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Impact of Flow Configurations on Response Time and Data Quality in Real-Time, In-Line Fourier Transform Infrared (FTIR) Monitoring of Viscous Flows
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Impact of Flow Configurations on Response Time and Data Quality in Real-Time, In-Line Fourier Transform Infrared (FTIR) Monitoring of Viscous Flows
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  • Nasser Al Azri
    Nasser Al Azri
    Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, Pennsylvania 15261, United States
  • Corey Clifford
    Corey Clifford
    Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, Pennsylvania 15261, United States
  • Robert M. Enick
    Robert M. Enick
    Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, Pennsylvania 15261, United States
  • Götz Veser*
    Götz Veser
    Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, Pennsylvania 15261, United States
    *Email: [email protected]
    More by Götz Veser
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Organic Process Research & Development

Cite this: Org. Process Res. Dev. 2024, 28, 5, 1657–1667
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https://doi.org/10.1021/acs.oprd.3c00299
Published November 3, 2023

Copyright © 2023 The Authors. Published by American Chemical Society. This publication is licensed under

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Abstract

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The real-time, in-line monitoring of continuous flow concentrations is widely conducted via infrared (IR) spectrometry by using a flow cell connected to a reactive flow stream. For protective purposes, the IR sensor tip is typically offset from the flow. This offset can cause the formation of a stagnant boundary layer above the sensor, especially when dealing with high-viscosity fluids. As a result, the IR signal response time is often controlled by the slow diffusional exchange of fluid in the boundary layer, as confirmed via 2D computational fluid dynamics (CFD) simulations. We evaluated several flow configuration modifications in a typical IR flow cell in order to identify the changes to the flow dynamics that enable improved response times with minimal changes to the cell configuration: the use of (i) vertical flow, where the standard horizontal flow over the sensor is redirected to contact vertically with the sensor, (ii) a static mixer to create radial flow momentum above the IR sensor, and (iii) horizontal or vertical nozzles to direct the flow toward the IR sensor. The vertical flow configuration did not show any significant improvement over the standard horizontal flow configuration. However, the static mixer, horizontal nozzle, and vertical nozzle configurations all resulted in markedly improved response times and signal quality, albeit at the expense of a higher pressure drop across the flow cell. These results point toward straightforward, user-accessible modifications of in-line IR flow cells that result in significant improvements in signal stability and acquisition times.

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Copyright © 2023 The Authors. Published by American Chemical Society

SPECIAL ISSUE

This article is part of the Flow Chemistry Enabling Efficient Synthesis 2024 special issue.

1. Introduction

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The real-time, in-line monitoring of reactant and/or product concentrations in a reactive continuous flow is widely used both in research and developmental laboratories, as well as in the chemical industry. (1−3) It is typically achieved via a flow cell or a probe sensor that is connected or inserted into the flow stream. This in-line monitoring enables one to reliably analyze complex reaction systems and gain a better understanding of and better control over chemical processes with minimal flow intervention and without the need for disruptive and invasive sampling and off-line analysis. (4−6) From a safety perspective, it allows one to detect and monitor hazardous chemicals with minimal contact (7) and accurately manage process safety under continuous flow conditions. (8,9) In addition, the in-line monitoring of the progress of a reaction is becoming even more important in the context of intensifying specialty and fine chemicals processing by transitioning from batch operations to continuous operations. (10−12) This batch-to-continuous transition requires a continuous in-line tracking of concentrations in order to perform rapid screening of reaction conditions, (13−16) identify key intermediate steps and reaction end points, (17,18) and develop robust and sensitive reaction kinetics and thermodynamic data (19−22) that can be used for process modeling, design, optimization, and scale-up of continuous flow processes.
The real-time, in-line monitoring of reactive continuous flows has been demonstrated using different spectroscopic tools such as Raman spectroscopy, (23−25) nuclear magnetic resonance (NMR) spectroscopy, (26,27) mass spectroscopy (MS), (28,29) ultraviolet–visible (UV–vis) spectroscopy, (30,31) high-performance liquid chromatography (HPLC), (32,33) near-infrared (NIR) spectroscopy, (34,35) and Fourier transform infrared (FTIR) spectroscopy. (36−38) Raman spectroscopy, NMR spectroscopy, and MS allow for the qualitative and quantitative study of the chemical composition (i.e., structure, concentration, product yield, and reaction rates) with a high time efficiency due to the high resolution and speed of the data acquisition processes. However, these spectroscopic tools remain comparatively difficult to use for in-line monitoring when studying complex reaction mixtures, as these techniques are often compromised by signal-overlapping. (39) UV–vis spectroscopy measures the ability of a chemical substance to absorb light in the ultraviolet and visible regions of the electromagnetic spectrum. Thus, it is limited to UV–vis-active compounds and lacks the ability to obtain any structural information. (39−41) FTIR spectroscopy enables the qualitative and quantitative study of the chemical composition by identifying and tracing functional groups in (typically organic) compounds over time based on their unique vibrational spectra in the IR range with high specificity and good temporal resolution. For this reason, FTIR is the most widely used in-line analytical tool in continuous flow chemistry when applied to specialty and fine chemicals, as these chemicals are usually identified by specific functional groups. (42,43)
In order to monitor the reactive flow in a continuous flow reactor, the IR probe sensor is typically contained in a flow cell connected in-line or at the exit of the reactor. (44,45) Within this cell, the IR sensor tip is typically offset from the continuous horizontal flow for protective purposes. Here, we focus on the Mettler Toledo ReactIR flow cell, a widely used commercial IR instrument with a typical flow cell configuration, in which the IR sensor tip is offset by 1 mm from the flow path. While this configuration is well-suited for medium- or low-viscosity fluids, high-viscosity flows with viscosities of up to several thousand centipoises, which are often encountered in specialty and fine chemistry, result in very low Reynolds numbers (Re < 10) and, hence, a (near) stagnant boundary layer above the sensor that updates very slowly. This can result in strong delays in response times to changes in the flow composition caused by transient reaction conditions or by changes in the experimental conditions (such as flow rate or temperature), resulting in excessively long experimental run times, the consumption of large amounts of raw materials, and uncertainty and inaccuracy in the measurements. To address this issue, in this study we present a combined experimental–computational evaluation of several flow cell configurations with the goal of accelerating response times by using simple modifications of the flow cell that the user can implement to achieve significant improvements in response time and IR signal stability.

