Dynamic method, input-response technique

It is well known that the measurement of residence time distribution usually employs the dynamic method [54], the so-called input-response technique. However, for measuring RTD of solid particles the input signal is a difficult and troublesome problem. The author of the present book employs an arbitrary known function as the input signal so that this problem is solved. This procedure is also applicable, in principle, to the measurements of RTD of solid materials in other devices. 

The time that a molecule spends in a reactive system will affect its probability of reacting and the measurement, interpretation, and modeling of residence time distributions are important aspects of chemical reaction engineering. Part of the inspiration for residence time theory came from the black box analysis techniques used by electrical engineers to study circuits. These are stimulus-response or input-output methods where a system is disturbed and its response to the disturbance is measured. The measured response, when properly interpreted, is used to predict the response of the system to other inputs. For residence time measurements, an inert tracer is injected at the inlet to the reactor, and the tracer concentration is measured at the outlet. The injection is carried out in a standardized way to allow easy interpretation of the results, which can then be used to make predictions. Predictions include the dynamic response of the system to arbitrary tracer inputs. More important, however, are the predictions of the steady-state yield of reactions in continuous-flow systems. All this can be done without opening the black box. 

In a chromatographic experiment a small packed adsorption column is subjected to a perturbation in the inlet concentration of an adsorbable species and the dynamic response at the column outlet is measured. Such measurements provide, in principle, a simple and rapid means of studying adsorption kinetics and equilibria. This method has been widely applied to gaseous sorbates but similar techniques are in principle applicable with liquids. In practice it is usual to employ either a pulse or a step input although other types of perturbation may also be used. The choice between step or pulse depends entirely on practical convenience since exactly the same information may be obtained from either experiment. 

Stochastic identification techniques, in principle, provide a more reliable method of determining the process transfer function. Most workers have used the Box and Jenkins [59] time-series analysis techniques to develop dynamic models. An introduction to these methods is given by Davies [60]. In stochastic identification, a low amplitude sequence (usually a pseudorandom binary sequence, PRBS) is used to perturb the setting of the manipulated variable. The sequence generally has an implementation period smaller than the process response time. By evaiuating the auto- and cross-correlations of the input series and the corresponding output data, a quantitative model can be constructed. The parameters of the model can be determined by using a least squares analysis on the input and output sequences. Because this identification technique can handle many more parameters than simple first-order plus dead-time models, the process and its related noise can be modeled more accurately. 

---