The effects of explicit chiral symmetry breaking multiquark interactions on the spin 0 and 1 meson nonets: the ruling of the vector mesons

We have recently extended the scalar-pseudoscalar sector of a generalized NJL Lagrangian that includes all NLO non derivative interactions in Nc counting (including explicit symmetry breaking ones) in order to incorporate the spin 1 mesons in the low-lying ground state of QCD [1]. Upon bosonization, the well known mixing of the scalar-vector and of the pseudoscalar- axial-vector fields occurs in the quadratic part of the Lagrangian. We show that a linearized diagonalization of these terms can be effected in a completely general way without compromising the underlying symmetries of the Lagrangian [2]. The resulting spin 1 mass spectra evidence a relation involving only the vector and axial-vector meson masses and the constituent quark masses. We discuss the dominant role of this relation in the fits and we show that the model may be fitted to accommodate to a very good accuracy the 4 low-lying meson spectra.Comment: 5 pages, 3 tables; based on a talk given at Hadron 2017, Salamanc

A general framework to diagonalize vector--scalar and axial-vector--pseudoscalar transitions in the effective meson Lagrangian

A new mathematical framework for the diagonalization of the nondiagonal vector--scalar and axial-vector--pseudoscalar mixing in the effective meson Lagrangian is described. This procedure has unexpected connections with the Hadamard product of $n\times n$ matrices describing the couplings, masses, and fields involved. The approach is shown to be much more efficient as compared with the standard methods employed previously. The difference is especially noticeable if the chiral symmetry is broken explicitly. The paper ends with an illustrative application to the chiral model with broken $SU(3)_L\times SU(3)_R$ symmetry.Comment: 16 page

Relationship between paste components and anode quality for the primary aluminum industry

Pre-baked anode production for the primary aluminum industry uses only the density data of the green anode block (weight and height of the anode) for quality control of the manufactured anodes. A delay of 25 to 30 days is required after forming of green anodes to obtain the characteristics of the baked anodes, thus to have a feedback on anode quality problems. The characterization of anode paste to predict the properties of baked anodes is highly useful to avoid the baking of an anode which will be rejected once baked. Its production requires doubling of the energy used for the accepted anodes since it has to be recycled. A control method prior to of the baking process will reduce the greenhouse gas (GHG) emissions. The project highlights the links between the structure of green and baked anodes with the physical and reactivity properties of industrial anodes. An image analysis program was developed which introduces a concept of matrix localization inside and outside of coke particles. It has been found that electrical resistivity is not always related to the density or the bending strength directly, but rather to the homogenization of the pitch during baking, and it required a little more pitch than it did for the two other properties. The density is more related to the arrangement of coke particles within the matrix, the ability to fill the pores of the coke which is related to its pores’ sizes. The bending strength depends on how much the transformation of the matrix within the structure of the green anode is successful during the baking and on the distribution of coke particle sizes within the matrix. The reactivity of the anodes depends on the crystalline length of the anode, the microporosity of the cokes, and the network of macropores of the coke and that of the matrix of the anodes. The design of a kneader, a mold, and a rheology test of the anode paste helped highlight the optimization criteria for the anode paste. The raw materials and all particle sizes used in dry aggregate recipes of the anode in the anode plant were used in the laboratory to form pastes and cores from ten different paste recipes. Indicators for pitch level, density, and electrical resistivity of laboratory baked cores were determined. An agreement is found between the characteristics of the pastes, and the densities and electrical resistivities of cores in the baked state. La production d’anode précuite pour l’industrie primaire de l’aluminium utilise que la donnée de la densité du bloc d’anode à l’état cru (la masse et la hauteur de l’anode) comme contrôle de la qualité de la fabrication des anodes. Un retard de 25 à 30 jours est nécessaire après la mise en forme des anodes crues pour obtenir les caractéristiques des anodes cuites en rétroaction de problème de qualité. La caractérisation de la pâte d’anode pour en prédire les propriétés des anodes cuites devient un champ de recherche utile pour éviter la cuisson d’une anode rejetée une fois cuite. Sa production demande le double d’énergie contrairement aux anodes de qualité produite du premier coup puisqu’elle a besoin d’être recyclée. Une méthode de contrôle plus en avant du processus de la cuisson permet d’éliminer des gaz à effets de serre. Le projet fait la lumière entre les liens de la structure des anodes crues et des anodes cuites avec les propriétés physiques et réactives des anodes industrielles. Un programme d’analyse d’image a été développé avec l’introduction de la localisation de la matrice à l’intérieur et à l’extérieur des particules de coke. Il a été constaté que la résistivité électrique n'est pas toujours liée à la densité ou à la résistance à la flexion, mais plutôt à l'homogénéisation du brai durant la cuisson et nécessite un peu plus de brai que les deux autres propriétés. La densité dépend de l'arrangement des particules de coke au sein de la matrice, la capacité à remplir les pores du coke qui est liée à la taille de ses pores. La résistance à la flexion dépend de la réussite de la transformation de la matrice à la cuisson et de la répartition des tailles de particules de coke au sein de la matrice. Les réactivités des anodes sont quant à elles tributaires de la longueur cristalline de l’anode, la microporosité des cokes et le réseau de macropores dans le coke et la matrice des anodes. La conception d’un malaxeur, d'un moule et des tests de rhéologie de la pâte d’anode a permis de mettre en lumière les critères d’optimisation des pâtes d’anode. Les matières premières et toutes les tailles de particules utilisées dans les recettes d’agrégats secs des anodes à l’usine d’anode ont été utilisées en laboratoire pour former des pâtes et des carottes de dix différentes recettes de pâte. Les indicateurs pour le niveau de brai, la densité et la résistivité électrique de carottes cuite de laboratoire ont été déterminés. Un lien a été trouvé entre les caractéristiques de la pâte et la densité et la résistivité des carottes à l’état cuit

