A DC glow discharge produced in N₂ gas can generate several species that are important in different applications, such as the modification of surface properties of materials. A low-pressure glow discharge apparatus was used for the the analysis of the Ar–N₂ mixture at a total pressure of 2.0 Torr, a power of 20 W and 40 l/min flow rate of gases. The emission bands were measured in the wavelength range of 200–1100 nm. The principal elements are N₂, N ₂⁺ and Ar I. The electron temperature was found in the range of 1.72–2.08 eV, and the ion density was in the order of 10¹⁰ cm⁻³.

The variations in the physical and chemical parameters involved in the degradation of dye Acid Black 52 (AB52) using plasma were analyzed. The plasma, or electrical discharge, was generated in the gas-liquid interface at atmospheric pressure using a dc power supply. The system configuration comprised two tungsten electrodes: the cathode is immersed in the solution while the anode is fixed above the surface with a gap of 5 mm, where the plasma is generated. The absorbance, total organic carbon, chemical oxygen demand, pH variation, electrical conductivity, optical emission spectrum, and the change in volume and temperature were measured for each sample as a function of treatment time.

This work characterizes the degradation of the textile dye azo Acid Black 52 by measuring several physical and chemical parameters. A corona plasma was created at atmospheric pressure and applied on the liquid-air interface of water samples containing the dye. 1.0 mM of ferrous sulfate (FeSO₄) was added to 1.0 mM dye solution, for a total volume of 250 mL. For each treatment, a number of parameters were quantified. These were voltage, current, temperature, loss of volume, pH, electrical conductivity, concentration, optical mission spectra, chemical oxygen demand (COD), total organic carbon (TOC), and the removal ratio. Because of the increase in the sample temperature, the volume lost by evaporation was explored. The results show that the efficiency of the dye degradation by plasma is a function of treatment time. Moreover, the reactive concentration of FeSO₄ and the exposition time of the plasma were varied at a constant volume, leading to the determination of the concentrations and optimal times. Considering the degradation and removal parameters, at the maximum treated time of 80 min, it found that COD was of 96.36%, TOC of 93.93%, and the removal ratio of 97.47%.

Ethanol plasma produced in low pressure with constant flux has been characterized in situ by optical emission spectroscopy (OES) and a Langmuir probe. From these methods, hydrogen and carbon monoxide are the main products from the ethanol decomposition in the plasma. In addition, C ₂ , OH, CH, O ⁺ ₂ , and H _α species were identified. The values obtained for electron density and temperatures were 1.26 × 10 ¹⁸ m ^-3 and 1.16 eV, respectively. The mass deposited (MD) on the electrode was analyzed by scanning electron microscopy and Fourier transform infrared spectroscopy (FT-IR) techniques. The morphology of the MD changes as a function of the discharge time from thin film to a cluster formation. The FT-IR results show that the main absorbance corresponds with C-H bonds, indicating that the mass deposit is formed mainly by this molecular fraction, which agrees with the CH species identified as the less abundant molecule in the plasma by OES. In addition, C≡C, O-H, C-H, C=C, and C-O bonds were identified.

This research presents the prediction of temperatures in the chamber of a solar dryer using artificial neural networks (ANN). The dryer is a forced-flow type and indirect. Climatic conditions, temperatures, airflow, and geometric parameters were considered to build the ANN model. The model was a feed-forward network trained using a backpropagation algorithm and Levenberg–Marquardt optimization. The configuration of the optimal neural network to carry out the verification and validation processes was nine neurons in the input layer, one in the output layer, and two hidden layers of thirteen and twelve neurons each (9-13-12-1). The percentage error of the predictive model was below 1%. The predictive model has been successfully tested, achieving a predictor with good capabilities. This consistency is reflected in the relative error between the predicted and experimental temperatures. The error is below 0.25% for the model’s verification and validation. Moreover, this model could be the basis for developing a powerful real-time operation optimization tool and the optimal design for indirect solar dryers to reduce cost and time in food-drying processes.