Elementos do caderno de especificações técnicas e do sistema de controle para a estruturação da Indicação de Procedência vinhos de altitude de Santa Catarina.

O Brasil possui diversas indicações geográficas (IG) de vinhos registradas ou em fase de estruturação. Dentre as IG registradas estão Vales da Uva Goethe - localizada no estado de Santa Catarina, Vale dos Vinhedos, Pinto Bandeira, Altos Montes, Monte Belo e Farroupilha - localizadas no estado do Rio Grande do Sul. Quatro indicações geográficas estão em fase de estruturação, incluindo: Vinhos de Altitude de Santa Catarina ? no Estado de Santa Catarina, Campanha Gaúcha e Altos de Pinto Bandeira ? no estado do Rio Grande do Sul, e Vale do São Francisco - localizada nos estados da Bahia e Pernambuco (Figura 12.1)

Dynamic modeling with experimental validation and control of a two-phase closed thermosyphon as heat supplier of a novel pilot-scale falling film distillation unit

Worldwide efforts in process intensification led to innovative designs for distillation, notably known as an energy-intensive process. Focusing on the boosting of thermal efficiency, our research team developed a novel pilot-scale thermosyphon-assisted falling film distillation apparatus. A network dynamic modeling is proposed to describe the thermal behavior of this new device, and the model is validated by dedicated experimental campaigns with the pilot-scale unit. The thermal network-based model was able to predict accurately the transient behavior and steady-state temperature of the two-phase closed thermosyphon. The experimental and predicted transfer coefficients showed reasonable agreement within the ±25% deviation band. A feedback control of the thermosyphon's evaporator temperature is performed in Simulink® to manage the steam chamber temperature. PID technique is adopted to achieve faster and smoother the control target, with the secondary effect to reduce mechanical stresses, increasing life cycle, and reducing energy consumption by 3.1%

Machine learning modeling and genetic algorithm-based optimization of a novel pilot-scale thermosyphon-assisted falling film distillation unit

Engaged in the global trend towards more energy-efficient and sustainable technologies, our research team has developed a falling film distillation apparatus with an innovative heat supply through a two-phase closed thermosyphon. In order to evaluate the performance of this energy-intensified distillation process, a supervised machine learning (ML) predictive model based on artificial neural networks is implemented for the separation of the ethanol–water binary mixture. The feed temperature, the evaporator temperature, and the feed flow rate are the three input variables of the model, whereas the ethanol mass fraction in the distillate, the distillate mass flow rate, the recovery factor, and the separation factor are the four performance indicators evaluated. The feed-forward ML has been trained, tested, and validated using a total dataset of 64 experimental runs carried out in the pilot-scale unit, covering a wide operating range of the input variables. Despite the high non-linearity, the ML approach was capable of modeling this new process accurately. The optimal topology of the ML model was achieved with a network arrangement of 10 neurons within 1 hidden layer (3:10:4), with a correlation coefficient (R) greater than 0.95 for all data. The predictive abilities of the ML model were harnessed to investigate the individual and synergistic interaction effects of the operating variables by plotting generalization graphs. Finally, the optimal operating conditions were evaluated by the genetic algorithm (GA) technique, being the feed temperature of 90.6 °C, the evaporator temperature of 109.6 °C, and the feed flow rate of 26.3 L/h, the process operating values that led the maximum of the four performance indicators, simultaneously. Under these operating conditions, a distillate mass flow rate of 4.9 kg/h, with 50.6%wt ethanol-enriched, a recovery factor of 84.9%, and a separation factor of 57.4 have been achieved, showing the high-performance feasibility of the pilot-scale unit

A Systematic Procedure to Develop a Capillary Electrophoresis Method Using a Minimal Experimental Data

The choice of an appropriate background electrolyte (BGE) and its components for capillary electrophoresis analysis is the main step in capillary electrophoresis method development. The use of an inadequate co-ion component could lead to asymmetrical peaks and selecting an inappropriate counter-ion could affect the buffer capacity and the pH of the BGE, leading to unreliable analysis. In this paper, we describe a systematic procedure for the development of a capillary electrophoresis method, based on the effect of varying pH on the ion effective mobility, to optimize the BGE composition. The method was applied to the separation of L-ascorbic acid in different samples. The optimized background electrolyte composition was 40 mmol L-1 tris(hydroxymethyl)aminomethane and 20 mmol L-1 2-morpholinoethanesulfonic acid, at pH 8.1. Sorbic acid was used as the internal standard and separation was carried out in a fused-silica capillary (32 cm total length and 8.5 cm effective length, 50 µm inner diameter), with a short-end-injection configuration and direct ultraviolet (UV) detection at 266 nm. The separation was performed in 26 s. The method shows good linearity (R2 > 0.999), excellent values for inter-day and intra-day precision and good recovery (in the range of 94-107%). The values obtained for limit of detection (LOD) and limit of quantification (LOQ) were 0.14 and 41 mg L-1, respectively. The systematic procedure applied shows to be a very useful tool for the first step method development for capillary electrophoresis