Constraint qualifications in linear vector semi-infinite optimization

Linear vector semi-infinite optimization deals with the simultaneous minimization of finitely many linear scalar functions subject to infinitely many linear constraints. This paper provides characterizations of the weakly efficient, efficient, properly efficient and strongly efficient points in terms of cones involving the data and Karush–Kuhn–Tucker conditions. The latter characterizations rely on different local and global constraint qualifications. The global constraint qualifications are illustrated on a collection of selected applications.

The influence of surfactant loading level in a montmorillonite on the thermal, mechanical and rheological properties of EVA nanocomposites

In this work, montmorillonite (Mt) has been organically modified with ethyl hexadecyl dimethyl ammonium (EHDDMA) in 20, 50, 80 and 100% of the nominal exchange capacity (CEC) of the Mt. A full characterization of the organo-montmorillonite (OMt) obtained has been made, including thermal analysis, X-Ray Diffraction, elemental analysis CHN and nitrogen adsorption. According to the results, 12% in mass of the surfactant added is strongly retained by the Mt. When the mass percentage of EHDDMA exchanged in the OMt is increased up to this level, the interactions OMt–EHDDMA are steeply reduced depending on the EHDDMA content. Clay polymer nanocomposites (CPN) were prepared by melt mixing of EVA and different loads of OMt. The CPN were compress molded to obtain 1 mm thick sheets, which have been characterized according to their mechanical, thermal and rheological behaviors. The major changes in the structure of the OMt are obtained for low contents of EHDDMA. Nevertheless, the CPN containing OMt exchanged at 20 and 50% of the CEC show relatively low effect of the EHDDMA while the mechanical response and rheological behavior of CPN with OMt modified at 80 and 100% of the CEC are much more pronounced.

Robust Solutions of MultiObjective Linear Semi-Infinite Programs under Constraint Data Uncertainty

The multiobjective optimization model studied in this paper deals with simultaneous minimization of finitely many linear functions subject to an arbitrary number of uncertain linear constraints. We first provide a radius of robust feasibility guaranteeing the feasibility of the robust counterpart under affine data parametrization. We then establish dual characterizations of robust solutions of our model that are immunized against data uncertainty by way of characterizing corresponding solutions of robust counterpart of the model. Consequently, we present robust duality theorems relating the value of the robust model with the corresponding value of its dual problem.

Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods.

Methane dry reforming over Ni supported on pine sawdust activated carbon: Effects of support surface properties and metal loading

The influence of metal loading and support surface functional groups (SFG) on methane dry reforming (MDR) over Ni catalysts supported on pine-sawdust derived activated carbon were studied. Using pine sawdust as the catalyst support precursor, the smallest variety and lowest concentration of SFG led to best Ni dispersion and highest catalytic activity, which increased with Ni loading up to 3 Ni atoms nm-2. At higher Ni loading, the formation of large metal aggregates was observed, consistent with a lower "apparen" surface area and a decrease in catalytic activity. The H2/CO ratio rose with increasing reaction temperature, indicating that increasingly important side reactions were taking place in addition to MDR.

Electrocatalytic reduction of CO2 to formate using particulate Sn electrodes: Effect of metal loading and particle size

The development of electrochemical processes for the conversion of CO2 into value-added products allows innovative carbon capture & utilization (CCU) instead of carbon capture & storage (CCS). In addition, coupling this conversion with renewable energy sources would make it possible to chemically store electricity from these intermittent renewable sources. The electroreduction of CO2 to formate in aqueous solution has been performed using Sn particles deposited over a carbon support. The effect of the particle size and Sn metal loading has been evaluated using cyclic voltammetry and chronoamperometry. The selected electrode has been tested on an experimental filter-press type cell system for continuous and single pass CO2 electroreduction to obtain formate as main product at ambient pressure and temperature. Experimental results show that using electrodes with 0.75 mg Sn cm−2, 150 nm Sn particles, and working at a current density of 90 mA cm−2, it is possible to achieve rates of formate production over 3.2 mmol m−2 s−1 and faradaic efficiencies around 70% for 90 min of continuous operation. These experimental conditions allow formate concentrations of about 1.5 g L−1 to be obtained on a continuous mode and with a single pass of catholyte through the cell.

Constraint qualifications in convex vector semi-infinite optimization

Convex vector (or multi-objective) semi-infinite optimization deals with the simultaneous minimization of finitely many convex scalar functions subject to infinitely many convex constraints. This paper provides characterizations of the weakly efficient, efficient and properly efficient points in terms of cones involving the data and Karush–Kuhn–Tucker conditions. The latter characterizations rely on different local and global constraint qualifications. The results in this paper generalize those obtained by the same authors on linear vector semi-infinite optimization problems.