Process control refers to the methods that are used to control and manipulate process variables in manufacturing a product. Main aim of this work is to design and simulate the working of a single input-single output (SISO) tank system using model predictive control (MPC) and conventional control and comparing the performances of both systems. The controlled variable is the liquid level in the tank and manipulated variable is the inlet flow rate of the liquid. Control systems based on a servo and regulatory control schemes are designed and simulated in Scilab. Tuning methods like Ziegler-Nichols, Coon-Cohen, and Tyreus-Luyben are used for the design of conventional controllers (P, PI, and PID). MPC system is designed using Differential Evolution heuristic. From the results obtained, it is revealed that the model predictive control scheme developed was able to control the liquid level in the tank with no offset and a settling time which was considerably lower than those offered by the conventional control schemes. The validated MPC system provides zero offset, better settling times, self-learning control action and incorporation of non-linear models with ease. This model can serve as a base for future improvement studies on the said model predictive control scheme.