Competitive reaction modelling in aqueous systems. The case of contemporary reduction of dichromates and nitrates by nZVI

In various Countries, Cr(VI) still represents one of the groundwater pollutant of major concern, mainly due to its high toxicity, furthermore enhanced by the synergic effect in presence of other contaminants. As widely reported in the recent literature, nanoscale zero valent iron particles (nZVI-p) have been proved to be particularly effective in the removal of a wide range of contaminants from polluted waters. In this work, experimental tests of hexavalent chromium reduction in polluted groundwater in the presence of nitrate by nZVI-p are presented and discussed. The effect of different nitrate amounts on Cr(VI) reduction mechanism was investigated and the obtained results were successfully interpreted by the proposed kinetic model. nZVI-p produced by the classical borohydride reduction method were added in to synthetic solutions with the initial concentration of Cr(VI) set at 93, 62 and 31 mg L-1 and different nitrate contents in the range 10-100 mg L-1. According to the experimental results, nitrate showed an adverse effect on Cr(VI) reduction, depending on the nZVI/Cr(VI) and Cr(VI)/NO3 - ratio. The proposed kinetic model soundly grasps the competitive nature of the Cr(VI) reduction process when other chemical species are present in the treated solution

Production of nano zero valent iron particles by means of a spinning disk reactor

Nitrates are considered hazard compounds for human health due to their tendency to be reduced to nitrites, in particular in reducing environment. Nano zero valent iron (nZVI) represents an efficient and low-cost adsorbent/reductive agent for nitrate removal from groundwater. In this work, nZVI particles were produced by means of two different equipment types based on the same chemical synthesis method: a batch stirred tank reactor (BSTR) and a spinning disk reactor (SDR). This latter apparatus is capable to strongly promote micromixing at a steady-state, continuous condition, and such as qualifies to subsist in the framework of process intensification. Particle size distribution (PSD) of the obtained nZVI particles were measured by a DLS technique. The removal efficiency of the produced nVI particles were checked by using two NO3-solutions (1.6 and 6.4 mM) and by monitoring nitrate concentration reduction rates at selected time intervals. Results showed that the nZVI particles produced by SDR have a narrow PSD with a mean diameter of 65nm; on the contrary, particles produced by BSTR shows bimodal PSD with modal sizes of 105 nm and 400 nm, respectively. Experimental tests of nitrates reduction in water have been performed, using both the particles produced by the above mentioned techniques. Results of batch tests showed that the highest removal efficiency of nitrates was observed by using the nZVI particles produced by means of SDR, as a consequence of the higher average specific surface. Since nitrate removal process involves both reduction and adsorption processes, the removal mechanism has been investigated, and the pseudo-first-order reduction kinetic model was successfully tested and reported in both cases

Artificial aggregate from non metallic automotive shredder residue

Until 2005 in the European Union (EU) approximately 12 M vehicles were yearly shredded, and 8 or 9 M t/ year of waste was produced. About 14 million tons of End of Life Vehicles (ELVs) are foreseen by 2015. This huge amount of waste must be treated and disposed of in a sustainable way. The most common treatment technologies, involve ELVs shredding to recover iron and steel (70%) and non ferrous metals (5%) from vehicles. The remaining fraction, called Automotive Shredder Residue (ASR), and representing about 25% wt. of each vehicle, is generally landfilled. For more than two thirds, this last residue deals with combustible materials (fibers, polyethylene etc..), suitable to be reused as a fuel, but a substantial amount of soil particles, metals, glasses and plastics residues are also present. Consequently, a new sustainable way to reuse ASR is to separate the organic from the inorganic fraction, and use them in combustion plants, gasification and in the cement industry, respectively. Regarding this second way of recovery, several studies have been already successfully performed with the aim of transforming ASR into aggregates for asphalt or cement mixes, by thermal treatment followed by chemical treatment, or by physical processes, such as granulation. In this work, a selected fraction of non metallic automobile shredder residue was immobilized in granules produced at room temperature in a pilot scale granulator. Granules were obtained by mixing selected amount of ASR with a binder (cement or lime) in the presence of additions (fly ash) and admixtures. The final aim of this work was to investigate the mechanical properties of concrete samples produced using the artificial aggregate obtained through different combinations of ASR, fly ash and binder. Additional freeze and thaw tests were finally performed to assess concrete durability along time

