Evaluation of Thermal Performance and Energy Efficiency of a Continuous Milk Pasteurization System Using a PTFE Pipe and Vegetable Oil Heating Medium

The United Kingdom’s Net Zero 2050 target is driving all industries toward decarbonisation. The food and drink industry (FDI), one of the UK’s largest production sectors, relies heavily on energy-intensive processes such as fermentation, pasteurisation, and microbial inactivation, most of which rely on fossil fuel combustion. Consequently, this sector contributes approximately 26% of greenhouse gas emissions, equivalent to around 9.1 million tonnes of CO2 annually. Additionally, the UK’s rapid transition to renewable electricity generation is also encouraging the FDI to adopt electrically driven heating methods. In this context, Ohmic Heating (OH) has emerged as a promising alternative, offering rapid and energy-efficient heating. Among its configurations, Continuous Flow Ohmic Heating (CFOH) is particularly suited for pumpable products such as soups, sauces, and juices. Despite its potential, the adoption of CFOH in industry is limited by its complex, multi-physics nature, which involves coupled electrical, thermal, and fluid dynamic phenomena. Existing mathematical and Computational Fluid Dynamics (CFD) models provide partial solutions. Mathematical models often oversimplify system behaviour through linearisation, while CFD models, though accurate, are computationally expensive. This creates a clear need for modelling approaches that are both computationally efficient and capable of accurately capturing CFOH thermophysical behaviour. Achieving this requires a thorough understanding of the interactions between key process variables and product properties, forming the foundation for optimised control system design. To address this gap, this research first conducted a comprehensive literature review to identify the key parameters influencing CFOH performance. Based on these insights, a high-fidelity physical model was developed in MATLAB Simscape to simulate CFOH behaviour under varying product conditions. The model was validated against both established mathematical formulations and experimental data from a pilot-scale CFOH system, achieving a mean absolute percentage error (MAPE) of ±5% in terms of accuracy. Its ability to accurately predict temperature variations in response to changes in thermophysical properties makes it a robust platform for advanced control development. Utilising this validated model, three control strategies were designed and tested, including a conventional Proportional–Integral–Derivative (PID) controller, a Model Predictive Control (MPC) scheme, and a novel Neural Network-based Model Reference Control (MRC). These were implemented on a pilot-scale CFOH system at the Advanced Food Innovation Centre (AFIC), Sheffield Hallam University, and evaluated for temperature tracking accuracy, stability, and responsiveness. Results demonstrated significant improvements in process control and energy efficiency, with successful industrial trials on products such as sweet and sour sauce and tikka sauce, achieving process efficiency gains of up to 87% while maintaining product quality. However, insights were gained during the development stage of this work that CFOH performance is sensitive to the electrical conductivity, physical properties, and rheology of the product, particularly dynamic viscosity, which affects flow behaviour, residence time, and heating rate. Accurate viscosity prediction during processing is therefore essential for optimising system performance. To address this challenge, the study presents OhmNet, a Neural Network-based soft sensor capable of predicting the dynamic viscosity of tikka sauce during processing with a mean squared error (MSE) of 0.002, demonstrating exceptional predictive accuracy. The integration of OhmNet with advanced controllers enables optimised temperature regulation and power consumption, supporting sustainable and energy-efficient operation. Overall, this work addresses a gap in existing literature where, to the best of the author’s knowledge, no unified methodology exists that integrates physics-based modelling, real-time control implementation, and AI-driven soft sensing within food processing. Addressing this gap, the study delivers three key novel contributions: 1. development of an accurate physical model of CFOH that captures the coupled electrical, thermal, and fluid dynamics; 2. real-time implementation and comparative evaluation of multiple advanced control strategies; and 3. integration of a machine learning-based soft sensor for intelligent viscosity prediction. By combining these elements into a validated, scalable workflow, this work delivers a robust solution for optimised, sustainable, and industrially relevant ohmic food processing. The framework directly supports the UK’s Net Zero ambitions while advancing the broader objective of low-carbon, energy-efficient food manufacturing.