2. Experimental Section

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2.1. IR Instrument

The present study utilized a ReactIR 45m instrument (Mettler Toledo), which is a Fourier transform infrared (FTIR) spectrometer designed for the real-time, in-line monitoring of reaction components. The instrument was fitted with a 24 h mercury cadmium telluride (MCT) detector and coupled with a diamond-tipped attenuated total reflectance (ATR) sentinel sensor (DiComp) and a K4 mirror conduit. (45) The diamond sentinel sensor was located inside a flow cell with a cylindrical channel (internal diameter = 4.82 mm) that could be connected to a reaction flow stream for continuous in-line monitoring over a range of temperatures (193–473 K) and pressures (vacuum up to 103 bar) (Figure 1).

Figure 1

Figure 1. Standard horizontal flow configuration, where the IR sensor tip (red) that is located inside the flow cell (gray) is offset by 1 mm from the horizontal flow channel.

2.2. Investigated Flow Path Configurations

To identify the effects of flow dynamics on the temporal response of the infrared (IR) system, five different flow configurations in the flow cell were experimentally investigated, as shown in Figure 2: (A) the commercial standard horizontal flow (HF) configuration, where the flow enters from one side, passes through a cylindrical channel, and exits from the opposite side. (B) A static mixer (SM) configuration, where a helical static mixer (Stratos Tube Mixer, Koflo, Part No. 3/16-17) with an outer diameter (OD) of 4.82 mm, an internal diameter (ID) of 3.35 mm, a length (L) of 12.38 cm, and 17 helical elements was inserted into the flow cell channel to transfer a radial flow momentum and direct the stream toward the IR sensor window. (C) A horizontal nozzle (HN) configuration, where a 1 mm homemade nozzle (made from a Swagelok tube with an OD of 3.18 mm) was inserted horizontally into the flow channel to directly impinge the flow downward onto the IR sensor window. (D) A vertical nozzle (VN) configuration, in which a 1.75 mm homemade nozzle (made from a Swagelok tube with an OD of 4.76 mm) was inserted vertically into the flow channel to direct a vertically impinging flow toward the IR sensor window. Finally, (E) a vertical flow (VF) configuration, where the standard flow was simply redirected from a horizontal to a vertical contact with the IR sensor window (i.e., without the use of a nozzle).

Figure 2

Figure 2. The five flow configurations investigated in the present study: (A) standard horizontal flow (HF), (B) static mixer (SM), (C) horizontal nozzle (HN), (D) vertical nozzle (VN), and (E) vertical flow (VF).

