Heat Transfer Performance of Milk Pasteurization Plate Heat Exchangers Using MWCNT/Water Nanofluid

Advances of nanofluid in food processing: Preparation, thermophysical properties, and applications

Nanofluids (NFs) are homogenous mixes of solid nanoparticles as well as base fluid in which the size of the solid nanoparticles (NPs) is smaller than 100 nm. These solid NPs are intended to enhance the thermophysical characteristics and heat transmission attributes of the base fluid. The thermophysical characteristics of nanofluids are influenced by their density, viscosity, thermal conductivity and specific heat. These colloidal solutions of nanofluids include condensed nanomaterials such as nanoparticles, nanotubes, nanofibers, nanowires, nanosheets, and nanorods. The effectiveness of NF is significantly influenced by temperature, shape, size, type, as well as the concentration of NPs or the thermal characteristics of the base fluid. Compared to oxide NPs, metal NPs have superior thermal conductivity. Many of these investigations revealed that hybrid NFs had enhanced thermal conductivity than traditional ones. Thermal conductivity values are reduced by the formation of clusters in nanofluid. When compared to spherically formed nanoparticles, cylindrically shaped nanoparticles produced superior outcomes. In food industries, NFs could be used in various unit operations where heat needs to be transported from a heating or cooling medium to food product using a heat exchanger, as in freezing, pasteurization, refrigeration, drying, thawing, sterilization, and evaporation. The objective of this review is to analyze the recent developments in the research of nanofluids including innovative production methods, stability assessment, enhancement approaches, and thermophysical properties of nanofluids.

On the Role of Nanofluids in Thermal-hydraulic Performance of Heat Exchangers-A Review

Heat exchangers are key components in many of the devices seen in our everyday life. They are employed in many applications such as land vehicles, power plants, marine gas turbines, oil refineries, air-conditioning, and domestic water heating. Their operating mechanism depends on providing a flow of thermal energy between two or more mediums of different temperatures. The thermo-economics considerations of such devices have set the need for developing this equipment further, which is very challenging when taking into account the complexity of the operational conditions and expansion limitation of the technology. For such reasons, this work provides a systematic review of the state-of-the-art heat exchanger technology and the progress towards using nanofluids for enhancing their thermal-hydraulic performance. Firstly, the general operational theory of heat exchangers is presented. Then, an in-depth focus on different types of heat exchangers, plate-frame and plate-fin heat exchangers, is presented. Moreover, an introduction to nanofluids developments, thermophysical properties, and their influence on the thermal-hydraulic performance of heat exchangers are also discussed. Thus, the primary purpose of this work is not only to describe the previously published literature, but also to emphasize the important role of nanofluids and how this category of advanced fluids can significantly increase the thermal efficiency of heat exchangers for possible future applications.

Development of Simple-to-Use Predictive Models to Determine Thermal Properties of Fe2O3/Water-Ethylene Glycol Nanofluid

Thermophysical properties of nanofluids play a key role in their heat transfer capability and can be significantly affected by several factors, such as temperature and concentration of nanoparticles. Developing practical and simple-to-use predictive models to accurately determine these properties can be advantageous when numerous dependent variables are involved in controlling the thermal behavior of nanofluids. Artificial neural networks are reliable approaches which recently have gained increasing prominence and are widely used in different applications for predicting and modeling various systems. In the present study, two novel approaches, Genetic Algorithm-Least Square Support Vector Machine (GA-LSSVM) and Particle Swarm Optimization- artificial neural networks (PSO-ANN), are applied to model the thermal conductivity and dynamic viscosity of Fe2O3/EG-water by considering concentration, temperature, and the mass ratio of EG/water as the input variables. Obtained results from the models indicate that GA-LSSVM approach is more accurate in predicting the thermophysical properties. The maximum relative deviation by applying GA-LSSVM was found to be approximately ±5% for the thermal conductivity and dynamic viscosity of the nanofluid. In addition, it was observed that the mass ratio of EG/water has the most significant impact on these properties.