Wastewater pollutants represent a threat to both aquatic and terrestrial environs. In river systems, and other aquatic environs, high concentrations of carbon and nutrient compounds from sewage can cause serious degradation, depleting the water of oxygen as well as promoting algal blooms. If our environment is to be conserved, the efficient and economic removal of these pollutants is a problem that needs to be addressed.The performance of the wastewater removal process is strongly related to the design and operation of the wastewater facility. Most current methods for design of treatment vessels are based on empirical and heuristic techniques, and they cannot adequately predict how vessel configuration, such as the size and position of inlets, baffles or mixers, affects the hydrodynamics and the overall performance. Computational Fluid Dynamics (CFD) can provide a method for simulation of different designs to predict the effect on performance. The biological reactions that are used to remove pollutants from the wastewater occur primarily within, what can conceptually be thought as, bio-catalytic particles. These particles suspended in the liquid form 'sludge'. The reactor design, through the hydrodynamics, influences the movement of the sludge and reactant species (pollutants) through the vessel and this determines the removal of pollutants from wastewater. The CFD model developed here, incorporates the three-dimensional hydrodynamics, sludge and species transport. The sludge transport has a slip velocity in the vertical direction to account for settling, and the density gradients it forms, which influence the hydrodynamics. The species that are transported in this system are soluble substrate (a carbon source) and nitrate, while the source and sink terms are the biochemical reactions relying on the local concentration of sludge. The biological reactions are derived from the Activated Sludge Model Number 1 (Henze et al 1987). Experimental measurements were made on an existing system and were used for calibration and validation of the model. The system used for this purpose is an anoxic section of a bioreactor located at the Luggage Point Wastewater Treatment Facility, Brisbane, Australia. In this part of the reactor nitrate is biologically converted to nitrogen gas, through consumption of soluble substrate species. Calibration included the setting of the velocity profile, turbulence parameters and sludge concentration at the inlets with parameters of the phenomenological settling model. Validation included comparison of point velocities, predicted velocity profiles and residence time distribution curves. The velocity comparisons demonstrated the difficulty of its use in validation but the CFD model was generally able to predict experimentally measured data. The residence time distribution of the vessel examined was predicted very well by the CFD model, with a correlation coefficient of above 0.97.Once the model was calibrated and validated it allowed the investigation the effect of various reactor modifications (baffle number and positions, mixer number, position and direction). Scenarios of differing internal configurations were developed to examine these effects, where performance measures were developed and applied to investigate each scenario. These investigations show that a flow type closer to plug flow gave greater removal of pollutants. The design modifications that aided in the producing this good flow type were the even distribution of mixers and baffles along the reactor length to induce several well mixed zones in series (approximating plug flow) and the redirection of mixers against the flow thus aiding in creating these well mixed zones. This demonstrated that CFD along, in conjunction with the design methodology, experimental calibration and validation techniques and the performance measures provided, is a valuable design tool. The CFD tools developed provide an improvement to our fundamental understanding of mixed wastewater treatment vessels and fill a gap in the current state of understanding of this field of knowledge.

<b><sc>Abstract. </sc></b>Advective heat and mass transfer, due to pressure differences created by wind or buoyancy, dominates the exchange processes in naturally ventilated structures. The air exchange rate (AER) quantifies this transfer. The airflow patterns including air velocities and turbulences govern the indoor environmental parameters such as temperature, gases and humidity. These patterns form the essential link between the outdoor environment and the buildings microclimate; thus, an understanding of the principles of air motion is necessary in order to provide the correct quantities of air and the proper distribution patterns to meet the needs of the application. Computational fluid dynamics (CFD) have been applied in very limited studies for naturally ventilated animal houses considering wind directions and surrounding buildings. This paper presents isothermal CFD simulations with a naturally ventilated dairy (NVD) barn model to assess the influence of wind direction and surroundings on the AER and indoor and outdoor airflow distributions. A typical NVD building and its surroundings located in Northeast Germany were selected for model development and simulation. ANSYS Workbench 2020R1 platform (ANSYS Inc) was used for creating model geometry, meshing and simulation. Simulations were performed for four wind directions and for a computation model with and without surrounding buildings. The standard kinetic energy (k)-dissipation (ε) turbulence model was used for all simulations. In order to improve the quality of CFD calculations, i.e. to make calculations with sufficient accuracy, several important issues (e.g. grid independency and convergence criteria etc) were considered carefully for both the governing equations and the computation. CFD validation was performed with the measured data from a boundary layer wind tunnel under strictly controlled laboratory conditions taking into account full scale measurement. The results showed that the simulated differences in AER between wind directions can go up to 52.2% (without surroundings) and 65.1% (with surroundings). Furthermore, comparing the simulations with and without surrounding buildings showed that neglecting the surroundings can lead to overestimation of the AER with up to 52 %. Further investigations are required to understand the airflow pattern and estimate AER in unsteady conditions considering wind directions and surroundings.