2.3. Continuous Flow Setup

A simplified schematic of the continuous flow setup is shown in Figure 3. A positive displacement pump (Tuthill) was used to continuously feed the solvent or polymer–solvent mixture; a three-way valve was used for rapid switching between the two feeds. The volumetric flow rate of the feed was monitored via a flow meter, which was used to maintain the desired feed flow rate through the system. For all of the experiments reported in this study, the flow was laminar with a Reynolds number Re < 10. An in-line ReactIR 45m spectrometer was used to monitor the IR signal versus time. The volume between the three-way valve and the IR sensor was ∼42 cm3, resulting in an expected initial delay in the IR signal between 126 s for a flow rate of 20 cm3/s and 13 s for a flow rate of 200 cm3/s. A thermocouple probe and pressure transducers were used to continuously monitor the inlet fluid temperature and pressure drop across the flow cell, respectively, for the different flow configurations (monitored via LabVIEW).

Figure 3

Figure 3. Flow diagram of the continuous flow setup.

2.4. Experimental Flow Mixtures

To evaluate the impact of viscosity on the response time and data quality for the different flow configurations, seven different samples were prepared with different viscosities by varying the amount of solvent (mineral oil or petroleum naphtha) and polymer (polyisobutylene succinic anhydride (PIBSA) with a molecular weight of ∼1000, which is completely miscible with both solvents), as shown in Table 1. In this fashion, the viscosity of the flow (at 293 K) was adjusted from 7 to 4850 mPa s (i.e., by almost 3 orders of magnitude) without changing the chemical nature of the polymer–solvent mixture. Sample S1 (pure petroleum naphtha) was used to investigate the time it took to replace the S2 and S3 polymer–solvent mixtures (i.e., to remove them from the IR sensor tip), while sample S4 (pure mineral oil) was used to replace the high-viscosity polymer–solvent mixtures S5–S7. The mineral oil that was used was a hydrotreated heavy paraffinic petroleum distillate mixture of C20 to C31 saturated hydrocarbon isomers. The petroleum naphtha that was used was a hydrotreated light petroleum distillate mixture of C8 to C19 nonaromatics, monoaromatics, and diaromatics derived from the refining of crude oil. All chemicals (mineral oil, petroleum naphtha, and PIBSA) were provided by the Lubrizol Corporation and were used as obtained.
Table 1. Compositions and Physical Properties at Room Temperature (293 K) of the Experimental Mixtures Used in the Present Studya
 solvent wt %   
sample identifiermineral oilpetroleum naphthapolymer wt %viscosity at 293 K (mPa s)density at 293 K (kg/m3)
S1-100%0%7780
S2-80%20%9802
S3-60%40%28826
S4100%-0%42840
S580%-20%134852
S660%-40%769865
S740%-60%4850879
a

The mixtures cover a wide range of fluid viscosities and are labeled in order of viscosity, from the least viscous sample (S1) to most viscous sample (S7). All experiments were conducted at room temperature.

2.5. Infrared (IR) Signal Analysis

The IR signal response was monitored by evaluating the changes in the peak area versus time while switching from a pure solvent (S1 or S4) to a polymer–solvent mixture (S2, S3, S5, S6, or S7) and vice versa. The polymer–solvent mixtures had a clearly identifiable signal between 1831 and 1738 cm–1, corresponding to the carbonyl group (C═O) of polyisobutylene succinic anhydride (PIBSA), while the pure solvents had no signal in this range, allowing for robust tracing of the change in the flow composition (Figure 4). For quantification, the full peak area of the carbonyl group (C═O) was selected with a two-point baseline correction and traced every 5 s. The obtained peak area time trace was converted to a normalized anhydride concentration signal by using eq 1. A normalized signal of 0 indicates the pure solvent, while a normalized signal of 1 indicates the presence of the polymer–solvent mixture at the IR sensor. The reproducibility of the IR signal response was investigated (Figure S1); it was determined that the signals were closely reproducible with a maximum standard error of 0.14 for the horizontal nozzle, 0.13 for the vertical nozzle, and 0.09 for the static mixer.
normalized anhydride signal=C═O peak area(t)C═O peak area(min)C═O peak area(max)C═O peak area(min)
(1)

Figure 4

Figure 4. IR spectra of the solvent (dashed line) and polymer–solvent mixtures (solid lines). The polymer–solvent mixtures have a distinct peak between 1831 and 1738 cm–1, corresponding to the carbonyl group (C═O) of the polymer.

