Optimizing reboiler duty and reflux ratio profiles of vapor recompressed batch distillation

Batch distillation columns are more energy inefficient than their continuous analogous. These batch columns commonly operate at a fixed reboiler duty and even reflux ratio. To improve their energetic potential, an attempt is made to optimize the reboiler duty, and reflux ratio profiles of the conventional batch scheme and its vapor recompressed counterpart. For this, an advanced version of the genetic algorithm is employed under the multi objective optimization (MOO) framework. This stochastic optimization method (i.e., elitist non-dominated sorting genetic algorithm) is utilized to find a cluster of optimum points formally known as the Pareto-optimal front. Then, one optimal point is selected using the technique for order of preference by similarity to ideal solution method with entropy information for weighting of objective functions, for selecting one of the Pareto-optimal solutions. Using these procedures, we first optimize the conventional column considering reboiler heat duty and reflux ratio profiles as decision variables along with others. Then, it is retrofitted by employing a vapor recompression based heat pump. Further, the MOO framework is used to design vapor recompressed batch distillation configuration in the new plant scenario. All these schemes are finally evaluated in terms of energy and cost savings, and total annual production (TAP). The TAP is increased reasonably, and the raw material requirement is reduced significantly for the conventional column operated at variable reboiler duty as compared column operated at constant reboiler heat duty mode. With the adaptation of vapor recompression in the conventional column, a significant reduction in energy is achieved without compromising TAP as compared to the optimal conventional column operated at dynamic reboiler heat duty and reflux mode.