This paper presents the preliminary steps required for conducting experiments to obtain the optimal operating conditions of a hybrid impeller mixer and to determine the residence time distribution (RTD) using computational fluid dynamics (CFD). In this paper, impeller speed and clearance parameters are examined. The hybrid impeller mixer consists of a single Rushton turbine mounted above a single pitched blade turbine (PBT). Four impeller speeds, 50, 100, 150, and 200 rpm, and four impeller clearances, 25, 50, 75, and 100 mm, were the operation variables used in this study. CFD was utilized to initially screen the parameter ranges to reduce the number of actual experiments needed. Afterward, the residence time distribution (RTD) was determined using the respective parameters. Finally, the Fluent-predicted RTD and the experimentally measured RTD were compared. The CFD investigations revealed that an impeller speed of 50 rpm and an impeller clearance of 25 mm were not viable for experimental investigations and were thus eliminated from further analyses. The determination of RTD using a ????-???? turbulence model was performed using CFD techniques. The multiple reference frame (MRF) was implemented and a steady state was initially achieved followed by a transient condition for RTD determination. This study showed that the optimum conditions for mixing operations in a hybrid impellerare100 rpm and 50 mm for impeller speed and clearance height, respectively, which resulted in an RTD curve with a strong, sharp peak.

A number of models exist to simulate the residence time distribution (RTD) of a system or process. Four of these models known as the tanks in series model, axial dispersion model (ADM), aggregated dead zone model, and the advection dispersion equation, have been used to assess which is most suitable for representing the RTD of a hydrodynamic vortex separator (HDVS) when compared to RTD measurements taken under laboratory conditions on a full-scale 3.4 m diameter unit. Computational fluid dynamics (CFD) is also used to model the HDVS and compare with the RTD models and experimental measurements. It has been shown that the fit by each of the RTD models to observed RTDs vary quite considerably, with the ADM being the most appropriate for the HDVS studied, based on having the highest R t2 value. Given the number of model variables that influence CFD predictions, the outputs from the CFD models appear to be reasonable.