2.6. Computational Fluid Dynamics (CFD)

To gain further insight into the experimentally observed effects of the flow cell geometry on the temporal response of the IR sensor, two-dimensional (2D) computational fluid dynamics (CFD) simulations were performed (using Ansys Fluent, version 2021R2) (46) and validated against the corresponding experimental observations. Coupled mass, linear momentum, and species conservation relationships (eqs 24) were applied to the flow cell configurations shown in Figure 2 to assess the impacts of the resultant velocity profiles, boundary layers, and possible dead volume on the response time at the IR sensor. Assuming that the pure solvent and polymer–solvent mixture hydraulically behave as a single mixture, the conservation expressions take the following form:
ρt+·(ρv)=0
(2)
t(ρv)+·(ρv×v)=p+·[μ(v+(v)T)]+ρg
(3)
t(ρYi)+·(ρvYi)=·(ρDi,mYi)
(4)
where ρ is the fluid density, v⃗ is the velocity vector, p is the local pressure, μ is the dynamic viscosity, g⃗ is the gravitational acceleration field, Yi is the mass fraction of the ith species, and Di,m is the corresponding mass diffusivity of that species within the mixture. Each simulated flow cell was assumed to be isothermal (i.e., no temperature-induced variations in the thermophysical properties of each species) by using the densities and dynamic viscosities listed in Table 1. The local material properties were computed using the mass-weighted average (based on the mass fraction of the species) of the constitutive chemicals within each finite volume cell.
For each flow cell configuration, a structured orthogonal mesh was generated by dividing the 2D geometry into quadrilaterals (see Figure S2 for an example of the decomposition of the HF configuration) and specifying the number of divisions across each edge. To capture any dead volume around the IR sensor, an additional grading operation was specified to bias the local mesh size down to 0.1 μm at the surface of the sensor. It should be noted that, to ensure a sufficient spatial resolution in the CFD results, this grid spacing was significantly smaller than the penetration depth (i.e., sampling depth) of the IR beam ∼2 μm above the sensor tip. An example of this mesh grading on the HF flow cell is displayed in Figure S3. The grid independence in the obtained computational results was verified by successively refining the finite volume mesh until the solution remained invariant to increases in the cell density.
Within each simulation, a laminar flow model was used (as Re < 10 for all experiments) in conjunction with the following boundary conditions: (i) a fixed uniform inlet velocity, (ii) zero diffusion flux for all flow variables (i.e., outflow boundary condition) to model the flow at the cell exit, and (iii) no-slip conditions at the wall. The inlet velocity was calculated by using each of the experimental volumetric flow rates and the cross-sectional area of the inlet channel or nozzle of the flow cell. Second-order accurate numerical schemes were applied to the temporal, advection, and diffusion terms in eqs 24. All three conservation equations were iterated until the root-mean-square residuals fell below an absolute convergence criterion of 1 × 10–6. To initialize the transient simulations, a steady-state simulation was performed first and used as the initial condition for the respective transient runs. At each inlet flow rate, the applied Δt was successively decreased until a time-step invariant solution was obtained.

3. Results and Discussion

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3.1. Infrared (IR) Signal Response to Flow Changes Using Different Configurations

To evaluate how the IR signal responded to changes in the flow composition for the different flow configurations, experimental studies were performed by initially flowing pure solvent through the flow cell channel and then switching to a polymer–solvent mixture (i.e., switching from a less viscous to a more viscous flow). Figure 5 shows the normalized anhydride signal time traces when switching from the S4 solvent to the S5 polymer–solvent mixture for the five investigated flow path configurations: horizontal flow (black dotted line), horizontal nozzle (solid red line), static mixer (solid green line), vertical flow (magenta dotted line), and vertical nozzle (solid blue line) at flow rates of 20 cm3/min (Figure 5A), 100 cm3/min (Figure 5B), and 200 cm3/min (Figure 5C), respectively.

Figure 5

Figure 5. IR signal (shown here as the normalized anhydride signal) vs time when switching from the S4 solvent (viscosity = 42 mPa s) to the S5 polymer–solvent mixture (viscosity = 134 mPa s) at flow rates of (A) 20, (B) 100, and (C) 200 cm3/min (note the different time scales for the x-axis).

The time t = 0 min represents the time when the three-way valve was switched from S4 to S5; thus, the initial time delay in the IR signal is due to the flow volume between the three-way valve and the IR sensor, as described in section 2.3. The time delays in both the horizontal flow (HF) and vertical flow (VF) configurations extend significantly beyond the expected initial delay at each flow rate (Figure 5, black and magenta dotted lines, respectively). Additionally, the time to full “signal conversion” (i.e., the detection of the full polymer–solvent mixture signal at the IR sensor with a normalized anhydride signal of 1) for these two flow configurations is the longest out of all the configurations. In addition to this extended initial delay and long amount of time to achieve full signal conversion, the IR signal is unstable, and the time trace does not follow a smooth “S-shaped” transition, which is particularly pronounced at a lower flow rate.
Utilizing a static mixer (SM) to break up the boundary layer characteristic of the fully developed laminar flow and hence improve the radial mixing at the IR sensor resulted in a significantly improved response time, with a shortened initial delay time and a shorter time to achieve full signal conversion, as well as the stabilization of the acquired signal (Figure 5, green line). Impinging the flow directly onto the IR sensor tip using the two nozzle configurations (horizontal nozzle (HN) and vertical nozzle (VN)) resulted in an even further acceleration of the fluid exchange at the IR sensor and, hence, the shortest delay and response times, which were again most visible at lower flow rates (see the red and blue lines in Figure 5 for HN and VN, respectively).
Similar effects were observed when switching from more viscous to less viscous flows (see Figure 6 for the case when the flow switched from the S5 polymer–solvent mixture to the S4 solvent). For the horizontal and vertical flow configurations, the delay time in the IR signal drastically increased beyond the expected initial time delay, especially at the lowest flow rate when these configurations became essentially unusable, and the time to achieve full signal conversion (i.e., the detection of the pure solvent signal at the IR sensor with a normalized signal of 0) also further increased. Considering the switch from low- to high-viscosity flows discussed above, the IR signal also showed poor stability (indicating flow instabilities). These issues became even more pronounced when the change in viscosity was larger (e.g., switching between S4 and S6, which increased the viscosity of the flow by almost 20-fold; see Figures S4 and S5).

