy for a transfer function.
Dynamic RGA (DRGA) could possibly be utilized to analyze the plant under consideration at any
available frequency yet at one single frequency at one point of time only [50].
RGA can be used by generalizing effectively for the non-square plants [51].
Partial Relative Gain (PRG) can effectively resolve the pairing issue for very large systems.
Another set of RGA methods available in the literature are as follows: μ interaction index method,
PRGA (Performance Relative Gain Array) [52], GI (General Interaction) measure [53], NRGA
(normalized RGA) matrix.
The MIMO system stability is tested using Niederlinski Index [54]. Yang Bo et al. [55] suggested
an algorithmic technique for selecting the structure of control.
The knowledge offered by RGA about the most appropriate time to use the multivariable controller
is in a very limited form and also no indication is provided by it about the appropriate method of
selecting the multivariable controller structures [56].
Chien et al. [57] presented simple technique for the tuning of multiloop PID controller which can
be used for incomplete knowledge of process. For the processes with not only the multiple controlled
outputs but also the multiple manipulated variables, one solution for selecting one of the best feasible
SISO controllers out of the all the configurations available, is to attempt all possible loops one by one
and then choose those pairs of input-output which reduce the quantity of interaction among the SISO
controllers. Both the static as well as dynamic RGA methods attempt to reduce the interaction among
the SISO loops by choosing a suitable pairing yet are unable to achieve its complete absence.
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D Guhaa et. al. [58] investigated a coordinated hybrid energy distributed power system for its
dynamic behavior by developing the design of an optimized 3-degree-of-freedom PID controller. They
concluded that the controller designed is user friendly and serves the advantages of improved stability,
fast frequency active compensation and power oscillation.
Zafer Bingul et.al. [59] employed the PSO and ABC approach for tuning of the PID and FOPID for
the SOPDT system. From the simulation results, they concluded that the PSO algorithm is effective to
improve the step response of second order process. Also, ABC algorithm exhibited better performance
for the process with time delay. For the cost function variable, the simulation results revealed that
ABC controller is superior.
Farshad Merrikh-Bayat [60] proposed a LMI algorithm for tuning the MIMO PI/PD/PID controller
parameters. It served the advantage of eliminating the need of stabilizing controller for initializing the
control. This is an important research as in some control problems, finding a stabilizing controller is
not easy.
5. Summary and Conclusion
Classical PID controllers have so many shortcomings associated with them. One of them is that, with
PID controllers, it is very hard to handle presence of disturbance, dead time and constraints.
In order to overcome all these problems associated with the classical controllers, control systems
based on soft computing techniques are designed. Some examples of soft computing techniques are
GA, FLC and ANN. Some modern optimization techniques which are nowadays very frequently
adopted for control system optimization SA, ACO and PSO. A combination of these techniques is also
used for upgrading the response of the controller. However, role of classical PID controllers cannot be
ignored as these above-mentioned techniques are applied on the existing PID controller in order to
tune its parameters and optimize and its performance.
Efforts have been made for surveying literature regarding stability and control of single and
multivariable FOPDT process models. Prospects of refinements in terms of quality with rise of
productivity by the use of automation have also been discussed. This survey clearly indicates that the
PID controllers are very effective for a variety of industrial processes; therefore, their extensive use
has been carried on in various chemical industrial processes.
Finally, we conclude that there is still a wide scope of improvement in the control systems which
presently exist in the industrial processes.
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