(343b) Scale-up and Scale-Down of Heat Flow Data

Scale-up of heat flow data (Qr) is an important part of assessing process safety. A simple approach is to use lab heat flow data to estimate the adiabatic temperature and pressure rise that could occur in the plant and determine if this could cause secondary reactions or excessive pressure. A more advanced approach is to use modeling to develop a thermokinetic model which allows better prediction of plant temperatures and pressures under a variety of potential scenarios. In addition, a robust model provides process insight and a valuable tool for process development. Examples of using Dynochem to model thermokinetics are presented.

Thermokinetic modeling can be difficult in many cases (e.g., if multiple reactions are involved). Fortunately, it is possible to scale and stretch Qr data without obtaining kinetics and heats of reaction as long as basic rules are followed. Some of the potential pitfalls of scaling and stretching are discussed.