The digitalization of industrial drying is critical for enabling real-time monitoring, adaptive control, and process optimization in energy-intensive manufacturing systems. Although high-fidelity CFD provides a rigorous framework for resolving the underlying multiphysics transport processes, its computational cost precludes real-time control. This challenge is compounded by the wide geometric variability of products across industrial drying lines, which forces conventional surrogate models to be re-trained or re-meshed for each new shape. Geometry-aware yet geometry-agnostic surrogate models offer a scalable alternative by enabling physics-consistent prediction across evolving geometries. Here, we present a region-aware surrogate framework integrated with sequential data assimilation for three-dimensional convective drying of hygroscopic porous media. The proposed Tri-State Additive Physics (TSAP) graph neural network mirrors the governing-equation decomposition of the domain: a shared message-passing backbone captures transport common across regions, while additive residual networks encode terms unique to the air domain (Navier–Stokes), porous interior (Brinkman–Darcy drag), and evaporative interface. A CFD model benchmarked against laboratory potato-slice drying experiments (R2 = 0.951, RMSEMwb = 0.055 kgwater/kgwet) was used to generate 240 synthetic cases, of which 192 were used for training. It was shown the TSAP surrogate achievesRMSEMwb ≤ 0.011 over 333-min of drying and it generalizes robustly to unseen ellipsoidal, square, and pyramidal geometries, where the baseline fails to produce reliable predictions (2.7–13 × lower per-field prediction error). Our findings also demonstrate that accurate bulk moisture predictions alone are insufficient to guarantee spatial drying uniformity, as localized high-moisture pockets may persist despite acceptable volume-averaged moisture content. Integration of the proposed sequential data assimilation framework with virtual surface-imaging observations further enhances spatial predictive fidelity, achieving a 24–53 % reduction in prediction drift with less than 1 % additional computational cost.

Industrial drying accounts for approximately 10% of global industrial energy consumption, and its effective control depends on accurately predicting the coupled spatiotemporal evolution of temperature, moisture, and airflow. However, these dynamics are governed by computationally intensive partial differential equations, and data-driven surrogates suffer from error accumulation over long time horizons, limiting real-time control and optimization. To address these limitations, this work presents a hybrid digital twin framework for real-time monitoring and control of convective drying processes, coupling a graph neural network (GNN) surrogate with nudging-based data assimilation for three-dimensional drying of porous media. A tri-state additive physics (TSAP) GNN architecture is developed to model coupled transport phenomena across heterogeneous domains: a shared message-passing backbone captures global transport interactions, while region-specific residual networks encode the distinct physical behavior of the air domain, porous interior, and evaporative interface. The framework is evaluated on convective hot-air drying of potato slices and validated against experimental measurements (RMSEMwb = 0.055 kgwater/kgwet). Trained on 192 high-fidelity CFD simulations, the surrogate achieves RMSEMwb ≤ 0.011 kgwater/kgwet across all test cases. While bulk moisture predictions are accurate, they mask localized errors near the air–porous interface. Data assimilation improves both bulk and local accuracy by up to 95% and 83%, respectively, with corrections propagating from virtual sensor locations to unobserved regions through the learned message-passing dynamics. The proposed framework enables near real-time, spatially resolved monitoring of drying processes and is scalable to complex three-dimensional geometries, providing a practical pathway toward improved energy efficiency, reduced operational uncertainty, and closed-loop control in industrial drying systems.