Very deep convolutional neural networks for face identification

In very recent years, several classification problems in computer vision, have boosted its performance by using Deep Learning techniques, in particular Convolutional Neural Networks (CNNs). The topic of the research project will focus in exploring state of the art deep learning architectures in computer vision applications. Recently architectures like GoogleNet and VGG have shown to perform better than other architectures.The goal of this thesis is to evaluate the face identification problem using very deep convolutional neural networks. In recent years, the use of CNN, with a large amount of images in databases, have made the deep learning technique very performant. The problems in training a network from scratch, such as having sufficient hardware resources and large databases, can be overcome using the finetune technique on pretrained models. This thesis evaluate the performance in finetuning for face classification the most recent CNN architectures which have obtained the best results at ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in the last years, in particular VGG, GoogLeNet and ResNet. All the pre-trained models of the CNNs were downloaded from the MatConvNet website. VGG-16 has shown best results in face classification which was followed with ResNet-101 and GoogLeNet that are the matter of this thesis

Synthesis and CO2 adsorption capacity of biomass waste functionalized by nanoparticles

Two composite materials were synthesized based on sodium alginate and biochar derived from licorice processing waste functionalized with silicon dioxide nanoparticles (SiO2) and iron oxide (Fe2O3), respectively, enabling the valorization of industrial waste. The adsorptive capacities of the two materials (Alg-SiO2 and BCLFe2O3) toward CO2 in the gaseous stream with nitrogen were evaluated by acid titration of carbonates present in a trap for CO2 consisting of a KOH solution placed downstream of the adsorption column. The aim of the present work is to evaluate the CO2 adsorption capacity of material functionalized by nanoparticles. Adams–Bohart, Thomas models, and % removal efficiency curves for the adsorption were examined to investigate the dynamic behavior of the column. From the tests performed in CO2 and N2 flow, the BCL-Fe2O3 material was demonstrated to have an adsorbent higher capacity than Alg-SiO2, respectively CO2 adsorbed 25 and 6 mg/g

Perspectives in nanotechnology based innovative applications for the environment

In this perspective paper, the actual trends in nanotechnology based innovative applications for the environment are analyzed and possible future trends were studied. On the basis of the relevant topics of the NINE congress held in Rome, 2016, a bibliographical search was performed on papers fitting in one or more categories within the last 5 years, that is: 1. Nanosensors and bionanosensors for environmental characterization and monitoring 2. Technologies for the production of Nanomaterials for the environment 3. Nanostructured materials for advanced remediation processes 4. Nano-based water and wastewater treatment processes 5. Membrane processes for the environment 6. Health and safety issues concerning Nanomaterials 7. Education on Environmental Engineering and Nanotechnology. A yearly count of contributions was performed and taken as an indicator of interest of the specific topic within the wide broad scientific community. In a second step, the resulting data was analyzed by regression techniques to estimate the trend in the next future and to evaluate the next challenges within the international research framework

Nitrates removal by bimetallic nanoparticles in water

Nitrate contamination of groundwater has become a major environmental concern since nitrates are easily transferred from unsatured zone to the satured one, due to their solubility and low sorptivity on soil particles caused by their negative charge. The effectiveness and rapidity of the reduction of NO3- is strongly dependent on the contact time, the concentration of the reductive agent, the properties and composition of the surrounding medium (pH, dissolved oxygen concentration, heavy metals and organic matter concentration). The aim of this work was to investigate the effectiveness of nZVI and bimetallic nanoparticles of Fe/Cu in the remediation of nitrate-polluted groundwaters. nZVI and Fe/Cu nanoparticles were prepared by sodium borohydride reduction method at room temperature and ambient pressure. Results confirm that the decontamination of nitrate in groundwater via the in-situ remediation by Fe/Cu nanoparticles is environmentally attractive. Batch experiments were conducted on water samples contaminated in laboratory using NaNO3 to fix the initial nitrate concentration to 57.5 mg/L. The Cu/Fe0 ratio was fixed to 0.05 (w/w) and the parameter investigated was the Fe0/NO3- weight ratio (5,10 and 15 w/w). During the tests the aqueous solution was analyzed to measure the evolution of NO3- and pH at 0, 30, 60, 90, 120 and 150 min. The results showed the increasing rate of reduction of nitrate by adding copper to ZVI particles; in fact fixing the Fe0/NO3- to 15 the tests without copper resulted in a complete removal within 150 min against the 60 min required by the tests with copper

