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LITERATURE SURVEY
This chapter presents a brief review of earlier works done in liquid level control strategies and
various intelligent control techniques.
2.1 LIQUID LEVEL MONITORING AND CONTROL
Many earlier works dealt with various techniques of monitoring and controlling of liquid levels in
industrial and domestic applications. Broadly this automatic control problem can be achieved under
two means: mechanical methods and electrical methods. Float ball type liquid level control is a
popular method of control still used in practice for normal applications such as overhead tank
overflow restrictors etc. The electrical methods of control include a microcontroller-based circuits
which automatically predict the liquid levels and accordingly active the circuit to operate motors.
In spite of several such available methods, still there are new techniques in this application so as
avoid dangerous operating conditions in industrial boilers.
Tan [1] proposed a water level control system for nuclear steam generator. The control system
consisted of a feedback controller and a feedforward controller. The robustness and performance
of both the controllers are analysed and tuning of the 2 parameter of the controllers. It is shown that
the proposed gain scheduled controller can achieve good performance at high and low power levels.
Safarzadeh et al. [2] presented a water level control system for horizontal steam generators
using the quantitative feedback theory.
Moradi et al. [3] proposed a control strategy to achieve desired tracking of drum water level.
Sliding mode & H-∞ control schemes are employed. Transfer function between drum water level
(output) and feedwater vs. steam mass rate were considered.
Maffezoni [4] highlighted the principal dynamic phenomena which determine the structuring
of boiler-turbine control systems, clarifying the essential connections of such phenomena with the
physical nature of the process. Zhang and Hu [5] proposed the water level control system using PI
controllers. Zhang et al. [6] analysed the water level control of pressurized water reactor nuclear
power station using PID and fuzzy controllers. Ansarifar et al.[7] proposed an adaptive estimator
based dynamic sliding mode control method for water level control. Liu et al. [8] presented a
proportional controller with partial feed forward compensation and decoupling control for the steam generator water level.
INTELLIGENT CONTROL TECHNIQUES
In 1965, the concept of Fuzzy Logic was conceived by Prof. LotfiZadeh at the University of
California at Berkley. He presented fuzzy set theory not as a control methodology, but as a way of
processing data by allowing partial set membership rather than crisp set membership or non
membership. This approach to set theory was not applied to control systems until the 70's due to
insufficient small-computer capability prior to that time. Professor Zadeh reasoned that people do
not require precise, numerical information input, and yet they are capable of highly adaptive control.
If feedback controllers could be programmed to accept noisy, imprecise input, they would be much
more effective and perhaps easier to implement [9]. Likewise, neural networks are also capable of
representing the precise information from existing data sets. These intelligent control techniques
like neural networks, fuzzy logic and genetic algorithms have been used in liquid level control for
the last two decades.
In 1997, Park and Seong [10] investigated self-organizing fuzzy logic controller for water level
control of steam generators. Wu et al. [11] built a prototype of water level control system
implementing both fuzzy logic and neural network control algorithm and embedded the control
algorithms into a standalone DSP-based micro controller and compared their performances. Sugeno
model was used for fuzzy logic control system and Model Reference Adaptive neural Network
Control based on back propagation algorithm was applied in neural network. Galzina et al. [12]
presented applied fuzzy logic for water level control in boiler drum and combustion quality control.
Fuzzy control rules were extracted from operator knowledge based on relative ruling criteria for
existing boiler room. Taoyan et al. [13] proposed a novel interval type-2 fuzzy control system by
extending the membership functions to interval type-2 membership function without increasing the
design complexity. The control system can efficiently reduce the uncertain disturbances from real
environment. Recently, Shome and Ashok [14] described an intelligent controller using fuzzy logic
to meet the nonlinearity of the system for accurate control of the boiler steam temperature and water level.