Evaluation of exergy performance and onion drying properties in a multi-stage semi-industrial continuous dryer: Artificial neural networks (ANNs) and ANFIS models

Analysis and control of hybrid convection-radiation drying systems toward energy saving strategy

Food drying is a vital technology in agricultural preservation, where hybrid drying provides efficient moisture removal. However, the transient behavior of the moisture ratio ( MR ${\mathrm{MR}}$ ) and associated drying rate ( DR ${\mathrm{DR}}$ ) could increase energy consumption if not managed with an effective energy-saving strategy. This study investigates the impacts of hybrid convection-radiation drying on MR ${\mathrm{MR}}$ , DR ${\mathrm{DR}}$ , drying time, and energy consumption. Unlike previous studies, which have not fully addressed the connections between operating conditions and drying kinetics, this work provides new insights into these relationships. Additionally, an artificial neural network (ANN) control model is applied to optimize energy consumption, offering a new approach to improving the efficiency of the drying process. During the experiment, the airflow velocity was varied from 0.7 m/s to 1.5 m/s, the airflow temperature was varied from 40°C to 60°C, and the radiation intensity was varied from 1500 W/m2 to 3000 W/m2. The results showed that the transient behavior exhibited four data groups with consistent MR ${\mathrm{MR}}$ and DR ${\mathrm{DR}}$ through the mean and statistical analysis. Increasing radiation intensity and air temperature has decreased the drying time, while higher airflow has increased the drying time. The energy indices were enhanced by increasing radiation intensity and temperature while reducing airflow velocity. The measured MR ${\mathrm{MR}}$ of all groups exhibited similar kinetics behavior, while the associated DR ${\mathrm{DR}}$ exhibited similar clustering through the self-organizing map. Those findings were further controlled using an ANN model with 99% predicting accuracy. With airborne heating at 60°C and airflow at 0.7 m/s, the radiation intensity is transiently controlled, showing a 6.5% drying time reduction and a 36% energy saving. Thus, controlling the radiation intensity and its impact on the slice properties is highly desirable in future work. This work could help designers improve the processing efficiency and energy conservation of garlic slices drying.

Achieving sustainability in heat drying processing: Leveraging artificial intelligence to maintain food quality and minimize carbon footprint

The food industry is a significant contributor to carbon emissions, impacting carbon footprint (CF), specifically during the heat drying process. Conventional heat drying processes need high energy and diminish the nutritional value and sensory quality of food. Therefore, this study aimed to investigate the integration of artificial intelligence (AI) in food processing to enhance quality and reduce CF, with a focus on heat drying, a high energy-consuming method, and offer a promising avenue for the industry to be consistent with sustainable development goals. Our finding shows that AI can maintain food quality, including nutritional and sensory properties of dried products. It determines the optimal drying temperature for improving energy efficiency, yield, and life cycle cost. In addition, dataset training is one of the key challenges in AI applications for food drying. AI needs a vast and high-quality dataset that directly impacts the performance and capabilities of AI models to optimize and automate food drying.

Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

BACKGROUND

Five computational intelligence approaches, namely Gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT) and support vector machine (SVM), were used to describe the evolution of moisture during the dehydration process of glutinous rice. The hyperparameters of the models were optimized with three strategies: Bayesian optimization, grid search and random search. To understand the parameters that facilitate intelligence model adaptation to the dehydration process, global sensitivity analysis (GSA) was used to compute the impact of the input variables on the model output.

RESULT

The result shows that the optimum computational intelligence techniques include the 3-9-1 topology trained with Bayesian regulation function for ANN, Gaussian kernel function for SVM, Matérn covariance function combined with zero mean function for GPR, boosting method for EoT and 4 minimum leaf size for DT. GPR has the highest performance with R2 of 100% and 99.71% during calibration and testing of the model, respectively. GSA reveals that all the models significantly rely on the variation in time as the main factor that affects the model outputs.

CONCLUSION

Therefore, the computational intelligence models, especially GPR, can be applied for an effective description of moisture evolution during small-scale and industrial dehydration of glutinous rice. © 2024 Society of Chemical Industry.

Sustainable seedling pots: Development and characterisation of banana waste and natural fibre-reinforced composites for horticultural applications

Plastic pots used in horticultural nurseries generate substantial waste, causing environmental pollution. This study aimed to develop biodegradable composites from banana pseudo-stem reinforced with agricultural residues like pineapple leaves, taro and water hyacinth as eco-friendly substitutes. The aim of this study is to develop optimised banana biocomposite formulations with suitable reinforcements that balance mechanical durability, biodegradation, and seedling growth promotion properties to serve as viable eco-friendly alternatives to plastic seedling pots. This study was carried out by fabricating banana fibre mats through pulping, drying and hot pressing. Composite sheets were reinforced with 50 % pineapple, taro or water hyacinth fibres. The mechanical properties (tensile, yield strength, elongation, bursting strength), hydrophilicity (contact angle, water absorption), biodegradability (soil burial test), and seedling growth promotion were evaluated through appropriate testing methods. The results show that banana-taro composites exhibited suitable tensile strength (25 MPa), elongation (27 %), water uptake (41 %) and 82 % biodegradation in 60 days. It was observed that biodegradable seedling trays fabricated from banana-taro composite showed 95 % tomato seed germination and a 125 cm plant height increase in 30 days, superior to plastic trays. The finding shows that the study demonstrates the potential of banana-taro biocomposites as alternatives to plastic nursery pots, enabling healthy seedling growth while eliminating plastic waste pollution through biodegradation.