Laser technologies, characterized by their unique properties such as monochromaticity, coherence, and directionality, offer versatile, non-contact, and sustainable solutions across the agri-food supply chain. In food systems, lasers contribute to improved safety and quality control through non-thermal microbial inactivation, laser-assisted preservation, and real-time contamination detection. Laser-based heating technologies have demonstrated considerable potential for applications in cooking and baking operations. The laser technology also enhances product traceability and sustainability via permanent, eco-friendly labeling and packaging innovations. Moreover, laser-based sorting and grading systems improve food quality assurance by enabling rapid, non-destructive detection of contaminants and defects. In agriculture, laser applications extend from seed biostimulation and germination enhancement to precision agriculture and pest management. Techniques such as LiDAR mapping, laser-induced spectroscopy, and photobiomodulation are advancing crop monitoring, yield prediction, and stress management. Low-level laser therapy has been shown to enhance photosynthesis, nutrient metabolism, and stress tolerance, while laser-assisted irrigation and land leveling systems significantly improve water-use efficiency. Furthermore, lasers are emerging as sustainable tools for pest and disease control, offering targeted, residue-free alternatives to chemical pesticides. By integrating laser-based systems into food processing, agricultural monitoring, and crop management, this review underscores their transformative potential for achieving sustainability, precision, and quality in the global agri-food sector. Continued interdisciplinary research is essential to optimize laser parameters, reduce costs, and scale applications for commercial adoption.

Air convective drying is an important food processing technology contributing to moisture reduction and food product preservation. Optimization of air convective drying is crucial to achieve high food quality and process efficiency. However, existing drying optimization methods have two critical limitations. First, conventional response surface methodology cannot adequately account for the intricate relationships between process variables and responses, and fails in optimization of multiple drying objectives including drying quality, drying time, and energy consumption. Second, process uncertainties are ubiquitous in industrial food drying, but existing modeling approaches often neglect these uncertainties. To address these limitations, this paper develops an uncertainty-aware constrained optimization framework for air convective drying of thin apple slices. Specifically, we employ machine learning techniques to establish variable-response relationships. The Monte Carlo simulation-based approach is utilized for uncertainty quantification. A constrained optimization method is then used to identify feasible design spaces and find the optimal process parameters. To validate our framework, we conduct drying experiments simulating real-world settings featured by thin apple slices and process uncertainties (e.g., sample thickness). Further, multiple key quality characteristics including color, texture, and water activity are measured and considered within the proposed framework. The developed response surface model demonstrates excellent prediction accuracy with an average mean absolute percentage error of 5.2%. The constrained optimization method leads to 17.9% energy savings and 19.4% reduction in drying time.

Drying processes are among the most energy-consuming operations in industrial and manufacturing settings, demanding strategic selection, design, and control for enhanced efficiency. Advancing drying technologies is critical for improving sustainability, lowering energy use, reducing carbon emissions, and minimizing waste. This study explores two innovative strategies aimed at transforming drying processes into sustainable, low-carbon systems by reducing energy consumption, minimizing waste, and maintaining a strong emphasis on preserving product quality. The first strategy showcases a sub-pilot scale hybrid ultrasonic-convective dryer for agri-food products. This technology, powered by electricity (process electrification), integrates non-thermal ultrasonic dehydration with convective heating and is presented as a sustainable and energy-efficient solution that enhances eco-friendly practices. The second strategy involves introducing and implementing a novel, multi-objective, mixed integer dynamic optimization technique to determine the optimal time-dependent process parameter values for the drying operation. This optimization technique yields operating conditions that are piecewise constant in time aiming to maximize the energy efficiency of the hybrid ultrasonic-convective dryer while ensuring strict adherence to product quality constraints. By adopting the hybrid ultrasonic-convective dryer, a notable 35% improvement in energy efficiency was achieved compared to conventional hot-air drying systems for drying apple slices. The proposed optimization framework further enhanced energy efficiency by nearly 14% over the most efficient process on the identical testbed, under static operating conditions. The reported enhancements have been experimentally validated. Regarding drying time (thereby improving production yield), the developed hybrid ultrasonic-convective dryer demonstrates as much as a 41% reduction in total processing time, which is further optimized by an additional 10% using our proposed optimization framework. The research outcomes have profound implications for the design and operation of drying systems, encompassing crucial aspects such as process electrification, cost-effectiveness, energy savings, time efficiency, product yield, product quality, and process automation.

