Maria J. Fernandez-Torres

Purge efficiency in the determination of trihalomethanes in water by purge-and-trap gas chromatography

Purge-and-trap gas chromatography-mass spectrometry (PT-GC-MS) has become an accepted method for the analysis of trihalomethanes (THMs) in water. The purge-and-trap technique is based on an efficient transfer of volatile organic compounds from the liquid (contained in the purge chamber) to the gaseous phase by bubbling with an inert gas. The aim of this work was to study the purge systems efficiency by means of several consecutive purge cycles lasting 11 min each of the same liquid sample. The concentration range chosen of THMs was very wide [5-200 microg L(-1)]. The inert gas flow rate was 40 mL min(-1), and experiments were performed at temperatures of 25, 35 and 50 degrees C. Bromoform (CHBr(3)), the least volatile compound, needed 19 cycles to be purged quantitatively at a concentration of 200 microg L(-1) and only 7 cycles at 5 microg L(-1) for a 25 mL sample at 25 degrees C. Chloroform (CHCl(3)), the most volatile compound, required 4 cycles to be fully extracted at 200 microg L(-1) and 2 at 5 microg L(-1). Finally, Novaks theoretical model, based on the distribution constant between gas and liquid phases, was used to correlate the THMs purging extraction data.

MILP method for objective reduction in multi-objective optimization

Abstract A procedure for reducing objectives in a multi-objective optimization problem given a set of Pareto solutions is presented. Three different models are detailed, which achieve three different degrees of objective reduction. These models are based on maintaining the dominance structure of the problem. To compare the performance of the proposed models, these are tested with pure mathematical cases and with actual data from previous works in the field of multi-objective optimization. The first model provides the reduced subset of objectives that do not alter the dominance structure of the problem at all. The second model determines the minimum subset of objectives that alters the dominance structure with an upper predefined limit for the error. The last model provides the subset of objectives with a previously defined cardinality, which achieves the minimum error. The possibility of different inputs introduces flexibility into the models, which accounts for the preferences of the decision-maker.

Isobutane Alkylation Process Synthesis by means of Hybrid Simulation-Multiobjective Optimization

Abstract Multiobjective Generalized Disjunctive Programming (MO-GDP) optimization has been used for the synthesis of an important industrial process, isobutane alkylation. The two objective functions to be simultaneously optimized are the environmental impact, determined by means of LCA (Life Cycle Assessment), and the economic potential of the process. The main reason for including the minimization of the environmental impact in the optimization process is the widespread environmental concern by the general public. For the resolution of the problem we employed a hybrid simulation- optimization methodology, i.e., the superstructure of the process was developed directly in a chemical process simulator connected to a state of the art optimizer. The model was formulated as a GDP and solved using a logic algorithm that avoids the reformulation as MINLP -Mixed Integer Non Linear Programming-. Our research gave us Pareto curves compounded by three different configurations where the LCA has been assessed by two different parameters: global warming potential and ecoindicator-99.