In previous work, several significant improvements in the measurement of distillation curves for complex fluids were introduced. The modifications to the classical measurement provide for (1) temperature and volume measurement(s) of low uncertainty, and most important, (2) a composition-explicit data channel in addition to the usual temperature-volume relationship. This latter modification is achieved with a new sampling approach that allows precise qualitative as well as quantitative analyses of each fraction, on the fly. In the new approach, the distillation temperature is measured in two locations. The temperature is measured in the usual location, at the bottom of the take-off in the distillation head, but it is also measured directly in the fluid. The measurement in the fluid is a valid state point that can be theoretically explained and modeled. The usual measurement in the head provides a temperature that is not a valid state point for a variety of reasons. Since there is over a century of distillation data based on a head temperature measurement, it is necessary to relate measurements made with the new instrument with those made classically. For this reason, we have further modified our developmental instrument to incorporate a model predictive temperature controller. In response to either an equation-of-state calculation or a previous distillation curve, the programmable temperature controller increases the fluid temperature to achieve a constant mass flow rate of vapor through the distillation head. This approach eliminates the aberrations that one typically encounters due to fluctuations in distillation rate, often referred to as hesitation. Thus, we can collect data from two temperature channels: one a true state point measurement (measured directly in the fluid) and the other, a temperature channel that is comparable to previous data (measured in the head).
Citation: International Journal of Thermophysics
Pub Type: Journals
boiling curve, complex fluids, distillation curve, hesitation, hydrocarbons, model predictive temperature controller