Recent developments in alternative drying techniques have significantly heightened interest in innovative technologies that improve the yield and quality of dried goods, enhance energy efficiency, and facilitate continuous monitoring of drying processes. Artificial intelligence (AI)-enabled optical sensing technologies have emerged as promising tools for smart and precise monitoring of food drying processes. Food industries can leverage AI-enabled optical sensing technologies to gain a comprehensive understanding of drying dynamics, optimize process parameters, identify potential issues, and ensure product consistency and quality. This review systematically discusses the application of selected optical sensing technologies, such as near-infrared (NIR) spectroscopy, hyperspectral imaging, and conventional imaging (i.e., computer vision) powered by AI. After covering the basics of optical sensing technologies for smart drying and an overview of different drying methods, it explores various optical sensing techniques for monitoring and quality control of drying processes. Additionally, the review addresses the limitations of these optical sensing technologies and their prospects in smart drying.

Albumen, primarily composed of ovalbumin, is a vital, nutrient-rich ingredient in the food industry. Drying is a critical step in low-water-activity albumen powder production, allowing extended shelf-life and reduced costs in handling, transportation, and storage of albumen products. Traditional drying methods, such as spray drying (SD) and hot air drying (HAD), often degrade albumen. This study explores variable frequency contact ultrasonic drying (CUD) as a novel and green alternative, operating at a central frequency of 20 kHz with sound amplitudes of 0 %, 40 %, and 60 %, and temperatures of 40 degrees C and 60 degrees C. The drying kinetics, physical, and foaming properties of CUD-dried albumen proteins were compared with those of hot-air-, spray-, and freeze-dried (FD) samples. Compared to HAD, CUD significantly enhanced the drying process, as evidenced by a 240 % increase in effective moisture diffusivity, a 66-78 % reduction in activation energy (Ea), and a 27 % reduction in drying time. Moreover, CUD maintained higher protein integrity, evident from a 24-35 % decrease in enthalpies, more beta-turn and random coil structures, and increased free sulfhydryl groups. Notably, CUD at 40 degrees C significantly improved foaming capacity by 88 %, and at 60 degrees C, it enhanced foaming stability by 34 %, outperforming other drying methods. Protein solubility of CUD-albumen was improved by 10-12 % compared to HAD and was slightly better than FD. CUD-albumen showed a brighter color with a 26 % lower browning index than the HAD samples. Overall, CUD emerges as an effective and sustainable method for drying high-protein materials, ensuring high-quality albumen powders.

Cellulose nanocrystals (CNCs) have garnered increased attention due to their renewable nature, abundant feedstock availbility, and good mechanical properties. However, one of the bottlenecks for its commercial production is the drying process. Because of the low CNC concentrations in suspension after isolation, CNC drying requires the removal of a large amount of water to obtain dry products for the following utilization and saving shipping costs. A novel multi-frequency, multimode, modulated ultrasonic drying technology was developed for CNC drying to improve product quality, reduce energy consumption, and increase production rate. CNCs dried with different drying technologies were characterized by Fourier transform infrared (FT-IR) spectra analysis, X-ray diffraction (XRD) analysis, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and redispersibility to measure the quality and property changes. Under the same temperature and airflow rate, ultrasonic drying enhanced drying rates, resulting in at least a 50% reduction in drying time compared to hot air drying. The mean particle sizes of CNC from ultrasonic drying changed little with settling time, indicating good redispersibility. In addition, ultrasonic dried CNCs exhibited good stability in aqueous solutions, with the zeta potentials ranging from –35 to –65 mV. Specific energy consumption and CO2 emissions of various CNC drying technologies were evaluated. Energy consumption of ultrasonic drying is significantly reduced compared to other drying technologies. Moreover, the potential CO2 emissions of the fully electrified ultrasonic drying could be net zero if renewable electricity is used.

The industrial drying process consumes approximately 12% of the total energy used in manufacturing, with the potential for a 40% reduction in energy usage through improved process controls and the development of new drying technologies. To achieve cost-efficient and high-performing drying, multiple drying technologies can be combined in a modular fashion with optimal sequencing and control parameters for each. This paper presents a mathematical formulation of this optimization problem and proposes a framework based on the Maximum Entropy Principle (MEP) to simultaneously solve for both optimal values of control parameters and optimal sequence. The proposed algorithm addresses the combinatorial optimization problem with a non-convex cost function riddled with multiple poor local minima. Simulation results on drying distillers dried grain (DDG) products show up to 12% improvement in energy consumption compared to the most efficient single-stage drying process. The proposed algorithm converges to local minima and is designed heuristically to reach the global minimum.

