Data-driven surrogate optimized and intensified extractive distillation process for clean separation of isopropanol from water: A sustainable alternative

Abstract

Isopropanol (IPA) is an important chemical because of its wide use in chemical and related process industries. In the production of IPA, an important processing step is its recovery and purification. The objective of this paper is to analyze different processes to find an energy efficient and environmentally friendly alternative for clean separation of isopropanol from water. A six-step design methodology is employed, which starts with the conventional extractive distillation process (EDP). Design parameters of EDP are first optimized by using a proposed artificial neural network (ANN) based data-driven surrogate (ANNDS). Using the optimal EDP as a reference design, improvements (intensification and/or integration) are considered through the so-called distillation-membrane hybrid process (DMHP), by heat pump vapor recompression (HP-EDP-I and HP-EDP-II), and dividing wall column without (E-DWC) and with (HP-E-DWC) heat pump, respectively. The results show that DMHP can be considered as a retrofit option with an energy reduction of 2.45% for the whole EDP. As compared to EDP, energy consumed in HP-EDP-I, HP-EDP-II, E-DWC, and HP-E-DWC can be reduced by 37.7%, 44.1%, 40.0%, and 56.4%, respectively. Moreover, CO2 emission is reduced by 42.6%, 50.4%, 40.0%, and 59.0%, respectively. Therefore, HP-E-DWC process is found to be the most sustainable alternative for the clean separation of isopropanol from water among the proposed processes. With further consideration of economic performance, HP-E-DWC is found to have the best performance with high separation capacity (>37500 kg/h), while E-DWC is found to be a favorable choice for a small plant with low capital cost and intermediate environmental impacts.