Double-pipe heat exchangers (DPHEs) are vital in industrial applications, and improving their performance is crucial for sustainability. This study uses three-dimensional Computational Fluid Dynamics (CFD) simulations to investigate the impact of passive flow modifications, specifically geometrically spaced and perforated ring inserts, on heat transfer and pressure drop in a DPHE. The research involved developing a 3D numerical model whose accuracy was ensured through a comprehensive mesh independence study and rigorous validation against established empirical correlations and experimental data. Subsequent simulations explored the influence of geometric spacing ( G -factor) and the number of perforations per ring. Results demonstrated that for unperforated rings P = 0 , uniform spacing maximised heat transfer, reaching a Nusselt number of 177.4 at a Reynolds number of 12,000. In contrast, strongly biased configurations exhibited superior overall performance by balancing thermal enhancement with hydraulic losses. These biased cases achieved a Performance Evaluation Criterion (PEC) of 1.065, equivalent to a 6.5% improvement compared with the uniform arrangement. The introduction of perforations significantly altered performance; a four-hole configuration with G = 1 . 00 consistently achieved the highest heat transfer and overall performance, with the Nusselt number rising to 195.8 and the PEC reaching 1.176, indicating an optimal balance between fluid mixing and flow resistance. By comparison, increasing the number of perforations further to eight reduced the pressure drop from 171 . 9 Pa for solid rings to 132 . 0 Pa , but had a less pronounced positive impact on heat transfer performance. For this configuration, the Nusselt number remained close to that of the unperforated case. Analysis of both turbulent kinetic energy (TKE) and velocity vector fields provided critical insights into the underlying mechanisms, illustrating how ring geometry and perforations disrupt boundary layers and generate beneficial turbulence. Furthermore, regression-based multivariate correlations for both the Nusselt number and friction factor were formulated as functions of Reynolds number, G -factor, and porosity. Validation against the full set of 75 CFD simulation cases demonstrated high accuracy, with 96% of the correlation-predicted Nusselt numbers deviating by less than ± 5 % from the corresponding CFD results, and the equivalent friction factor values deviating by less than ± 10 % . • Non-uniform ring spacing significantly affects DPHE thermo-hydraulic behaviour. • Four-hole perforated rings provide the best heat-transfer and pressure balance. • Strongly biased ring spacing yields higher overall PEC than weakly biased layouts. • CFD reveals mixing patterns driven by combined spacing and perforation effects. • Correlations predict Nusselt number and friction factor for 75 DPHE cases.

Cold storage facilities consume substantial energy due to refrigeration loads, with the building envelope acting as the dominant contributor. This study evaluates the thermal performance of insulation systems for cold storage buildings through numerical simulation and experimental validation. Three representative composite wall assemblies were modeled using a computational fluid dynamics (CFD) approach, and key thermal indicators such as temperature distribution, heat flux density, and U-value were obtained. Validation against published experimental data confirmed simulation accuracy within 0.5%. The results demonstrate that multilayer composite insulation with thick PIR (polyisocyanurate) layers significantly suppresses heat transfer, with calculated U-values ranging from 0.0810 to 0.0466 W·m −2 ·K −1 . These findings provide a quantitative basis and practical guidance for the energy-efficient design of cold storage envelopes.

Hydraulic servo valves are widely used in various fluid power systems because of their fast response and precision control. In this paper, we studied the effect of metering notch shapes and amount of their openings on the flow characteristics within the spool valve using a computational fluid dynamic (CFD) code, FLUENT. To obtain the results for more realistic operating conditions, viscous heating due to the jet flow and viscosity variation of the hydraulic fluid with temperature were considered. For two types of notch shape, streamlines, oil temperature and viscosity distributions, and variations of flow and friction forces acting on spool were showed. The flow and friction forces affected by the metering notch shapes and their openings, and oil temperature rise near metering notch was significant enough to results in the jamming phenomenon. A thermohydrodynamic (THD) flow analysis adopted in this paper can be used in optimum design of hydraulic servo valves.

This paper presents the preliminary steps required for conducting experiments to obtain the optimal operating conditions of a hybrid impeller mixer and to determine the residence time distribution (RTD) using computational fluid dynamics (CFD). In this paper, impeller speed and clearance parameters are examined. The hybrid impeller mixer consists of a single Rushton turbine mounted above a single pitched blade turbine (PBT). Four impeller speeds, 50, 100, 150, and 200 rpm, and four impeller clearances, 25, 50, 75, and 100 mm, were the operation variables used in this study. CFD was utilized to initially screen the parameter ranges to reduce the number of actual experiments needed. Afterward, the residence time distribution (RTD) was determined using the respective parameters. Finally, the Fluent-predicted RTD and the experimentally measured RTD were compared. The CFD investigations revealed that an impeller speed of 50 rpm and an impeller clearance of 25 mm were not viable for experimental investigations and were thus eliminated from further analyses. The determination of RTD using a k-ε turbulence model was performed using CFD techniques. The multiple reference frame (MRF) was implemented and a steady state was initially achieved followed by a transient condition for RTD determination.

