Optimization of Thermal Conductivity and Viscosity of Liquid Mixtures Using an Automated Continuous Flow System

Unlike pure liquids, liquid mixtures have highly tuneable thermophysical properties. Thanks to this characteristic, liquid mixtures have been widely adopted in a range of applications such as pharmaceuticals, polymers, and machinery. However, enhancement in one property may come at the expense of another. There is hence a need to finetune the composition of the mixture to achieve the desired outcome. Moreover, the vast parameter space and complex optimization response surfaces make it difficult to determine the optimum ratios manually due to the cost, time, and number of trials required. Automated experimentation systems coupled with machine learning models can expedite the optimization process. Continuous flow systems can provide experimental data to train the machine learning models to predict the optimal composition.<br/>Nanofluids are suspension of nanoparticles (1–100 nm) such as CuO, Al₂O₃, Ag, graphene, and carbon nanotubes (CNTs) in a base fluid. The addition of these nanoparticles has shown to enhance the thermal conductivity of the base fluid, e.g. water and ethylene glycol. Increasing the concentration of nanoparticles often comes at the cost of increased viscosity, which is an issue in practical systems as higher viscosity means more energy expenditure when driving fluid flow. Experimental investigations into nanofluids have generally been performed manually. The ethylene glycol-water ratio is usually fixed while only a few, select concentrations of nanoparticles are investigated. This research aims to build an automated continuous flow system to optimize the thermal conductivity and viscosity of ethylene glycol, water, and nanoparticle mixtures, a mixture often used in anti-freeze applications and automobile coolants.<br/> In this work, we integrate online optimization techniques into an automated continuous flow system to tune the thermophysical properties of ethylene glycol-water-silver nanowire (EG-W-AgW) nanofluids. The system finds the optimal solid-volume ratio that minimizes the viscosity and maximizes the thermal conductivity of the nanofluid. Different mixture ratios were continuously sampled while the viscosity and thermal conductivity were measured inline using modified capillary rheometer and transient hot-wire techniques, minimizing the time and liquid consumption required for each optimization trial. The viscosity of the fluid mixture was calculated by measuring the fluid pressure at two points on the tube. The thermal conductivity was estimated by inducing Joule heating in a platinum wire and observing the temperature rise of the wire.<br/> Several optimization algorithms such as Stable Noisy Optimization by Branch and Fit (SNOBFIT), Thompson Sampling Efficient Multi-objective Optimization (TSEMO), and Multi-Objective Particle Swarm Optimization (MOPSO) are applied to optimize the weighted objective function involving viscosity and thermal conductivity. The optimal ratio of EG-W-AgW predicted by each algorithm is compared. The proposed system enables the optimization algorithm to recommend parameters and the automated measurement system tests the predicted parameters immediately.