PFG NMR and Bayesian analysis to characterise non-Newtonian fluids

Thomas W Blythe; Andrew J Sederman; E Hugh Stitt; Andrew P E York; Lynn F Gladden

doi:10.1016/j.jmr.2016.11.003

PFG NMR and Bayesian analysis to characterise non-Newtonian fluids

J Magn Reson. 2017 Jan:274:103-114. doi: 10.1016/j.jmr.2016.11.003. Epub 2016 Nov 10.

Authors

Thomas W Blythe¹, Andrew J Sederman², E Hugh Stitt³, Andrew P E York⁴, Lynn F Gladden¹

Affiliations

¹ Magnetic Resonance Research Centre, Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, UK.
² Magnetic Resonance Research Centre, Department of Chemical Engineering and Biotechnology, University of Cambridge, Pembroke Street, Cambridge CB2 3RA, UK. Electronic address: ajs40@cam.ac.uk.
³ Johnson Matthey Technology Centre, Billingham TS23 1LB, UK.
⁴ Johnson Matthey Technology Centre, Blounts Court, Sonning Common, Reading RG4 9NH, UK.

PMID: 27898299
DOI: 10.1016/j.jmr.2016.11.003

Abstract

Many industrial flow processes are sensitive to changes in the rheological behaviour of process fluids, and there therefore exists a need for methods that provide online, or inline, rheological characterisation necessary for process control and optimisation over timescales of minutes or less. Nuclear magnetic resonance (NMR) offers a non-invasive technique for this application, without limitation on optical opacity. We present a Bayesian analysis approach using pulsed field gradient (PFG) NMR to enable estimation of the rheological parameters of Herschel-Bulkley fluids in a pipe flow geometry, characterised by a flow behaviour index n, yield stress τ₀, and consistency factor k, by analysis of the signal in q-space. This approach eliminates the need for velocity image acquisition and expensive gradient hardware. We investigate the robustness of the proposed Bayesian NMR approach to noisy data and reduced sampling using simulated NMR data and show that even with a signal-to-noise ratio (SNR) of 100, only 16 points are required to be sampled to provide rheological parameters accurate to within 2% of the ground truth. Experimental validation is provided through an experimental case study on Carbopol 940 solutions (model Herschel-Bulkley fluids) using PFG NMR at a ¹H resonance frequency of 85.2MHz; for SNR>1000, only 8 points are required to be sampled. This corresponds to a total acquisition time of <60s and represents an 88% reduction in acquisition time when compared to MR flow imaging. Comparison of the shear stress-shear rate relationship, quantified using Bayesian NMR, with non-Bayesian NMR methods demonstrates that the Bayesian NMR approach is in agreement with MR flow imaging to within the accuracy of the measurement. Furthermore, as we increase the concentration of Carbopol 940 we observe a change in rheological characteristics, probably due to shear history-dependent behaviour and the different geometries used. This behaviour highlights the need for online, or inline, rheological characterisation in industrial process applications.

Keywords: Bayesian analysis; Complex fluid; Herschel-Bulkley; NMR; PFG; Propagator; Rheology; Yield stress.

Publication types

Research Support, Non-U.S. Gov't