Direct-contact ultrasonic drying is a novel approach to dehydrate fruits and vegetables to reduce microbial growth and post-harvest loss while preserving nutrients and the quality of the final product. Moisture content is a critical component for food behavior during drying, and its accurate evaluation in real-time is essential for food quality control. This study conveys the potential implementation of portable near-infrared spectroscopy (NIRS) combined with multivariate analysis for real-time assessment of moisture content in apple slices during direct-contact ultrasonic drying. Partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed, and their performances for different pre-treatments methods and data partitioning algorithms were evaluated with both internal cross-validation and an external dataset. Three wavelengths were selected by SPA (1359, 1517, and 1594 nm) which were then used to introduce a closed-form equation for moisture content prediction with R2p = 0.99 and RMSEP = 3.32%. The results revealed that portable NIRS combined with multivariate analysis is quite promising for monitoring and evaluating the moisture content during ultrasonic drying.

DDG is a major source of protein, calcium, phosphorus, and sulfur is arguably the most important byproduct of the bioethanol industry with increasing demand over the past few years. Reducing energy consumption in the DDG production process and energy recovery from DDG is vital for sustainable bioethanol productions. In this paper, a novel direct-contact multi-frequency, multimode, and modulated (MMM) ultrasonic dryer (US) was developed for the first time and has been applied in dehydration of wet distillers’ grain (WDG). Ultrasonic drying (US) was combined with a convective airflow (HA) at different temperatures of 25 (room temperature), 50 and 70 °C to evaluate the impact of US, HA, and US + HA on drying kinetics, activation energy, chemical compositions, microstructure, and color of DDG. Semi-empirical kinetic models were developed and evaluating drying performances showed that the application of ultrasound significantly enhanced the drying rate and decreased the drying time (by 46%), especially at low drying temperatures. The activation energy for moisture removal in the presence of ultrasound was about 50% of that without ultrasound. The final dried distillers' grains product processed by ultrasonic drying had a brighter color, a higher available protein, a higher digestible protein (the lowest acid detergent insoluble crude protein), and a better surface profile with no compromise on minerals and fiber contents.

Fruits and vegetable powders are gaining attention due to their flavor, color, high nutritional content, and consumers’ demand for compact and lightweight foods. This study was undertaken to explore their commercial applications as an edible coating onto sliced apples to incorporate various functional and nutritional characteristics to apple chips. The subsequent aim of this work was to investigate miniature NIR spectroscopy as a tool to rapidly monitor and develop a predictive model for the drying of edible coating on these apple slices. The apple slices coated with selected fruit powders were dried and compared with uncoated samples. NIR spectra were collected at different drying times, and multivariate calibration models were developed using partial least-squares regression (PLSR) with raw and various pre-treated spectra. Instead of selecting different sets of feature wavelengths for coated and uncoated apple slices, a set of 7 key wavelengths was selected for convenient application to monitor moisture content during drying of apples with or without edible coatings. The results showed that the miniature NIR spectroscopy was able to monitor the drying process and discriminate between the coated and uncoated apple slices and drying times, primarily by the differences in sugar and water absorption bands.

Portable near-infrared spectrometer in the spectral range of 900-1700 nm was evaluated for the first time to assess and monitor apple hardness in real-time during ultrasonic drying. Calibration models were developed using PLS and ANN, and their performances were evaluated by internal leave-one-out cross-validation and an external dataset. Several pre-treatments including standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky-Golay first and second derivatives were employed to examine the effects of spectral variations in hardness prediction. Seven important wavelengths were selected using weighted regression coefficients to develop a simple MLR model to facilitate the model interpretation and circumvent noise. The models using PLS, MLR, and ANN with selected wavelengths predicted the apple hardness with R-p(2) of 0.91, 0.91, 0.95, and RMSEP of 14.78, 14.85, and 12.46 N, respectively. The results indicate that portable NIR spectrometers are quite promising for real-time monitoring of apple hardness during ultrasonic drying.

