In the work, averaging level control using model-based control and estimation algorithm on a buffer tank system
is studied. Implementation of Model Predictive Control (MPC) and Proportional-Integral (PI) control together
with Kalman filter for state and disturbance estimation show decent benefits and potentials. Results show that
acceptable setpoint tracking of water level in the basin under varying inflow can be achieved. MPC precedes PI
for smoother pump actions. Python as a popular programming language is adopted and showed potential
for real-time control (RTC).