Optimal Controller for Steam Boiler Drum Water Level Control Using Neural Network Estimator and LQR
No Thumbnail Available
Date
2018-10
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
The water level of boiler drum is one of the crucial control parameters for any process
industries, which reflects the control of mainly boiler load and feed water indirectly. The
increasing demand for rapid changes in industries leads to more uncompromising
requirements on the control systems for the processes. A very common control problem
elsewhere in steam generation is that of controlling the water level in a boiler drum.
Effectively controlling the drum water level in an industrial boiler helps to maximize
overall energy efficiency. The problem behind various approaches used to control the drum
water level is lack of practical aspects such as online tuning and ease of implementation
within a real plant distributed control system and also bad parameter tuning leading to poor
level performance. An optimization technique can solve those problems stated by
simplifying the design process and allowing easily usage with physical system while
satisfying the optimality condition such as speed and accuracy of the response should be
within specified limits.
In this thesis an optimal controller using a multivariable feedback technique using a neural
network state estimator based linear-quadratic regulator control is introduced. The purpose
of NN based LQR is to keep the level of the steam boiler drum water at the specified zero
reference value which avoids damage of boiler occurs by either overflow of water on the
top of drum which affects steam quality or by shortage of water in the drum causes the
drum burnt. Controller is designed and simulated on MATLAB/SIMULINK environment
and the designed optimal controller is compared with conventional PID controller.
Simulation result indicates that PID controller reaches its steady state value of pressure of
5.45 bar and water level oscillates about the set with slightly increasing amplitude. The NN
based LQR controller achieves steady state pressure and level value of 5.455 bar and
0.00322 mm respectively. Comparing the two controllers, the proposed neural network
based linear quadratic regulator achieved good performance in both steady state response
tracking and speed of response.
Description
Keywords
Drum level control, optimal control, linear quadratic regulator, neural network estimator