Figure 6

Figure 6. IR signal (shown here as the normalized anhydride signal) vs time when switching from the S5 polymer–solvent mixture (viscosity = 134 mPa s) to the S4 solvent (viscosity = 42 mPa s) at flow rates of (A) 20, (B) 100, and (C) 200 cm3/min (note the different time scales for the x-axis).

These results show that the standard horizontal flow and the vertical flow configurations are unsuitable for high-viscosity flows. We hypothesized that the offset of the IR sensor from the continuous flow path caused the liquid volume above the IR sensor to form a stagnant boundary layer, which limited the fluid exchange in the critical measurement area. To verify this hypothesis, we conducted detailed CFD simulations, as described in the following section.

3.2. 2D Computational Fluid Dynamics (CFD) Simulations

To gain deeper insights into the fluid flow dynamics, computational fluid dynamics (CFD) models of the steady-state velocity profiles in the flow cell channel were developed using the S5 polymer–solvent mixture for three of the flow configurations: the (i) horizontal flow configuration (Figure 7), (ii) vertical flow configuration (Figure 8), and (iii) horizontal nozzle configuration (Figure 9) with a flow rate of 20 cm3/min, which corresponds to inlet velocities of 0.0182 m/s for the horizontal and vertical flow configurations and 0.1382 m/s for the horizontal nozzle configuration. Similar steady-state velocity profiles were also developed for these flow configurations with a flow rate of 200 cm3/min (Figure S6).

Figure 7

Figure 7. Steady-state velocity profile developed using CFD for the horizontal flow configuration using the physical properties of the S5 polymer–solvent mixture and an inlet flow rate of 20 cm3/min.

Figure 8

Figure 8. Steady-state velocity profile developed using CFD for the vertical flow configuration using the physical properties of the S5 polymer–solvent mixture and an inlet flow rate of 20 cm3/min.

Figure 9

Figure 9. Steady-state velocity profile developed using CFD for the horizontal nozzle configuration using the physical properties of the S5 polymer–solvent mixture and an inlet flow rate of 20 cm3/min.

For the horizontal and vertical flow configurations, the steady-state models show a velocity of 0 (blue) and a thick, stagnant boundary layer in the fluid volume right above the IR sensor tip. These results indicate that the fluid exchange in the critical measurement area of the IR sensor (up to 2 μm above the sensor tip) is purely diffusion-driven, thus explaining the long initial time delay and the long time to full conversion that were observed experimentally (Figures 5 and 6). At higher flow rates (i.e., higher velocities), the fluid exchange becomes faster due to the higher shear rate, which shortens the time to full signal conversion. In contrast, the horizontal nozzle configuration forces the flow to impinge onto the IR sensor window, as indicated by the area with a non-zero velocity profile (green-yellow) in the fluid volume right above the IR sensor.
Next, transient CFD simulations were performed to investigate the time needed for a pure solvent to replace a polymer–solvent mixture in the IR sensor measurement area and to gain further insight into the impact of diffusive mass transfer on the concentrations in the boundary layer. A critical parameter that is required for the CFD species transport models is the mass diffusivity. To the best of our knowledge, there is no reported literature value of the mass diffusion coefficient Doil–polymer of mineral oil in polyisobutylene succinic anhydride. Therefore, the experimentally measured normalized time traces were used to fit the diffusion coefficient (Doil–polymer) based on the set of experiments where S6 was replaced with S4 at flow rates of 20, 100, and 200 cm3/min. A diffusion coefficient (Doil–polymer) between 1.0 × 10–14 and 5.0 × 10–14 m2/s resulted in a reasonable fit (Figure S7), and these values were used to compare the simulated time traces to the experimental data when replacing the S5 polymer–solvent mixture with the S4 solvent at flow rates of 20, 100, and 200 cm3/min (Figure 10). A reasonable agreement was observed, with the maximum time differences between the experimental data and the simulations not exceeding ∼10%, i.e., 3 min for the lowest flow rate (20 cm3/min) and 0.4 min for the highest flow rate (200 cm3/min). This confirms that the very slow diffusion-driven fluid exchange due to the offset of the IR window from the flow path renders the horizontal and vertical flow configurations as poorly suited for viscous flows because of the formation of a stagnant boundary layer.