ANFIS and ANN models to predict heliostat tracking errors

The efficiency and performance of solar tower power are greatly influenced by the heliostats field. To ensure accurate tracking of reflectors often requires an evaluation of the beam reflected positions. This operation is costly time-consuming due to the number of heliostats. It is also necessary to set up a fast and less expensive method able to evaluate tracking heliostat. In this paper, prediction models based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) were applied to estimate rapidly and accurately heliostat error tracking. The modeling is based on the experimental data of seven different days. The input parameters are time and day number and the output is the beam reflected position following the altitude and azimuth axes. Both techniques have been able to predict the beam reflected position. A comparison of results showed that intelligent methods recorded better performance than conventional model based on geometric errors. For ANFIS model, coefficients of correlation (R²) of 0.97 is obtained compared to that of the ANN, 0.96 and 0.92 for altitude and azimuth axes respectively. The intelligent methods may be a promising alternative for predicting heliostat beam reflected the position.

Prediction of Almond Nut Yield and Its Greenhouse Gases Emission Using Different Methodologies

The evaluation of a production system to analyze greenhouse gases is one of the most interesting challenges for researchers. The aim of the present study is to model almond nut production based on inputs by employing artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) procedures. To predict the almond nut yield with respect to the energy inputs, several ANN and ANFIS models were developed, evaluated, and compared. Among the several developed ANNs, a network with an architecture of 8-12-1 and a log-sigmoid, and a linear transfer function in the hidden and output layers, respectively, is found to be the best model. In general, both approaches had a good capability for predicting the nut yield. The comparison results revealed that the ANN procedure could predict the nut yield more precisely than the ANFIS models. Furthermore, greenhouse gas (GHG) emissions in almond orchards are determined where the total GHG emission is estimated to be about 2348.85 kg CO2eq ha−1. Among the inputs, electricity had the largest contribution to GHG emissions, with a share of 72.32%.

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Drying as an effective method for preservation of crop products is affected by various conditions and to obtain optimum drying conditions it is needed to be evaluated using modeling techniques. In this study, an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR) was used for modeling the infrared-hot air (IR-HA) drying kinetics of parboiled hull. The ANFIS, ANN, and SVR were fed with 3 inputs of drying time (0–80 min), drying temperature (40, 50, and 60 °C), and two levels of IR power (0.32 and 0.49 W/cm2) for the prediction of moisture ratio (MR). After applying different models, several performance prediction indices, i.e., correlation coefficient (R2), mean square error index (MSE), and mean absolute error (MAE) were examined to select the best prediction and evaluation model. The results disclosed that higher inlet air temperature and IR power reduced the drying time. MSE values for the ANN, ANFIS tests, and SVR training were 0.0059, 0.0036, and 0.0004, respectively. These results indicate the high-performance capacity of machine learning methods and artificial intelligence to predict the MR in the drying process. According to the results obtained from the comparison of the three models, the SVR method showed better performance than the ANN and ANFIS methods due to its higher R2 and lower MSE.

Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics

Pumpkin seeds were dried in a vibro-fluidized bed dryer (VFBD) at different temperatures at optimized vibration intensity of 4.26 and 4 m/s air velocity. The drying characteristics were mapped employing semiempirical models and Artificial Neural Network (ANN). Prediction of drying behavior of pumpkin seeds was done using semiempirical models, of which, one was preferred as it indicated the best statistical indicators. Two-term model showed the best fit of data with R2 − 0.999, and lowest χ2 − 1.03 × 10−4 and MSE 7.55 × 10−5. A feedforward backpropagation ANN model was trained by the Levenberg–Marquardt training algorithm using a TANSIGMOID activation function with 2-10-2 topology. Performance assessment of ANN showed better prediction of drying behavior with R2 = 0.9967 and MSE = 5.21 × 10−5 for moisture content, and R2 = 0.9963 and MSE = 2.42 × 10−5 for moisture ratio than mathematical models. In general, the prediction of drying kinetics and other drying parameters was more precise in the ANN technique as compared to semiempirical models. The diffusion coefficient, Biot number, and hm increased from 1.12 × 10−9 ± 3.62 × 10−10 to 1.98 × 10−9 ± 4.61 × 10−10 m2/s, 0.51 ± 0.01 to 0.60 ± 0.01, and 1.49 × 10−7 ± 4.89 × 10−8 to 3.10 × 10−7 ± 7.13 × 10−8 m/s, respectively, as temperature elevated from 40 to 60°C. Arrhenius’s equation was used to the obtain the activation energy of 32.71 ± 1.05 kJ/mol.

Extraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs)

This research study focuses on the evaluation of the total phenolic compounds (TPC) and antioxidant activity (AOA) of strawberries according to different experimental extraction conditions by applying the Artificial Neural Networks (ANNs) technique. The experimental data were applied to train ANNs using feed- and cascade-forward backpropagation models with Levenberg-Marquardt (LM) and Bayesian Regulation (BR) algorithms. Three independent variables (solvent concentration, volume/mass ratio and extraction time) were used as ANN inputs, whereas the three variables of total phenolic compounds, DPPH and ABTS antioxidant activities were considered as ANN outputs. The results demonstrate that the best cascade- and feed-forward backpropagation topologies of ANNs for the prediction of total phenolic compounds and DPPH and ABTS antioxidant activity factors were the 3-9-1, 3-4-4-1 and 3-13-10-1 structures, with the training algorithms of trainlm, trainbr, trainlm and threshold functions of tansig-purelin, tansig-tansig-tansig and purelin-tansig-tansig, respectively. The best R² values for the predication of total phenolic compounds and DPPH and ABTS antioxidant activity factors were 0.9806 (MSE = 0.0047), 0.9651 (MSE = 0.0035) and 0.9756 (MSE = 0.00286), respectively. According to the comparison of ANNs, the results showed that the cascade-forward backpropagation network showed better performance than the feed-forward backpropagation network for predicting the TPC, and the FFBP network, in predicting the DPPH and ABTS antioxidant activity factors, had more precision than the cascade-forward backpropagation network. The ANN technique is a potential method for estimating targeted total phenolic compounds and the antioxidant activity of strawberries.

Thermodynamic Evaluation of the Forced Convective Hybrid-Solar Dryer during Drying Process of Rosemary (Rosmarinus officinalis L.) Leaves

This study aimed to examine the energy and exergy indices of the rosemary drying process in a hybrid-solar dryer (HSD) and the effects of air-drying parameters on these thermodynamic indices. Drying experiments were carried out at four levels of air temperature (40, 50, 60, and 70 ∘C) and three levels of air velocity (1, 1.5, and 2 m/s). Energy and exergy were calculated by application of the first and second laws of thermodynamics. Based on the principal laws, energy efficiency, exergy losses, and exergetic improvement potential rate, were evaluated. The results showed that the energy utilization ratio (EUR) ranged from 0.246 to 0.502, and energy utilization (EU) ranged from 0.017 to 0.060 (kJ/s). Exergy loss and efficiency varied from 0.009 to 0.028 (kJ/s) and from 35.08% to 78.5%, respectively, and increased with increased temperature and air velocity. It was found that the exergy loss rate was affected by temperature and air velocity because the overall heat transfer coefficient was different under these conditions. By comparison, with increasing temperature and air velocity, the exergy efficiency increased. Because most energy is used to evaporate moisture, this behavior may be explained by improved energy utilization. The drying chamber sustainability index ranged from 0.0129 to 0.0293. This study provides insights into the optimization process of drying operations and operational parameters in solar hybrid dryers that reduce energy losses and consumption.

Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms

The study targeted towards drying of cantaloupe slices with various thicknesses in a microwave dryer. The experiments were carried out at three microwave powers of 180, 360, and 540 W and three thicknesses of 2, 4, and 6 mm for cantaloupe drying, and the weight variations were determined. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were exploited to investigate energy and exergy indices of cantaloupe drying using various afore-mentioned input parameters. The results indicated that a rise in microwave power and a decline in sample thickness can significantly decrease the specific energy consumption (SEC), energy loss, exergy loss, and improvement potential (probability level of 5%). The mean SEC, energy efficiency, energy loss, thermal efficiency, dryer efficiency, exergy efficiency, exergy loss, improvement potential, and sustainability index ranged in 10.48–25.92 MJ/kg water, 16.11–47.24%, 2.65–11.24 MJ/kg water, 7.02–36.46%, 12.36–42.70%, 11.25–38.89%, 3–12.2 MJ/kg water, 1.88–10.83 MJ/kg water, and 1.12–1.63, respectively. Based on the results, the use of higher microwave powers for drying thinner samples can improve the thermodynamic performance of the process. The ANFIS model offers a more accurate forecast of energy and exergy indices of cantaloupe drying compare to ANN model.

Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling

Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) were studied. In the analysis of the performance of our approach on moisture ratio (MR) of apple slices, artificial neural networks (ANNs) was used to provide with a background for further discussion and evaluation. In order to evaluate the models mentioned in the literature, the Midilli et al. model was proper for dehydrating of apple slices in both MD and CD. The MD drying technology enhanced the drying rate when compared with CD drying significantly. Effective diffusivity (D_eff) of moisture in CD drying (1.95 × 10^-7-4.09 × 10^-7 m²/s) was found to be lower than that observed in MD (2.94 × 10^-7-8.21 × 10^-7 m²/s). The activation energy (Ea) values of CD drying and MD drying were 122.28-125 kJ/mol and 14.01-15.03 W/g respectively. The MD had the lowest specific energy consumption (SEC) as compared to CD drying methods. According to ANN results, the best R² values for prediction of MR in CD and MD were 0.9993 and 0.9991, respectively.