Claudio A. O. Nascimento

Neural network based approach for optimization of industrial chemical processes

Abstract Process optimization involves the minimization (or maximization) of an objective function, that can be established from a technical and/or economic viewpoint. In general, the decision variables are subject to constraints such as valid ranges (max and min limits) as well as constraints related to safety considerations and those that arise from the process model equations. Usually in chemical engineering problems, both the objective function and the constraints are non-linear. Computational methods of non-linear programming with constraints usually have to cope with problems such as numerical evaluation of derivatives (Jacobian, Hessian) and feasibility issues. The basic idea of the optimization method using neural network (NN) is to replace the model equations or plant data by an equivalent NN, and use this NN to carry on a grid search on the region of interest. As an additional benefit, the full mapping of the objective function allows one to identify multiple optima easily, an important feature not presented by conventional optimization methods. Moreover, the constraints are easily treated afterwards since the points with violated constraints can be recognized and classified (according to weak or hard constraints). This approach was applied in some industrial chemical process: the process of nylon-6,6 polymerization in a twin-screw extruder reactor and an acetic anhydride plant.

Modeling the kinetics of a photochemical water treatment process by means of artificial neural networks

Abstract We have investigated the kinetics of the degradation of 2,4-dimethyl aniline (2,4-xylidine), chosen as a model pollutant, by the photochemically enhanced Fenton reaction. This process, which may be efficiently applied to the treatment of industrial waste waters, involves a series of complex reactions leading eventually to the mineralization of the organic pollutant. A model based on artificial neural networks has been developed for fitting the experimental data obtained in a laboratory batch reactor. The model can describe the evolution of the pollutant concentration during irradiation time under various conditions. It has been used for simulating the behavior of the reaction system in sensitivity studies aimed at optimizing the amounts of reactants employed in the process — an iron(II) salt and hydrogen peroxide. The results show that the process is much more sensitive to the iron(II) salt concentration than to the hydrogen peroxide concentration, a favorable condition in terms of economic feasibility.

Intracellular biosynthesis and removal of copper nanoparticles by dead biomass of yeast isolated from the wastewater of a mine in the Brazilian Amazonia.

In this study was developed a natural process using a biological system for the biosynthesis of nanoparticles (NPs) and possible removal of copper from wastewater by dead biomass of the yeast Rhodotorula mucilaginosa. Dead and live biomass of Rhodotorula mucilaginosa was used to analyze the equilibrium and kinetics of copper biosorption by the yeast in function of the initial metal concentration, contact time, pH, temperature, agitation and inoculum volume. Dead biomass exhibited the highest biosorption capacity of copper, 26.2 mg g−1, which was achieved within 60 min of contact, at pH 5.0, temperature of 30°C, and agitation speed of 150 rpm. The equilibrium data were best described by the Langmuir isotherm and Kinetic analysis indicated a pseudo-second-order model. The average size, morphology and location of NPs biosynthesized by the yeast were determined by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and transmission electron microscopy (TEM). The shape of the intracellularly synthesized NPs was mainly spherical, with an average size of 10.5 nm. The X-ray photoelectron spectroscopy (XPS) analysis of the copper NPs confirmed the formation of metallic copper. The dead biomass of Rhodotorula mucilaginosa may be considered an efficiently bioprocess, being fast and low-cost to production of copper nanoparticles and also a probably nano-adsorbent of this metal ion in wastewater in bioremediation process.

Study of Atmospheric Ozone Formation by Means of a Neural Network-Based Model

The occurrence of high ozone levels in the atmosphere of urban areas has become a serious pollution problem in a number of large cities in the world. Although mathematical models have been proposed for predicting ozone concentrations as a function of a number of gas components, sometimes there are uncertainties due to lack of the combined effects of meteorological factors and the complex chemical reaction system involved. The application of neural network models, based on measured values of air pollutants and meteorological factors at different locations within the São Paulo Metropolitan Area, combine chemical and meteorological information. This has shown to be a promising tool for predicting ozone concentration. Simulations carried out with the model indicate the sensitivity of ozone in relation to different air pollution and weather conditions. Predictions using this model have shown good agreement with measured values of ozone concentrations.

Biosynthesis and uptake of copper nanoparticles by dead biomass of Hypocrea lixii isolated from the metal mine in the Brazilian Amazon Region.

A biological system for the biosynthesis of nanoparticles (NPs) and uptake of copper from wastewater, using dead biomass of Hypocrea lixii was analyzed and described for the first time. The equilibrium and kinetics investigation of the biosorption of copper onto dead, dried and live biomass of fungus were performed as a function of initial metal concentration, pH, temperature, agitation and inoculum volume. The high biosorption capacity was observed for dead biomass, completed within 60 min of contact, at pH 5.0, temperature of 40°C and agitation speed of 150 rpm with a maximum copper biosorption of 19.0 mg g−1. The equilibrium data were better described using the Langmuir isotherm and kinetic analysis indicated that copper biosorption follows a pseudo-second-order model. The average size, morphology and location of NPs biosynthesized by the fungus were determined by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and transmission electron microscopy (TEM). NPs were mainly spherical, with an average size of 24.5 nm, and were synthesized extracellularly. The X-ray diffraction (XRD) analysis confirms the presence of metallic copper particles. Infrared spectroscopy (FTIR) study revealed that the amide groups interact with the particles, which was accountable for the stability of NPs. This method further confirmed the presence of proteins as stabilizing and capping agents surrounding the copper NPs. These studies demonstrate that dead biomass of Hypocrea lixii provides an economic and technically feasible option for bioremediation of wastewater and is a potential candidate for industrial-scale production of copper NPs.

