Industrial sodium chlorate production is a highly energy-intensive
electro-chemical process. If the pH of the chlorate cell is not controlled,
the current efficiency drops from 99% to as low as 66.66%. Hence control of
chlorate cell pH is very significant for energy-efficient sodium chlorate
production. This study puts forward a fractional order PID controller for
controlling the pH of the sodium chlorate cell. The tuning of FOPID
controller variables is affected by employing particle swarm optimization.
The highlight of the controller is that it is flexible, easy to deploy and
the time of computation is significantly low as few parameters are needed to
be adjusted in PSO. The performance analysis of the suggested FOPID-PSO
controller was studied and compared with the traditional PI controller and
PID controller using time-domain provisions like settling time, rise time
and peak overshoot and error indicators like integral square error (ISE),
integral absolute error (IAE), and integral time absolute error (ITAE).
FOPID controller employing PSO proved to perform well compared to
conventional controllers with 0.5 sec settling time and 0.1 sec rise time.
This demonstrates that the FOPID-PSO controller has better setpoint
tracking, which is very essential for the process under consideration.
Cumene is used as precursor for production of many organic chemicals and as
thinner in paints & lacquers. Its production process involves one of the
large-scale manufacturing processes with complex kinetics. Different
classical control strategies have been implemented and compared for the
cumene reactor in this process. As a system with large degrees of freedom a
novel approach for extracting the state space model from the COMSOL
Multiphysics implementation of the system is adopted here. Internal Modern
Control (IMC) based PI and PID controllers are derived for the system. To
derive the controller setting the system is reduced to the FOPDT and SOPDT
model structure using Skogestad half rules. The integral time is modified to
obtain the excellent set point tracking and faster disturbance rejection.
From the analysis it can be stated that PI controller suits more for this
specific process. Particle Swarm Optimization (PSO) algorithm, an
evolutionary computation technique is also used to tune the PI settings. The
PI controllers with IMC, Zeigler Nichols and PSO tuning are compared and it
can be concluded that PSO PI controller settles at 45s without any
oscillations and settles down faster for the disturbance of magnitude 0.5
applied at t=800s, through it is computationally intensive compared to other
controller strategies.
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