Experimental methods in chemical engineering: Residence time distribution—RTD

Abstract

Reactor performance, solids‐(gas)‐mixing, flow through porous media, distillation columns, or through granulators improve as the fluid dynamics approach ideal plug flow. The residence time distribution (RTD) is a diagnostic measure of how close fluid flow approaches ideal conditions. The technique introduces a step change to the inlet concentration—a Dirac‐δ function, Heaviside step function, or a rectangular pulse (bolus)—while high frequency detectors monitor the concentration along the vessel and/or at the exit. The effluent concentration profile spreads due to the variance in the process lines leading to the vessel and at the exit, the detector response, and the system. We quantify how much each of these contributes to the overall variance in a fluidized bed with 9 g of fluid cracking catalyst in 8 mm diameter quartz tubes. The injection variance is lowest for a GC sample loop configuration, compared to a 3‐way valve or 4‐way valve geometry. RTD measurements detect bypassing due to dead zones in vessels and the axial‐dispersion model and continuous stirred‐tank model to characterize deviation from plug flow. However, when the contribution to variance from the ancillary lines and detector is large compared to the system, the uncertainty in the model parameters is high. Research on RTD fundamentals concentrate on boundary conditions while, here, we focus on experimental errors: mechanical, physicochemical mathematical, and instrumental.

Residence time distribution (RTD) revisited

“…Various types of tracer detection can be used in laboratory and at industrial scale tests such as UV-Vis, conductivity, online Near Infrared (NIR) spectroscopy, radioactive tracers, etc. Bérard et al ( Bérard et al, 2020 ) presented RTD examples ranging from applications in static mixers, tubular loop reactor, structured packings for catalytic distillation, continuous powder blending using some of the above mentioned techniques for tracer detection. A recent review of the use of radiotracers in chemical industry was published by Sheoran et al ( Sheoran et al, 2018 ) emphasizing the need for radioactive decay correction and tail correction and listing applications of DTR to Fluid Catalytic Cracking (FCC), fluidized bed gasifier, liquid-liquid sieve plate extractor, aniline production, phosphoric acid production, etc.…”

Issue Highlights

“…The inlet valve configuration, sample frequency, tracer type, and ancillary lines contribute to experimental variance. [2] Deep Neural Network Based Recursive Feature Learning for Nonlinear Dynamic Process Monitoring Jiazhen Zhu, Hongbo Shi, Bing Song, Shuai Tan, Yang Tao…”

“…The inlet valve configuration, sample frequency, tracer type, and ancillary lines contribute to experimental variance. [2] The recently proposed stacked denoising auto-encoder (SDAE) has been applied in process monitoring frequently for extracting the robust and nonlinear latent variables from data against noise. However, SDAE leaves the dynamic relationship unconsidered.…”

“…The axial‐dispersion and continuous stirred tank models characterize the deviation from ideal behaviour (plug flow), but are inappropriate for gross flow anomalies. The inlet valve configuration, sample frequency, tracer type, and ancillary lines contribute to experimental variance …”

RTD Modeling of a Non-Ideal Coiled-Tube Reactor Through Experimental Investigation for Pulse Input Using Methylene Blue Dye

“…For an ideal reactor, the flow and mixing conditions are needed to be exactly known, which allows us to develop the theoretically mathematical model equation (1). However, in reality, for built reactors, these complex characteristics much deviate to some extent from ideal behavior due to various possible reasons such as a consequence of a short-circuiting, absence of turbulence, macroscopic internal currents, and stagnant zones (2). In an actual system, the velocity distribution profile leads to a residence time distribution (RTD).…”