Thermodynamic Insight for the Design and Optimization of Extractive Distillation of 1.0-1a Class Separation

We study the continuous extractive distillation of minimum boiling azeotropic mixtures with a heavy entrainer (class 1.0-1a) for the acetone-methanol with water and DIPE-IPA with 2-methoxyethanol systems. The process includes both the extractive and the regeneration columns in open loop flowsheet and closed loop flowsheet where the solvent is recycled to the first column. The first optimization strategy minimizes OF and seeks suitable values of the entrainer flowrate FE, entrainer and azeotrope feed locations NFE, NFAB, NFReg, reflux ratios R1, R2 and both distillates D1, D2. OF describes the energy demand at the reboiler and condenser in both columns per product flow rate. It accounts for the price differences in heating and cooling energy and in product sales. The second strategy relies upon the use of a multi-objective genetic algorithm that minimizes OF, total annualized cost (TAC) and maximizes two novel extractive thermodynamic efficiency indicators: total Eext and per tray eext. They describe the ability of the extractive section to discriminate the product between the top and to bottom of the extractive section. Thermodynamic insight from the analysis of the ternary RCM and isovolatility curves shows the benefit of lowering the operating pressure of the extractive column for 1.0-1a class separations. A lower pressure reduces the minimal amount of entrainer and increases the relative volatility of original azeotropic mixture for the composition in the distillation region where the extractive column operates, leading to the decrease of the minimal reflux ratio and energy consumption. The first optimization strategy is conducted in four steps under distillation purity specifications: Aspen Plus or Prosim Plus simulator built-in SQP method is used for the optimization of the continuous variables: R1, R2 and FE by minimizing OF in open loop flowsheet (step 1). Then, a sensitivity analysis is performed to find optimal values of D1, D2 (step 2) and NFE, NFAB, NFReg (step 3), while step 1 is done for each set of discrete variables. Finally the design is simulated in closed loop flowsheet, and we calculate TAC and Eext and eext (step 4). We also derive from mass balance the non-linear relationships between the two distillates and how they relate product purities and recoveries. The results show that double digit savings can be achieved over designs published in the literature thanks to the improving of Eext and eext. Then, we study the influence of the Eext and eext on the optimal solution, and we run the second multiobjective optimization strategy. The genetic algorithm is usually not sensitive to initialization. It allows finding optimal total tray numbers N1, N2 values and is directly used with the closed loop flow sheet. Within Pareto front, the effects of main variables FE/F and R1 on TAC and Eext are shown. There is a maximum Eext (resp. minimum R1) for a given R1 (resp. Eext). There exists an optimal efficiency indicator Eext,opt which corresponds to the optimal design with the lowest TAC. Eext,opt can be used as a complementary criterion for the evaluation of different designs. Through the analysis of extractive profile map, we explain why Eext increases following the decrease of FE and the increase of R1 and we relate them to the tray numbers. With the sake of further savings of TAC and increase of the environmental performance, double-effect heat integration (TEHI) and mechanical heat pump (MHP) techniques are studied. In TEHI, we propose a novel optimal partial HI process aiming at the most energy saving. In MHP, we propose the partial VRC and partial BF heat pump processes for which the coefficients of performance increase by 60% and 40%. Overall, optimal partial HI process is preferred from the economical view while full VRC is the choice from the environmental perspective.