Eduardo Vyhmeister

Neural Networks and Support Vector Machine Models Applied to Energy Consumption Optimization in Semiautogeneous Grinding

Semiautogenous (SAG) mills for ore grinding are large energy consumption equipments. The SAG energy consumption is strongly related to the fill level of the mill. Hence, on-line information of the mill fill level is a relevant state variable to monitor and drive in SAG operations. Unfortunately, due to the prevailing conditions in a SAG mill, it is difficult to measure and represent from first principle model the state of the mill fill level. Alternative approaches to tackle this problem consist in designing appropriate datadriven models, such as Neural Networks (NN) and Support Vector Machine (SVM). In this paper, NN and a SVM (specifically a Least Square-SVM) are used as Nonlinear autoregressive with exogenous inputs (NARX) and Nonlinear autoregressive moving average with exogenous inputs (NARMAX) models for on-line estimation of the filling level of a SAG mill. Good performances of the developed models could allow implementation in SAG operation/control hence optimizing its energy consumption.

Combining Homogeneous Catalysis with Heterogeneous Separation using Tunable Solvent Systems

Tunable solvent systems couple homogeneous catalytic reactions to heterogeneous separations, thereby combining multiple unit operations into a single step and subsequently reducing waste generation and improving process economics. In addition, tunable solvents can require less energy than traditional separations, such as distillation. We extend the impact of such solvents by reporting on the application of two previously described carbon dioxide tunable solvent systems: polyethylene glycol (PEG)/organic tunable solvents (POTS) and organic/aqueous tunable solvents (OATS). In particular, we studied: (1) the palladium catalyzed carbon-oxygen coupling of 1-bromo-3,5-dimethylbenzene and o-cresol to potassium hydroxide to produce o-tolyl-3,5-xylyl ether and 1-bromo-3,5-di-tert-butylbenzene to potassium hydroxide to produce 3,5-di-tert-butylphenol in PEG400/1,4-dioxane/water and (2) the rhodium-catalyzed hydroformylation of p-methylstyrene in water/acetonitrile to form 2-(p-tolyl) propanal. In addition, we introduce a novel tunable solvent system based on a modified OATS where propane replaces carbon dioxide. This represents the first use of propane in a tunable solvent system.

Rotary Dryer Control Using a Grey-Box Neural Model Scheme

The application of a Grey-box Neural Model (GNM) in a nonlinear model predictive control scheme (NMPC) of a direct rotary dyer is presented in this work. The GNM, which is based on the combination of phenomenological models and empirical artificial neural network (ANN) models, was properly developed and validated by using experimental fish-meal rotary drying information. The GNM was created by combining the rotary dryer mass and energy balances and a feed forward neural network (FFNN), trained off-line to estimate the drying rate and the volumetric heat transfer coefficient. The GNM results allowed us to obtain the relation between the controlled variable (solid moisture content) and the manipulated variable (gas phase entrance temperature) used in the predictive control strategy. Two NMPC control strategies, one with a fixed extended prediction horizon and another with an extended range prediction horizon, were applied to a simulated industrial fish-meal drying process. The results showed that a correct rotary dryer representation can be obtained by using a GNM approach. Due to the representation capability of the GNM approach, excellent control performances of the NMPCs were observed when the process variables were subject to disturbances. As analyzed in this work, the fixed extended prediction horizon MPC surpassed recognized control methodologies (quadratic dynamic matrix control).

Identification of terpenes and essential oils by means of static headspace gas chromatography-ion mobility spectrometry

