Non-linear Adaptive Artificial Neural Networs Control of Municipal Wastewater Treatment Plants
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Date
2019-06
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
Wastewater
is used
water
from
any
combination of
domestic,
industrial,
commercial
or agricultural
activities, surface runoff or stormwater, and any sewer inflow or sewer infiltration.
The characteristics of
wastewater
vary
depending on
the
source.
Types of
wastewater
include
domestic
wastewater
from
households,
municipal wastewater from communities (also called sewage) or industrial wastewater from
industrial
activities.Wastewater
treatment
is the
process of
treating
contaminants
prior
to releasing
wastewater into the environment or reusing. Basically, there are four steps to remove contaminants in
sewage
wastewater
which
are;
pretreatment,
primary
treatment,
secondary
treatment
and
tertiary
treatment.The activated sludge process is a biological process and an essential secondary treatment in wastewater treatment , where bacteria plays a role of degrading organic substances based on the the crucial process control parameter, dissolved oxygen (DO) concentration. The DO concentration in the aeration tank(s) is maintained at the desired level by manipulation of airflow rate, applying a Neural network based
adaptive Proportional-Integral-Derivative (PID) controller.
In this thesis work, an Adaptive Neural Network Radial Basis Function PID (ANNRBFPID) control strategy is implemented to control a DO concentration in aerated bioreactors which update the set point of DO adaptively and withstand uncertain disturbances. Two models are selected to represent an activated sludge
process. The first one is the simplified model with only four state variables. The second model is the
Activated
Sludge
Model
no.1(ASM1)
the
more
realistic
and
accepted
model
with
13 state
variables.
Matlab/Simulink and SIMBA# software used for simulating the designed mathematical model and control of the activated sludge process for the simplified model and ASM1 respectively. The powerful learning and adaptive ability of the RBF neural network make the adaptive adjustment of the PID parameters to be realized. Hence, when the wastewater quality and quantity fluctuate, adjustments to some parameters online
can be made by ANNRBFPID algorithm to improve the performance of the controller.
The Matlab/Simulink simulation result show that the DO can be maintained at 2mg/L or any desired setpoint
with the presence of uncertain disturbances and continuously variable influents with ANNRBFPID control
algorithm and the simulation result
shows that ANNRBFPID achieve better control performance than
conventional PID. On the other hand, SIMBA# simulation results show that the international standard limit for Ntot (Total Nitrogen), CODtot (Total Chemical Oxygen Demand), SNH (NH4(+) and NH3 nitrogen),
TSS(Total Suspended Solids) is given by
< 18g, < 100g, < 4g, < 30g respectively and the simulation result
obtained is 11.04 g N/m³, 23.82 g COD/m³,
0.5421 g N/m³, 5.061 g/m³ respectively.
Description
Keywords
dissolved oxygen concentration, adaptive PID, radial basis function(RBF), SIMBA#