Figure 10

Figure 10. Comparing the transient behavior between the CFD simulations and the experimental data for the horizontal flow configuration when switching from the S5 polymer–solvent mixture to the S4 solvent at flow rates of (A) 20, (B) 100, and (C) 200 cm3/min. The light-blue shaded area represents the error band for the experimental time traces (based on our experimental data reproducibility tests).

3.3. Effect of Fluid Viscosity on Infrared (IR) Signal Response Time

Because the standard horizontal flow configuration was identified as being poorly suited for highly viscous flows, we next aimed to identify the viscosity range for which this configuration shows an acceptable performance (i.e., rapid response times and stable signals). Therefore, IR signal time traces were investigated when switching from polymer–solvent mixtures with different viscosities to a pure solvent at flow rates of 20 and 100 cm3/min (the vertical flow configuration was not further investigated, as it had shown no significant improvement over the standard horizontal flow configuration). Figure 11 summarizes the results using plots of the response time (Δt) versus viscosity of the polymer–solvent mixture, where response time is defined as the time it takes to obtain a normalized signal of 0.05 after switching from the polymer–solvent mixture to the pure solvent. This time difference includes the initial delay time due to the flow volume between the three-way valve and the IR sensor.

Figure 11

Figure 11. Response time (Δt) to go from a normalized signal of 1 to 0.05 when switching from polymer–solvent mixtures with different viscosities to a pure solvent at flow rates of (A) 20 and (B) 100 cm3/min (note the different time scales for the y-axis).

Out of all of the configurations, the horizontal nozzle configuration resulted in the fastest response time (lowest Δt) across the entire range of viscosities studied. The vertical nozzle configuration showed similar behavior at lower viscosities, but at the lower flow rate of 20 cm3/min, its response time strongly increased at the highest viscosity (∼4800 mPa s) from <4 to 32 min. This unexpected behavior can be explained by the geometry of this configuration (Figure 2): the vertical nozzle configuration has dead volume between the walls of the vertical flow tube and the inserted pipe/nozzle. Thus, the solvent “jet” first sweeps the viscous polymer mixture from the IR sensor, as reflected in the rapid drop in the normalized anhydride signal in Figure 12 (blue line). However, due to the extreme viscous nature of S7, the dead volume slowly gets entrained with the pure solvent flow, resulting in the subsequent increase in the normalized signal. Once the dead volume is cleared, the signal resumes the expected drop to zero, albeit still with some instability/disruption due to small amounts of residual polymer in the dead volume. Similar behavior was observed for the horizontal nozzle configuration, as the dead volume between the walls of the horizontal path and the inserted pipe causes similar issues (Figure 12, red line); however, clearing the dead volume in this configuration takes slightly less time. This behavior is observed only for the highest-viscosity mixture (S7). Thus, for high-viscosity mixtures, achieving convective flow toward the IR sensor and avoiding dead volume are key for achieving rapid response times.

Figure 12

Figure 12. IR signal response time traces when switching from the S7 polymer–solvent mixture to the S4 solvent at a flow rate of 20 cm3/min for the horizontal nozzle (red line), vertical nozzle (blue line), and static mixer (green line) configurations, highlighting the effect of dead volume on the IR signal acquisition (the right figure is a zoomed-in version of the first 5 min of the left figure (the red shaded area)).

The static mixer configuration showed comparable performance to the vertical and horizontal nozzle configurations at higher flow rates (100 cm3/min), as their response times were almost the same in the range of viscosities investigated in this study (Figure 11B). However, the static mixer configuration was not as good as the nozzle configurations at low flow rates (∼20 cm3/min), having almost double the response time except at the highest viscosity (∼4800 mPa s), where the static mixer configuration showed comparable performance to the vertical nozzle configuration (Figure 11A). In terms of flow stability, the static mixer configuration outperformed both of the nozzle configurations, as it showed an expected “S-shaped” transition, which indicates the absence of the dead volume that was observed with the nozzle configurations (Figure 12, green line). Additionally, it is apparent that the horizontal flow configuration is poorly suited even for fluids with moderately low viscosities (∼10 mPa s) and low flow rates, as its response times are 3 times as long as those of the vertical and horizontal nozzle configurations. However, the standard horizontal configuration seems to be well-suited for higher flow rates (100 cm3/min) and very low viscosities (<10 mPa s), having response times that are comparable to those of the vertical and horizontal nozzle configurations.

