No abstract

An energy-efficient two-column process based on a conventional extractive column process is explored by combining a preconcentration column and entrainer recovery column, which provides potential energy savings and capital investment reduction. Two case studies are demonstrated to verify the above-mentioned energy and economic advantages: the separation of isopropyl alcohol–water using DMSO as the entrainer and the separation of acetonitrile–water using ethylene glycol as the entrainer. In both cases, based on the global economic optimization, a design with optimized operation conditions for the conventional and improved processes is developed. It is revealed that the new process offers energy savings of 13.7% and 16.3%, respectively, and a similar reduction in total annual costs as compared to those of the conventional process.

No abstract

No abstract

In the production of isopropyl alcohol, diisopropyl ether appears as a byproduct. Diisopropyl ether and isopropyl alcohol form a binary minimum-boiling homogeneous azeotrope. In this paper, two methods are explored to achieve the separation of this binary azeotrope: fully heat-integrated pressure-swing distillation and extractive distillation with 2methoxyethanol as an entrainer. The optimal design and dynamic control of the two processes are investigated, respectively. It is revealed that the fully heat-integrated pressure-swing distillation system offers 5.75% reduction in the total annual cost and 7.97% saving in energy consumption as compared to the extractive distillation system. Moreover, control configurations of both processes performed nicely in maintaining the two products' quality when feed flow rate and composition disturbances are introduced. From the standpoint of dynamic control stability, both processes can be used for DIPE/IPA separation.

No abstract

Abstract. Biomedical research results are being published at a high rate, and with existing search engines, the vast amount of published work is usually easily accessible. However, reproducing published results, either experimental data or observations is often not viable. In this work, we propose a framework to overcome some of the issues of reproducing previous research, and to ensure re-usability of published information. We present here a framework that utilizes the results from state-of-theart biomedical literature mining, biological system modeling and analysis techniques, and provides means to scientists to assemble and reason about information from voluminous, fragmented and sometimes inconsistent literature. The overall process of automated reading, assembly and reasoning can speed up discoveries from the order of decades to the order of hours or days. Our framework described here allows for rapidly conducting thousands of in silico experiments that are designed as part of this process.

No abstract