AbstractStatic headspace gas chromatography-ion mobility spectrometry (SHS GC-IMS) is a relatively new analytical technique that has considerable potential for analysis of volatile organic compounds (VOCs). In this study, SHS GC-IMS was used for the identification of the major terpene components of various essential oils (EOs). Based on the data obtained from 25 terpene standards and 50 EOs, a database for fingerprint identification of characteristic terpenes and EOs was generated utilizing SHS GC-IMS for authenticity testing of fragrances in foods, cosmetics, and personal care products. This database contains specific normalized IMS drift times and GC retention indices for 50 terpene components of EOs. Initially, the SHS GC-IMS parameters, e.g., drift gas and carrier gas flow rates, drift tube, and column temperatures, were evaluated to determine suitable operating conditions for terpene separation and identification. Gas chromatography-mass spectrometry (GC-MS) was used as a reference method for the identification of terpenes in EOs. The fingerprint pattern based on the normalized IMS drift times and retention indices of 50 terpenes is presented for 50 EOs. The applicability of the method was proven on examples of ten commercially available food, cosmetic, and personal care product samples. The results confirm the suitability of SHS GC-IMS as a powerful analytical technique for direct identification of terpene components in solid and liquid samples without any pretreatment. Graphical abstractFingerprint pattern identification of terpenes and essential oils using static headspace gas chromatography-ion mobility spectrometry.

In-Situ FTIR Kinetic Study in the Silylation of Low-k Films with Hexamethyldisilazane Dissolved in Supercritical CO2

In-situ Fourier transform infrared spectroscopy measurements were obtained by using an innovative equipment to study the heterogeneous reaction between a hydrolyzed porous methylsilsesquioxane film and hexamethyldisilazane (HMDS) dissolved in CO2 at supercritical conditions. Gas and solid infrared signatures were separated to obtain kinetic information of the heterogeneous reaction. A two-step reaction mechanism was observed: a fast first step controlled by kinetics and a second step controlled by the diffusion of the HMDS inside the porous material. Infrared information was used to derive a rate law expression of the silylation reaction between HMDS and Si-OH. A first order of reaction relative to the concentration of hydrophilic sites was observed with activation energy of 51.85 ± 1.25 kJ/mol.

Simulation and Process Optimization of a Membrane-Based Dense Gas Extraction Using Hollow Fiber Contactors

Supercritical fluid and membrane technology coupling is a relatively new concept applicable to solvent separation and solute extraction. In these processes a hydrophobic or hydrophilic macroporous membrane is used as a two-different-nature solutions contactor. This methodology is an alternative to conventional liquid solution supercritical fluid extraction processes, which are associated with high investment costs. In the present work, a membrane-based supercritical fluid extraction module is modeled, simulated, and optimized as an independent industrial-scale operational unit. UniSim design suite R390 software from Honeywell was used as the platform for the simulation. Acetone and ethanol literature extraction results and methanol experimental extraction results (27.6% to 14.5% with a 10 wt.% aqueous solution; 7.1% to 5.9% with a 500 ppm aqueous solution) were used for validation of the model and definition of the semi-empirical equation parameters. The generated industrial-scale system optimization, which used a modular membrane arrangement, was strongly dependent on thermodynamic, economic, and energetic variables (higher mass transfer resistance in the carbon dioxide phase increased the number of membranes needed; process feasibility was affected by the number of membrane units, carbon dioxide flow rate, and product added value; compression energy requirements affected the optimization result). The modeled system proved to be an important aid in the design, scaling, and optimization of systems that use membranes as phase contactors in liquid solution supercritical carbon dioxide extraction.

Study of Low-k Film Functionalization and Pore Sealing Using Chlorosilanes Dissolved in Supercritical Carbon Dioxide

Surface functionalization of hydrolyzed methyl-silsesquioxane films were performed by treatment of samples with trimethylchlorosilane (TMCS) and methyltrichlorosilane (MTCS) dissolved in supercritical carbon dioxide. Films thicknesses modifications, pore size distributions, hydrophobicity, dielectric constants, and chemical reaction analyses were performed by ellipsometry, ellipsometric porosimetry, goniometry, electrical measurements, and Fourier transform infrared spectroscopy, respectively. As results, the properties of the functionalized films were able to be modified in function of reaction conditions (concentration, temperature, and/or pressure). Layers thicker than a monolayer were deposited by both TMCS and MTCS, and a tradeoff between the surface functionalization and layer thickness for both chemicals was observed. The results led to the conclusion that a combination of reagents or processing steps could be used for surface properties tuning.