3.4. Pressure Drop across the Flow Cell

While the static mixer as well as the horizontal and vertical nozzle configurations show much faster response times that enable accelerated data collection, the downside of these configurations is an increased pressure drop, as shown in Figure 13. For the horizontal and vertical nozzle configurations, pressure drops of up to ∼550 and ∼450 kPa, respectively, were obtained for the highest-viscosity polymer–solvent mixture at a flow rate 20 cm3/min. The static mixer configuration caused a significantly lower pressure drop (∼275 kPa) at the same flow rate; however, this pressure drop was still significantly higher than that of the horizontal flow configuration, which caused a negligible pressure drop. Thus, improved performance in terms of response time and signal quality was achieved at the expense of a higher pressure drop across the flow cell. Given that the static mixer configuration enables response times that are comparable to those of the nozzle configurations with about half the pressure drop, this configuration may be the best compromise for many experimental situations that deal with high-viscosity fluids.

Figure 13

Figure 13. Pressure drop across the flow cell for the different configurations at flow rates of (A) 20 and (B) 100 cm3/min.

4. Conclusion

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In-line FTIR spectrometry is widely used in research and in the industrial monitoring of continuous flows, as it enables real-time, in-line monitoring without the need for sampling and off-line analysis. It is also particularly well-suited for rapidly screening complex reaction systems and their intermediates, identifying reaction mechanisms and kinetics, detecting hazardous chemicals with minimal contact, and monitoring and controlling chemical processes. However, the probe sensor inside in-line flow cells is typically slightly offset from the flow for protective purposes. This offset causes the liquid volume above the IR sensor to form a stagnant boundary layer, especially when dealing with high-viscosity flows. Our investigation sheds light on the slow response times that are often observed with such systems and possible ways to accelerate data acquisition and improve data quality and stability without necessitating a complete redesign of the flow cell.
Our results confirm that the slow response of the typical standard configuration is indeed caused by a purely diffusion-driven fluid exchange in the boundary layer, which was further confirmed via 2D CFD simulations of the fluid flow inside the flow cell. Several alternative flow configurations were evaluated in order to identify the changes to the flow dynamics that enable much faster response times with minimal changes to the instrumental configuration: a vertical flow configuration that redirects the standard horizontal flow toward vertical contact with the IR sensor; a static mixer configuration that introduces a helical static mixer in the horizontal flow channel to create radial flow momentum above the IR sensor; and two nozzle configurations (vertical and horizontal nozzles) that utilize a smaller-diameter pipe inside the inlet channel to force flow impingement on the IR sensor. The overall observations are summarized qualitatively in Table 2. It was determined that the vertical flow configuration does not yield a significant improvement over the standard horizontal flow configuration. In contrast, the static mixer, horizontal nozzle, and vertical nozzle configurations resulted in significant improvements in response times and signal quality, albeit at the expense of a higher pressure drop, especially when using the nozzle configurations.
Table 2. Summary of the Experimental Data for the Different Flow Configurations with Ratings: Good (+), Moderate (o), and Poor (−)a
 horizontal flow (HF)vertical flow (VF)static mixer (SM)horizontal nozzle (HN)vertical nozzle (VN)
IR sensor signal responseo++
data reproducibility+++
pressure drop++o
a

In regard to the IR sensor signal response, data reproducibility, and pressure drop of the flow configurations, (+) indicates a fast response time, good data reproducibility, and a low pressure drop, respectively, while (−) indicates a slow response time, poor data reproducibility, and a high pressure drop, respectively. (o) represents moderate/medium values for all aspects.

Overall, our study shows that simple, user-accessible modifications to typical in-line IR sampling can achieve significant improvements in data acquisition (i.e., response times and signal stability) when dealing with high-viscosity flows without the need for a complete redesign of the flow cell configuration. These modifications reduce experimental run times, reduce the amount of raw materials required for kinetic experimentations, and improve the reliability of in-line IR measurements. Thus, such modifications should be valuable to a wide range of chemists and chemical engineers who use in-line IR spectrometry in their work.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.oprd.3c00299.