Drying is one of the most prevalent methods to reduce water activity and preserve foods. However, it is also the most energy-intensive food processing unit operation. Although a number of drying methods have been proposed and tested for the purpose of achieving a time- and energy-efficient drying process, almost all current drying methods still rely on thermal energy to remove moisture from the product. In this study, a novel use of power ultrasound was explored for drying of apple slices without the application of heat. The non-thermal ultrasound contact drying (US-CD) was performed in the presence of an air stream (26–40 °C) flowing over product surface to remove mist or vapor produced by the ultrasound treatment. The effects of the non-thermal US-CD, hot-air drying (HAD), and freeze drying (FD) on the changes in rehydration ratio, pH, titratable acidity, water activity, color, glass transition temperature, texture, antioxidant capacity, total phenols, and microstructures of the samples were evaluated. The moisture content of the apple slices reached below 5% (w.b.) after 75–80 min of US-CD, which was about 45% less than that of the HAD method. The antioxidant capacity and total phenol contents of the US-CD samples were significantly higher than that of the AD samples. The non-thermal ultrasonic contact drying is a promising method which has the potential to significantly reduce drying time and improve product quality.

This paper studies a steady two-dimensional stagnation-point flow of nanofluids over a nonlinearly stretching/shrinking sheet in the presence of blowing/suction. The effects of different nanoparticle materials, namely copper, alumina and titania on the flow and heat transfer rate are investigated. Employing similarity variables, the governing partial differential equations including continuity, momentum and energy have been reduced to ordinary equations and are solved numerically via Runge-Kutta-Fehlberg scheme. It is shown that two solutions exist for shrinking sheets in both blowing and impermeable cases, while an additional solution appears in the case of suction (there are three solutions). Moreover, the effects of nonlinearly parameter β, blowing/suction S, and solid volume fraction ϕ on the heat and fluid flow characteristics are investigated in details.

This is a theoretical investigation on fully developed mixed convective flow of nanofluids inside microtubes subjected to a constant wall temperature (CWT). The modified Buongiorno model is used for the nanofluids which fully accounts for the distribution of nanoparticles concentration on thermophysical properties. The effect of nanoparticles migration originating from the nano-scale diffusivities including thermophoretic diffusion (temperature-gradient driven force) and Brownian diffusion (concentrationgradient driven force) on the thermophysical characteristics of nanofluids has been considered. A Navier's slip condition is considered at the wall to model the non-equilibrium region at the fluid-solid interface in micro-scale channels. A scale analysis is performed to estimate the relative significance of the pertaining parameters that should be included in the governing equations. The effects of pertinent parameters including the ratio of Brownian motion to thermophoresis (N-BT), slip parameter (k), mixed convective parameter (Nr), and bulk mean nanoparticle volume fraction (phi B) on the flow and thermal fields are investigated. The figure of merit (FoM) is used to measure the thermal performance of equipment and finding the optimum thermal condition. It is shown that increasing the buoyancy force would enhance the heat transfer rate, especially for the larger nanoparticles. Also, larger nanoparticles enhance the thermal performance based on a required heat transfer rate with the lowest penalty in the pressure drop. (C) 2016 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.

This is a numerical investigation of nanoparticle transport effect on magnetohydrodynamic mixed convective heat transfer of electrically conductive nanofluids in micro-annuli with temperature-dependent thermophysical properties. The modified Buongiorno's non-homogeneous model is applied for the nanoparticle-fluid suspension to simulate the migration of nanoparticles into the base fluid, originating from the thermophoresis (nanoparticle migration because of temperature gradient) and Brownian motion (nanoparticle slip velocity because of concentration gradient). Due to surface roughness at the solid–fluid interface in micro-annuli, the wall surfaces are subjected to a linear slip condition to assess the non-equilibrium region near the interface. The fluid flow has been assumed to be fully developed, and the governing equations including continuity, momentum, energy, and nanoparticle transport equation are reduced to a system of ordinary differential equations, before they have been solved numerically. The results are presented with and without considering the dependency of thermophysical properties upon the temperature. It is indicated that ignoring the temperature dependency of thermophysical properties does not significantly affect the flow fields and heat transfer behavior of nanofluids, but it changes the relative magnitudes. Furthermore, in the presence of magnetic field, smaller nanoparticles are more appropriate than larger ones.

