Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks.

In this study, two empirical correlations of the Nusselt number, based on two artificial neural networks (ANN), were developed to determine the heat transfer coefficients for each section of a vertical helical double-pipe evaporator with water as the working fluid. Each ANN was obtained using an experimental database of 1109 values obtained from an evaporator coupled to an absorption heat transformer with energy recycling. The Nusselt number in the annular section was estimated based on the modified Wilson plot method solved by an ANN. This model included the Reynolds and Prandtl numbers as input variables and three neurons in their hidden layer. The Nusselt number in the inner section was estimated based on the Rohsenow equation, solved by an ANN. This ANN model included the numbers of the Prandtl and Jackob liquids as input variables and one neuron in their hidden layer. The coefficients of determination were R 2 > 0.99 for both models. Both ANN models satisfied the dimensionless condition of the Nusselt number. The Levenberg-Marquardt algorithm was chosen to determine the optimum values of the weights and biases. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and the linear function in the output layer. The Nusselt numbers, determined by the ANNs, proved adequate to predict the values of the heat transfer coefficients of a vertical helical double-pipe evaporator that considered biphasic flow with an accuracy of ±0.2 for the annular Nusselt and ±4 for the inner Nusselt.

Key Clinical Factors Predicting Adipokine and Oxidative Stress Marker Concentrations among Normal, Overweight and Obese Pregnant Women Using Artificial Neural Networks.

Maternal obesity has been related to adverse neonatal outcomes and fetal programming. Oxidative stress and adipokines are potential biomarkers in such pregnancies; thus, the measurement of these molecules has been considered critical. Therefore, we developed artificial neural network (ANN) models based on maternal weight status and clinical data to predict reliable maternal blood concentrations of these biomarkers at the end of pregnancy. Adipokines (adiponectin, leptin, and resistin), and DNA, lipid and protein oxidative markers (8-oxo-2'-deoxyguanosine, malondialdehyde and carbonylated proteins, respectively) were assessed in blood of normal weight, overweight and obese women in the third trimester of pregnancy. A Back-propagation algorithm was used to train ANN models with four input variables (age, pre-gestational body mass index (p-BMI), weight status and gestational age). ANN models were able to accurately predict all biomarkers with regression coefficients greater than R² = 0.945. P-BMI was the most significant variable for estimating adiponectin and carbonylated proteins concentrations (37%), while gestational age was the most relevant variable to predict resistin and malondialdehyde (34%). Age, gestational age and p-BMI had the same significance for leptin values. Finally, for 8-oxo-2'-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care.

Modeling and validation of heat and mass transfer in individual coffee beans during the coffee roasting process using computational fluid dynamics (CFD).

Heat and mass transfer in individual coffee beans during roasting were simulated using computational fluid dynamics (CFD). Numerical equations for heat and mass transfer inside the coffee bean were solved using the finite volume technique in the commercial CFD code Fluent; the software was complemented with specific user-defined functions (UDFs). To experimentally validate the numerical model, a single coffee bean was placed in a cylindrical glass tube and roasted by a hot air flow, using the identical geometrical 3D configuration and hot air flow conditions as the ones used for numerical simulations. Temperature and humidity calculations obtained with the model were compared with experimental data. The model predicts the actual process quite accurately and represents a useful approach to monitor the coffee roasting process in real time. It provides valuable information on time-resolved process variables that are otherwise difficult to obtain experimentally, but critical to a better understanding of the coffee roasting process at the individual bean level. This includes variables such as time-resolved 3D profiles of bean temperature and moisture content, and temperature profiles of the roasting air in the vicinity of the coffee bean.