Nickel oxide nanoparticles film produced by dead biomass of filamentous fungus

The synthesis of nickel oxide nanoparticles in film form using dead biomass of the filamentous fungus Aspergillus aculeatus as reducing agent represents an environmentally friendly nanotechnological innovation. The optimal conditions and the capacity of dead biomass to uptake and produce nanoparticles were evaluated by analyzing the biosorption of nickel by the fungus. The structural characteristics of the film-forming nickel oxide nanoparticles were analyzed by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), and atomic force microscopy (AFM). These techniques showed that the nickel oxide nanoparticles had a size of about 5.89 nm and were involved in a protein matrix which probably permitted their organization in film form. The production and uptake of nickel oxide nanoparticles organized in film form by dead fungal biomass bring us closer to sustainable strategies for the biosynthesis of metal oxide nanoparticles.

Laser flash photolysis study of the photocatalytic step of the photo-fenton reaction in saline solution.

Abstract The photo-Fenton reaction (Fe2+/Fe3+, H2O2, UV light) is strongly inhibited by high concentrations of added chloride ion. In this work, the effect of added chloride ion on the photocatalytic step that converts Fe(III) back to Fe(II) is studied by nanosecond laser flash photolysis over a wide range of pH (1.0–3.3) and concentrations of Fe(III) (0.1–1.0 mM) and chloride ion (0.05–0.75 M). An explicit mechanistic model based on the preferential formation of the less-reactive Cl2·− radical anion via two routes (competitive photolysis of the iron(III)-chloride complex to chlorine atoms instead of the desired hydroxyl radical and pH-dependent scavenging of the hydroxyl radical by chloride ion) is proposed. This model, which fits the laser flash photolysis data for the production and decay of Cl2·− over the entire range of conditions investigated, suggests that inhibition of the photocatalytic step of the photo-Fenton process in the presence of chloride ion can be circumvented by maintaining the pH of the medium at or slightly above 3.0 throughout the reaction.

Neural network based approach for optimisation applied to an industrial nylon-6,6 polymerisation process

The basic idea of the proposed optimisation method is to replace the model equations by an equivalent neural network (NN) that mimics the phenomenological model, and use this NN to carry out a grid search, mapping all the region of interest. The proposed optimisation approach was applied to the industrial process of nylon-6,6 polymerisation in a twin-screw extruder reactor. This corresponds to the finishing stage of an industrial polymerisation plant. A qualitative optimisation procedure is used taking in account safe operation conditions, wear and tear of the equipment, product quality and energy consumption. The chosen operational variables are then checked with the phenomenological model. This approach provides more comprehensive information for the engineers analysis than the conventional non-linear programming procedure.

Ground-Level Ozone Mapping in Large Urban Areas Using Multivariate Statistical Analysis: Application to the São Paulo Metropolitan Area

Abstract A statistical study on the behavior of ground-level O3 concentration in different regions of a large urban area was carried out, with emphasis on pollutant gas concentrations and meteorological variables. The study was based on data generated by a network of measuring stations distributed throughout the São Paulo Metropolitan Area, in regions with different characteristics of traffic and economic activities. The combined application of principal component analysis and clustering techniques to data collected from 1997 until 2000 has led to the identification of implicit relationships between variables that have been associated with dominant processes related to O3 formation in different locations. Similarities between different regions of the city have also been detected and associated with local characteristics. The results indicate that the application of such statistical techniques to data collected in large urban areas enables the grouping of different regions according to their behavior in terms of O3 levels, as well as the identification of dominant processes in each group. These techniques are thus important in the planning of air pollution policies, especially in the case of O3 , a pollutant that is not directly related to pollution levels alone.

Photo-Fenton oxidation of phenol and organochlorides (2,4-DCP and 2,4-D) in aqueous alkaline medium with high chloride concentration

A highly concentrated aqueous saline-containing solution of phenol, 2,4-dichlorophenoxyacetic acid (2,4-D) and 2,4-dichlorophenol (2,4-DCP) was treated by the photo-Fenton process in a system composed of an annular reactor with a quartz immersion well and a medium-pressure mercury lamp (450 W). The study was conducted under special conditions to minimize the costs of acidification and neutralization, which are usual steps in this type of process. Photochemical reactions were carried out to investigate the influence of some process variables such as the initial concentration of Fe(2+) ([Fe(2+)](0)) from 1.0 up to 2.5 mM, the rate in mmol of H(2)O(2) fed into the system (FH(2)O(2);in) from 3.67 up to 7.33 mmol of H(2)O(2)/min during 120 min of reaction time, and the initial pH (pH(0)) from 3.0 up to 9.0 in the presence and absence of NaCl (60.0 g/L). Although the optimum pH for the photo-Fenton process is about 3.0, this particular system performed well in experimental conditions starting at alkaline and neutral pH. The results obtained here are promising for industrial applications, particularly in view of the high concentration of chloride, a known hydroxyl radical scavenger and the main oxidant present in photo-Fenton processes.