Treatment of olive oil processing wastewater by ultrafiltration, nanofiltration, reverse osmosis and biofiltration

This paper deals with the possibility to purify olive mill wastewater streams from olive mills by means of coagulation, membrane technology and bio filtration. In the last decade, membrane processes have gained a main role to seek for a viable process to treat olive mill wastewater streams due to their capability to eliminate almost all of the pollutants in the water. One main drawback to this approach is the severe membrane fouling issues, that reduces sensibly these capabilities within a short period of time. In order to inhibit the fouling formation, in this work the boundary flux approach was used, that is the determination of proper operating conditions that do not promote fouling formation by specific measurements and modelling. Nevertheless, membranes may not be sufficient to reach the desired purification grade of the wastewater stream for a harmless disposal in the environment. Novelty of this work is the last process step, that is bio filtration and was accomplished by means of a biofilter. This step is necessary in order to guarantee the achievement of a treated water to a quality grade compatible to the discharge in superficial aquifers. The adopted system is compact, have small residence times and is capable to treat the RO permeate to the target values. The experimental work will be discussed and reported in this paper

The influence of heavy metals and organic matter on hexavalent chromium reduction by nano zero valent iron in soil

During the last decades great attention has been payed at evaluating the feasibility of Cr(VI) reduction in soil by nano zero valent iron (nZVI). An inhibitory effect on the Cr(VI) reduction by Fe-0 nanoparticles is generally shown in the presence of high level of heavy metals and natural organic matter in soil. Heavy metals in the environment can react with nZVI by redox reactions, precipitation/dissolution reactions, and adsorption/desorption phenomena. As a result of the presence of metals as Ni, Pb, a decrease in the rate of Cr(VI) reduction was observed. Hence, in the present study, experimental tests of Cr(VI) reduction by nZVI in the presence of selected heavy metals, such as nickel and lead, and in the presence of high level of organic matter, are presented and discussed. Results showed a decrease in the rate of Cr(VI) reduction in soil by nZVI (at a x25 stoichiometric excess) from 91% to 78%, 71% and 74% in the presence of Ni, Pb and both metals respectively. As regards the results of Cr(VI) reduction in the presence of organic matter, by using a reducing solution of nZVI (x25 stoichiometric excess) a decrease of Cr(VI) reduction yield from 91% to 12% was observed after 2 hours of treatment in a soil containing 35.71 g/kg of organic matter. Such low efficiency was attributed to the adsorption of organic matter onto Fe-0 nanoparticles surface, thus saturating the active reaction sites of Fe-0 nanoparticles. In addition, a significant reduction of the organic carbon in the treated soil was observed (up to 77.5%) caused by the degradation of organic matter and its dissolution in the liquid phase. A slight decrease of the total metal concentration in treated soil was also observed. Finally, kinetic tests show that Cr(VI) reduction using nZVI in the presence of a high concentration of organic compound obeyed a pseudo-zero-order kinetic model

Very deep convolutional neural networks for face identification

In very recent years, several classification problems in computer vision, have boosted its performance by using Deep Learning techniques, in particular Convolutional Neural Networks (CNNs). The topic of the research project will focus in exploring state of the art deep learning architectures in computer vision applications. Recently architectures like GoogleNet and VGG have shown to perform better than other architectures.The goal of this thesis is to evaluate the face identification problem using very deep convolutional neural networks. In recent years, the use of CNN, with a large amount of images in databases, have made the deep learning technique very performant. The problems in training a network from scratch, such as having sufficient hardware resources and large databases, can be overcome using the finetune technique on pretrained models. This thesis evaluate the performance in finetuning for face classification the most recent CNN architectures which have obtained the best results at ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in the last years, in particular VGG, GoogLeNet and ResNet. All the pre-trained models of the CNNs were downloaded from the MatConvNet website. VGG-16 has shown best results in face classification which was followed with ResNet-101 and GoogLeNet that are the matter of this thesis