Laminar fully developed mixed convection of magnetohydrodynamic nanofluids inside microtubes at a constant wall temperature (CWT) under the effects of a variable directional magnetic field is investigated numerically. Nanoparticles are assumed to have slip velocities relative to the base fluid owing to thermophoretic diffusion (temperature gradient driven force) and Brownian diffusion (concentration gradient driven force). The no-slip boundary condition is avoided at the fluid-solid mixture to assess the non-equilibrium region at the fluid-solid interface. A scale analysis is performed to estimate the relative significance of the pertaining parameters that should be included in the governing equations. After the effects of pertinent parameters on the pressure loss and heat transfer enhancement were considered, the figure of merit (FoM) is employed to evaluate and optimize the thermal performance of heat exchange equipment. The results indicate the optimum thermal performance is obtained when the thermophoresis overwhelms the Brownian diffusion, which is for larger nanoparticles. This enhancement boosts when the buoyancy force increases. In addition, increasing the magnetic field strength and slippage at the fluid-solid interface enhances the thermal performance.

A parallel computational tool based on solving the full two-dimensional Navier-Stokes equations was developed to predict the behavior of two types of wave energy converters (WECs). The two WECs, a point absorber and a submerged terminator are subjected to nonlinear incident waves which are generated by different types of wave makers in a water tank. The governing equations are solved on a regular structured grid to resolve the flow field. The solution is obtained using a control volume approach in conjunction with the immersed boundary method for treating the interactions of the solid objects with the fluid flow. The interaction between two fluid flow is determined by the Volume-of-fluid (VOF) method. A two-step projection method along with Multi-Processing (OpenMP) is employed to solve the flow equations. To validate the model, the numerical results are compared with the available numerical and experimental data in various scenarios where good agreements are observed. Two types of wave maker, a piston and a flap device, are considered to generate waves in a water tank. Then, two types of WECs, a tethered circular cylinder and a bottom-hinged flap device, are tested in the water tank to predict motion, power output and efficiency of these two devices with the steep incident wave.

In this paper, the modified two-component non-homogeneous mixture model of Buongiorno is developed for the case of forced convection of alumina-water non-homogeneous nanofluid flow in concentric micro-annular tubes at constant wall temperature (CWT). Two different thermal boundary conditions have been considered such that for Case A the inner wall is adiabatic and the outer wall is kept at a constant temperature while for Case B the inner wall temperature remains constant and the outer wall is thermally isolated. Assuming a hydrodynamically and thermally fully developed flow, the governing equations of nanofluids in a concentric annulus are reduced to a nonlinear system of ordinary differential equations and solved using an appropriate reciprocal numerical algorithm via Runge-Kutta-Fehlberg method. The effects of N-BT (from 0.7 to 10), phi(B) (from 0.01 to 0.03), lambda (from 0.05 to 0.2) and zeta (0.4, 0.5 and 0.6) on the non-dimensional volume fraction of nanoparticles, velocity, and temperature profiles have been investigated for both cases. It is indicated that the anomalous heat transfer enhancement depends on the thermal boundary condition as well as the ratio of thermophoresis and Brownian motion. Furthermore, for Case B, there is an optimum nanoparticle diameter around 0.5 < N-BT < 1 that the thermal performance reaches its peak. However, for Case A, the thermal performance increases as the nanoparticle diameter increases. For both cases, the thermal performance decreases with an increase in the nanoparticle concentration. (C) 2017 Published by Elsevier Masson SAS.