Development of chemical processes using micro-scale plants

Abstract

This work evaluated the possibility of using micro-scale plants for process development. The main tasks in this study were measurement of physical properties, development of a 3 dimensional (3D) printed distillation column and development of a process for production of 2 methoxy-2,4,4-trimethylpentane (TOME) using a micro-scale plant. The measured physical properties were focusing on the chemicals present in the process for production of TOME.

The measured physical properties include density for a pure compound, excess enthalpy and liquid-liquid equilibrium for binary and ternary mixtures. The common activity coefficient models such as Non random two-liquid (NRTL) or UNIversal QUAsiChemical (UNIQUAC) were used for modeling of the phase equilibria measurement.

The 3D printing, as a new manufacturing technology, was investigated to discover the capabilities of this technique in production of complicated chemical apparatuses. In this work, a modular distillation column and its packing was designed, printed with a 3D printer, and evaluated in the laboratory. The 3D printed column was tested using different type of packings and with different reflux ratios. The modularity of the design and considerably rapid manufacturing made it possible to improve the design for several times and develop a practical distillation column that could be used for micro-distillation.

A micro-scale plant was used to study the possibility, strengths and weaknesses of process development in micro-plants. The case study was production of TOME using an etherification process. This process included a reactor and a distillation column. In this work, several steps of process design, such as batch production of the chemical using a glass apparatus, simulation - optimization of the process and micro-scale piloting of the process were done. The results of the measurements gave valuable information such as the compositions, flowrates and operational temperatures which were corresponding well with the modeling.