It has been proved that thermally coupled configurations can lower the energy consumptions around 30% in comparison to conventional distillation schemes for the separation of ternary mixtures. These potential energy savings have motivated research efforts on thermally coupled configurations for four or more component mixtures. Besides the significant energy savings that they have in contrast to...

Here MI is the total holdup inside the column, and MD and MB are the condenser and reboiler holdups. S = yD(1 xB) (1 yD)xB is the separation factor and Is = DyD(1 yD) + BxB(1 xB) is the \impurity sum". Note that Is may be extremely small for columns with both products of high purity (1 yD and xB both small) resulting in very large linearized values of 1. The response to changes in the internal ...

Abstract In this work we present a methodology for synthesizing sequences of columns that combine both, the heat integrated and the thermally coupled distillation columns. As a consequence it is possible to design systems capturing simultaneously the advantages of these two approaches. A superstructure based on separation tasks rather in actual columns is proposed. This approach is more flexibl...

Distillation is the most widely used but the most energy-consuming separation process in the chemical industry. The internally Heat-Integrated Distillation Columns (HIDiC) are one of the promising alternatives of the conventional distillation processes to reduce the energy consumptions in chemical processes. The HIDiC has a similar structure to a heat exchanger. The rectifying section is contac...

M and control of distillation columns is a research area that evokes considerable interest. On the one hand, substantial savings are possible with good controllers, on the other hand, distillation columns are often used as test cases for new methods of model based controller design. Strongly nonlinear behavior and high order are the characteristics that present challenges when designing control...

The process for transferring knowledge of multiple reinforcement learning policies into a single multi-task policy via distillation technique is known as policy distillation. When policy distillation is under a deep reinforcement learning setting, due to the giant parameter size and the huge state space for each task domain, it requires extensive computational efforts to train the multi-task po...