Browsing by Author "Solomon, Baye"

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    Non-linear Adaptive Artificial Neural Networs Control of Municipal Wastewater Treatment Plants
    (Addis Ababa University, 2019-06) Solomon, Baye; Dereje, Shiferaw (PhD)
    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.