Nowadays, the appropriate monitoring and control of industrial distillation columns play a crucial role in achieving the desired production. This thesis focuses on the design of the advanced control of Armfield's UOP3CC laboratory distillation column. Concentration is one of the difficult-to-measure variables in the industry, which is both time-consuming and costly to measure on a regular basis. As a solution to this issue, inferential sensors are used to indicate this difficult-to-measure quantity and then to use it in an advanced control structure. Inferential sensors enable fast and accurate measurement of key variables. Different approaches will be used in their design, including the data-driven approach, the model-based approach, and a combination of these, the hybrid approach. These sensors are capable to calculate difficult-to-measure or completely unmeasurable values that cannot be obtained directly from the sensors present on the laboratory distillation column. They are calculated based on the outputs from the physical sensors. The sensor inference methods studied in this work include ordinary least squares regression and the least absolute shrinkage and selection operator. Additionally, a custom procedure was developed based on data processing to identify one of the key properties of the process, steady state. Achievement of this state is essential for the identification of the process and for the subsequent design of the control system. To obtain the desired product concentrations, the appropriate variables must be selected, which can control the system correctly. This can be accomplished by using the variable pairing method. This approach determines which quantity will be controlled and which quantity should be manipulated to accomplish the goal. For a better understanding of the system, classical proportional integral derivative controllers are first proposed, which are designed by the trial-and-error method. Then a linear quadratic output feedback regulator is proposed for advanced control of the system. The goal is to minimize the optimisation function to ensure that the selected variable is controlled to the desired value. This work has several outputs, which also include a human-machine interface scheme in MATLAB Simulink or detailed documentation of how to proceed with the experiment on laboratory distillation column UOP3CC.