Appraisal of chlorine contact tank modelling practices

Multi-scale modeling and control of chemical looping gasification coupled coal pyrolysis system for cleaner production of synthesis gas

Fluid activation is of critical importance to fusion power plant design, as it impacts dose rates to maintenance personnel and equipment, heating in sensitive components, and radionuclide inventories with implications for accident scenarios and waste. The levels of fluid activation in a system are dependent on the neutron flux spectrum and the exposure time of the fluid. Accurately modelling the fluid irradiation history for a pipe system requires computational fluid dynamics (CFD) to determine the residence time distribution (RTD) of fluid particles passing through each component. However, performing CFD on whole pipe systems can be computationally expensive which limits frequency of design iterations. To address this concern, a new code was developed: FARBASE (the Fluid Activation Residence time dataBASE). This paper details the development and functionality of FARBASE, focusing on its two key features: An automated CFD pipeline which accepts a parametric description of a pipe component under given flow conditions, generates the pipe geometry, runs a steady-state OpenFOAM simulation, and returns the resulting RTD. A Gaussian process regression (GPR) surrogate model which can be trained on the CFD database and queried to provide uncertainty quantified predictions of RTDs. Where the uncertainty of the GPR prediction exceeds a given threshold, FARBASE can automatically perform additional CFD to update the database, improving the accuracy of future predictions. Work is ongoing to utilise FARBASE in UKAEA’s GammaFlow fluid activation code, providing RTDs which are used to determine the production and decay rates of key radionuclides in each component of a basic water circuit. This will be extended in future to model benchmark experiments and validate the combined tool, which aims to provide an efficient and standardised approach to modelling activation in complex fluid circuits.

Residence time distribution in a structured packing unit for monitoring aerosol emissions

This study presents an energy efficiency analysis of an electric furnace used for tempering heat treatments by implementing a forced convection fan. Improving energy efficiency in industrial heating systems remains a critical challenge, driven by the need to lower operational costs and enhance sustainability. A numerical model was developed based on heat transfer mechanisms, applying computational fluid dynamics (CFD) with a mesh of 138 565 elements and a validated mesh quality factor of 4.681. The continuity, momentum, and energy conservation equations were analyzed under real operating conditions. Results indicated that the maximum temperature increased from 290 to 327.2 K with the addition of the fan, while electrical consumption rose by only 1.54%, corresponding to an additional cost of merely USD 0.0005 per operating cycle. This thermal enhancement promotes greater temperature uniformity and reduces operational times. Consequently, integrating a forced convection system in industrial electric furnaces proves to be a technically and economically viable strategy.

This study performed a computational analysis and experimental validation of a hybrid photovoltaic-thermal (PVT) system combined with a heat pump for engine heating in thermal power plants. Using the Ansys Fluent software for computational fluid dynamics simulations, the research examined the thermal performance and efficiency of the PVT system under real operating conditions. The simulations confirmed that the PVT system could achieve high thermal efficiency, especially during periods of maximum solar radiation. The mesh model used in the simulations comprised 6,589,347 elements, refined to capture the details of fluid flow and heat transfer. The results indicated that the maximum outlet water temperature reached 315 K, while the experimental tests showed a maximum temperature of 328.15 K. The maximum thermal efficiency observed was 73% at noon. The study also demonstrated the feasibility of scaling up the system from a bench-scale prototype to industrial applications. By employing the Boussinesq approximation and maintaining the dimensionless Reynolds, Nusselt, Prandtl, Grashof, and Rayleigh numbers, the downscaled simulations were shown to be reliable and comparable to full-scale systems. The integration of the PVT system with a heat pump proved to be effective in reducing fossil fuel consumption, enabling simultaneous generation of electricity and heat, thereby improving energy efficiency and reducing operating costs in industrial settings. The PVT system faces climate constraints, high costs, and industrial integration challenges. The present study acknowledges the challenges in the widespread adoption of PVT systems and suggests future research to optimize these systems in diverse climatic and geographic contexts.