Roberto Guardani

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.

Photo-Fenton degradation of wastewater containing organic compounds in solar reactors

Abstract In this work, the photo-Fenton oxidation of phenol in aqueous solutions has been investigated using Fe 2+ , H 2 O 2 and UV–visible light (sunlight). Laboratory-scale experiments were carried out using solar reactors of two different configurations: (1) a concentrating parabolic trough reactor (PTR) and (2) a non-concentrating thin-film reactor. Global solar irradiance was measured during the experiments. Additional laboratory experiments were carried out in an annular photochemical reactor using an artificial light source, at the same experimental conditions. The results indicate that the photo-Fenton process using solar irradiation is an effective treatment for industrial wastewater containing phenol. At low contaminant concentration (TOC 0 =100 ppm), more than 90% of the total organic carbon content of the initial phenol solution could be converted to inorganic carbon within about 3 h of irradiation, using artificial light or sunlight (even on cloudy days), in reactors of different geometry. At moderate or higher phenol concentrations (TOC 0 =550 or 1000 ppm), the results indicate satisfactory TOC removal (45–55%) at reasonable degradation rates. Experiments under different insolation conditions suggest a direct linear dependence of the organic carbon removal on the accumulated sunlight energy reaching the system. Solar light can be used either as a complementary or alternative source of photons to the process.

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.

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.

Use of neural networks in the analysis of particle size distributions by laser diffraction

The use of techniques based on laser diffraction for measuring particle size distributions has become increasingly popular in recent years. However, the models used to compute the results from light scattering data still do not enable the accurate application of such techniques in highly concentrated suspensions of particles in fluids, as well as in cases of non-spherical particles. In this paper a neural network model is applied to light scattering data obtained with particles in liquid suspensions at different solid concentrations and compared with a conventional algorithm based on Fraunhofer diffraction. The results show that neural network models can be successfully used to compute particle size distributions from laser diffraction measurements and that this approach may expand the range of application of such techniques.

Experimental comparison and simulation of static and dynamic solid layer melt crystallization

Laboratory-scale experiments were carried out to compare different melt crystallization process configurations, namely static layer crystallization, in which only natural convection influences mass and heat transfer, and dynamic layer crystallization, in which forced convection is obtained by pumping the mother liquor as a falling film on a heat exchanger. The system utilized as case study was e-caprolactam containing different initial water concentrations as impurity. The layer growth rate was varied, based on criteria of highest possible productivity, purity and yield. Based on the experimental results a study on the required number of purification stages was carried out for different process options, using a mathematical model for the layer crystallization process.

Characterization of flow regime transitions in a circulating fluidized bed

Abstract Mean values and various characteristics of the fluctuating component of the local pressure drop are measured in a circulating fluidized bed. It is shown that simultaneous processing of the above given quantities is necessary for detailed flow regime identification. A parametric approach using a first-order autoregressive model of the time series is proposed to distinguish between the dilute- and dense-phase flows. The values of choking velocity are determined from the dependence of local static pressure drop on gas velocity. The results obtained are compared with some correlations for choking velocity.

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.

Use of solar energy in the treatment of water contaminated with phenol by photochemical processes

The solar driven photo-Fenton process for treating water containing phenol as a contaminant has been evaluated by means of pilot-scale experiments with a parabolic trough solar reactor (PTR). The effects of Fe(II) (0.04-1.0 mmol L-1), H2O2 (7-270 mmol L-1), initial phenol concentration (100 and 500 mg C L-1), solar radiation, and operation mode (batch and fed-batch) on the process efficiency were investigated. More than 90% of the dissolved organic carbon (DOC) was removed within 3 hours of irradiation or less, a performance equivalent to that of artificially-irradiated reactors, indicating that solar light can be used either as an effective complementary or as an alternative source of photons for the photo-Fenton degradation process. A non-linear multivariable model based on a neural network was fit to the experimental results of batch-mode experiments in order to evaluate the relative importance of the process variables considered on the DOC removal over the reaction time. This included solar radiation, which is not a controlled variable. The observed behavior of the system in batch-mode was compared with fed-batch experiments carried out under similar conditions. The main contribution of the study consists of the results from experiments under different conditions and the discussion of the system behavior. Both constitute important information for the design and scale-up of solar radiation-based photodegradation processes.

Industrial Wastewater Treatment by Photochemical Processes Based on Solar Energy

Background: The solar photo-Fenton process has enormous potential for becoming a viable alternative to conventional processes for the treatment of industrial wastewater. However the costs associated with the use of artificial irradiation have hindered many times industrial application of these processes. Method of Approach: In this work, the photo-Fenton remediation of various industrial wastewaters (containing silicones, pesticides, phenol and hydrocarbons, model, and real) in aqueous systems has been studied using Fe(II), H 2 O 2 , and UV-visible sunlight. Experiments were carried out using a concentrating parabolic trough reactor (PTR) and a nonconcentrating falling-film reactor. Results: In general, at low contaminant concentration, more than 90% of the total organic carbon content could be converted to inorganic carbon within about 2-3 h, using sunlight, in reactors of different geometry. Conclusions: Solar light can be used either as an effective complementary or alternative source of photons to the photo-Fenton degradation process of a diversity of chemical pollutants.