  • The reproducibility of the experimentally measured IR signal response for the three most efficient configurations, the structured orthogonal mesh details used for the CFD simulations, supporting data for the IR signal response for different mixtures, supporting data for the steady-state velocity profile and transient behavior of the normalized IR signal using CFD, and plots of experimentally measured temperatures and flow rates versus time for two different cases (PDF)

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Author Information

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  • Corresponding Author
  • Authors
    • Nasser Al Azri - Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, Pennsylvania 15261, United StatesOrcidhttps://orcid.org/0000-0003-3873-2592
    • Corey Clifford - Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, Pennsylvania 15261, United States
    • Robert M. Enick - Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, Pennsylvania 15261, United StatesOrcidhttps://orcid.org/0000-0003-1801-8033
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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The authors acknowledge the financial support from AIChE RAPID under contract #DEEE0007888-5-8. Additionally, this research was supported in part by the University of Pittsburgh Center for Research Computing through the computational resources provided.

References

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This article references 46 other publications.

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  1. Eva Deitmann, Gabriele Menges-Flanagan, Dirk Ziegenbalg. Infrared Spectroscopy as Process Analytics to Identify and Quantify Grignard Reagents. Organometallics 2024, 43 (3) , 219-226. https://doi.org/10.1021/acs.organomet.3c00441

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  • Abstract

    Figure 1

    Figure 1. Standard horizontal flow configuration, where the IR sensor tip (red) that is located inside the flow cell (gray) is offset by 1 mm from the horizontal flow channel.

    Figure 2

    Figure 2. The five flow configurations investigated in the present study: (A) standard horizontal flow (HF), (B) static mixer (SM), (C) horizontal nozzle (HN), (D) vertical nozzle (VN), and (E) vertical flow (VF).

    Figure 3

    Figure 3. Flow diagram of the continuous flow setup.

    Figure 4

    Figure 4. IR spectra of the solvent (dashed line) and polymer–solvent mixtures (solid lines). The polymer–solvent mixtures have a distinct peak between 1831 and 1738 cm–1, corresponding to the carbonyl group (C═O) of the polymer.

    Figure 5

    Figure 5. IR signal (shown here as the normalized anhydride signal) vs time when switching from the S4 solvent (viscosity = 42 mPa s) to the S5 polymer–solvent mixture (viscosity = 134 mPa s) at flow rates of (A) 20, (B) 100, and (C) 200 cm3/min (note the different time scales for the x-axis).

    Figure 6

    Figure 6. IR signal (shown here as the normalized anhydride signal) vs time when switching from the S5 polymer–solvent mixture (viscosity = 134 mPa s) to the S4 solvent (viscosity = 42 mPa s) at flow rates of (A) 20, (B) 100, and (C) 200 cm3/min (note the different time scales for the x-axis).

    Figure 7

    Figure 7. Steady-state velocity profile developed using CFD for the horizontal flow configuration using the physical properties of the S5 polymer–solvent mixture and an inlet flow rate of 20 cm3/min.

    Figure 8

    Figure 8. Steady-state velocity profile developed using CFD for the vertical flow configuration using the physical properties of the S5 polymer–solvent mixture and an inlet flow rate of 20 cm3/min.

    Figure 9

    Figure 9. Steady-state velocity profile developed using CFD for the horizontal nozzle configuration using the physical properties of the S5 polymer–solvent mixture and an inlet flow rate of 20 cm3/min.

    Figure 10

    Figure 10. Comparing the transient behavior between the CFD simulations and the experimental data for the horizontal flow configuration when switching from the S5 polymer–solvent mixture to the S4 solvent at flow rates of (A) 20, (B) 100, and (C) 200 cm3/min. The light-blue shaded area represents the error band for the experimental time traces (based on our experimental data reproducibility tests).

    Figure 11

    Figure 11. Response time (Δt) to go from a normalized signal of 1 to 0.05 when switching from polymer–solvent mixtures with different viscosities to a pure solvent at flow rates of (A) 20 and (B) 100 cm3/min (note the different time scales for the y-axis).

    Figure 12

    Figure 12. IR signal response time traces when switching from the S7 polymer–solvent mixture to the S4 solvent at a flow rate of 20 cm3/min for the horizontal nozzle (red line), vertical nozzle (blue line), and static mixer (green line) configurations, highlighting the effect of dead volume on the IR signal acquisition (the right figure is a zoomed-in version of the first 5 min of the left figure (the red shaded area)).

    Figure 13

    Figure 13. Pressure drop across the flow cell for the different configurations at flow rates of (A) 20 and (B) 100 cm3/min.

  • References


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  • Supporting Information

    Supporting Information


    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.oprd.3c00299.

    • The reproducibility of the experimentally measured IR signal response for the three most efficient configurations, the structured orthogonal mesh details used for the CFD simulations, supporting data for the IR signal response for different mixtures, supporting data for the steady-state velocity profile and transient behavior of the normalized IR signal using CFD, and plots of experimentally measured temperatures and flow rates versus time for two different cases (PDF)


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