The energy intensive distillation process has become a widely discussed topic as industry attempts to minimise energy consumption. The implementation of Model Predictive Control (MPC) can aid in the reduction of plant energy consumption. However, the leading chemical and petroleum software packages Aspen Plus and Aspen HYSYS do not currently support MPC. This project successfully integrated both MATLAB and LabVIEW with Aspen Plus Dynamics (APD), which enables the implementation of MPC schemes. This integration was established using Microsoft’s ActiveX Technology. In order to implement MPC from within MATLAB and LabVIEW, their respective MPC toolboxes were explored; these toolboxes possess several major flaws in their functionality. In particular, neither have the ability to perform RGA analysis or determine the model of the plant through data-driven modelling. To overcome these drawbacks a MATLAB script was developed which determines the model of the plant from automatic step tests in Simulink. Once the communication was established, and toolboxes documented, a high-fidelity distillation column was constructed in Aspen Plus before being exported to APD. This plant model was developed as a reference to compare the effectiveness of the PI and MPC control schemes, employing the Integral of Time-Weighted Absolute Error (ITAE) performance criterion. MPC outperformed the PI control schemes in all but one scenario. On average the ITAE values were 1000% lower for MPC, due to its ability to quickly track the set point and avoid overshoot. Further research has been highlighted on a number of toolbox features and dynamic communication options. Importantly, the use of the integrated software packages can provide a number of benefits for students and personnel. By developing a dynamic template it will be possible to implement these ideas into university, laboratory and workplace training. This could increase confidence in predictive control schemes, operator plant knowledge and reduce unsafe plant operation
