José M. Gozálvez-Zafrilla

Uncertainty and sensitive analysis of environmental model for risk assessments: An industrial case study

The objectives of this paper are the application of uncertainty and sensitivity analysis methods in atmospheric dispersion modeling to study the prediction of the dispersion of pollutants in the atmosphere. The Gaussian Plume Model is used to study the impact of meteorology on the dispersion of the emissions from an industrial source complex. The determination of ground-level concentration and maximum ground-level concentration is useful for the prediction of violations of air quality regulations. The Industrial Source Complex Short-Term (ISCST-3) air pollution model was adopted to predict the ground-level concentration of sulfur dioxide (SO2) emitted by a power plant located in an industrial region site in Spain. Quantitative uncertainty analysis has become a common component of risk assessments. Uncertainties were defined a priori for each of the following variables: wind speed, wind direction, and pollutant emission rate. In order to obtain information about the uncertainty of computer code results, a number of code runs was performed using the nonparametric tolerance limits method. The Monte Carlo method was used to propagate uncertainty across codes. The Spearman rank correlation coefficient was used as a sensitivity measure.

Evaluation of nanofiltration processes for brackish water treatment using the DSPM model

Abstract Nanofiltration (NF) is a membrane process particularly well suited for the treatment of brackish waters to obtain drinking-water or reuse industrial wastewater. Efficient design of NF systems required the simulation using mass transfer models to obtain permeate flux and component rejection. The most accurate models depend on feed composition and operating parameters of the system; unlike phenomenological models, these models are difficult to implement because of their complex mathematical structure. This paper presents an iterative solution of the Donnan-Steric Pore Model (DSPM) and its integration into an overall model to predict NF performance. The developed computer model has shown to be an effective tool to determine suitable NF system configurations and membranes for a particular brackish water problem.

Implementation of membrane models on a CAPE-OPEN tool to simulate a process including RO membranes

Process simulators are a useful tool for evaluating different configurations of chemical processes and developing new ones. Although these programs include many standard units like reactor or distillation towers, membrane units are not usually included. In this paper, it is shown the possibility to implement a reverse osmosis (RO) membrane unit in the free process simulator COCO, using input membrane parameters. The RO modeling is based on the coupling of the solution–diffusion model with a model for concentration polarization. The model was implemented as a Matlab CAPE-OPEN unit operation. In order to show the functionality of the developed application, a rinsing process adapted from literature was implemented to test different configurations. In this way, the combined use of the COCO simulator and the model of a reverse osmosis unit proved to be a useful tool for comparing the performance of different process configurations.

Efficient fitting of nanofiltration model parameters for a specified groundwater type by selecting suitable characterization data-sets

AbstractNanofiltration (NF) models can be useful to perform optimal designs of membrane systems and to estimate membrane performance for waters. There is a special interest in obtaining NF models with parameters based on measurable properties of the membrane and independent from the feed and operating conditions. However, many times, from a practical point of view, NF parameters can be directly fitted from experiments performed with salts in a range of compositions. The aim of this study is to select the better combination of experiments to yield a suitable fitting for the NF model Donnan steric-partitioning pore model with dielectric exclusion (DSPM-DE). In our case, the best fitting for a specific group of waters is searched (groundwater belonging to a Mediterranean region with moderate salinity). The first part of the work is devoted to study which combinations of salts and concentrations lead to higher information. Using known values of NF parameters, permselective results were computationally generat...

Modelling, control and optimization of a continuous distillation tower through fuzzy techniques

Abstract This paper applies a methodology for the design of a fuzzy controller applicable to continuous processes based on local fuzzy models and velocity linearizations. It has been applied to the implementation of a fuzzy controller for a continuous distillation tower. Continuous distillation towers can be subjected to variations in feed characteristics that cause loss of product quality or excessive energy consumption. Therefore, the use of a fuzzy controller is interesting to control process performance. A dynamic model for continuous distillation was implemented and used to obtain data to develop the fuzzy controller at different operating points. The fuzzy controller was built by integration of linear controllers obtained for each linearization of the system. Simulation of the model with controller was used to validate the controller effectiveness under different scenarios. The results showed that the fuzzy controller was able to keep the target output in the desired range for different inputs disturbances, changing smoothly from a predefined target output to another. The developed techniques are applicable to more complex distillation systems including more operating variables.