Coke–pitch interactions during anode preparation

The information on the interactions between coke and pitch is of great value for the aluminum industry. This information can help choose the suitable coke and pitch pairs as well as the appropriate mixing parameters to be used during the production of anodes. In this study, the interaction mechanisms of pitch and coke at the mixing stage were studied by a sessile-drop test using two coal-tar pitches as the liquid and three petroleum cokes as the substrate. The results showed that the coke–pitch interactions are related to both pitch and coke chemical compositions. The contact angle of different coke–pitch systems decreased with increasing time and temperature. At high temperatures, decreasing the pitch viscosity facilitated the spreading of pitch and its penetration into the coke bed. The chemical behavior of petroleum cokes and coal tar pitches were studied using the FT-IR spectroscopy and XPS. The results showed that the wettability behavior of cokes by pitches depends on their physical properties as well as the presence of surface functional groups of coke and pitch which can form chemical bonds

An artificial neural network model for predicting the CO2 reactivity of carbon anodes used in the primary aluminum production

Carbon anode is one of the key components for the electrolytic production of aluminum. It is mainly composed of calcined petroleum coke, coal tar pitch, and recycled carbon materials. The impurities in the raw materials, which are mainly by-products of different industries, influence significantly the quality of anodes. Usually, no well-known mathematical relationship exists between the various physical and chemical properties of raw materials and the final anode properties. In such situations, the artificial neural network (ANN) methods can serve as a useful tool to predict anode properties. In this study, published data have been used to show the proficiency of different artificial neural networks using the MATLAB software. The average error between the predicted and experimental values is around 6 %. The artificial neural network was also used to identify the effect of impurities such as, vanadium, iron, sodium, and sulfur on the CO2 reactivity of anodes. ANN also showed the effect of pitch percentage and coke porosity on the CO2 reactivity of anodes. The effect of CO2 and air reactivities of coke on the CO2 reactivity of anode was also studied. The predictions were found to be in good agreement with the results of other studies in the literature

Development of an analytical dynamic model of a vibro‐compactor used in carbon anode production

The carbon anode quality has a significant impact on the production of primary aluminum. Their performance can be evaluated by their various mechanical, electrical, physical, and chemical, properties such as density, electric resistivity, C02 and air reactivities. The focus of this work is to study the various parameters of the vibro-compaction, which is one of the critical steps in the process of anode manufacturing. In this work, a dynamic model of a vibro-compactor is developed. The vibrocompactor is modeled as a rigid mass suspended on springs and dampers and subjected to harmonic external excitation. This model is used to identify the optimal conditions of the vibrocompacting process. These conditions are obtained through a correlation between the analytical vibro-compaction parameters and data from an industrial vibro-compactor. The use of optimum parameters will help improve the anode performance and, consequently, lead to better productivity and reduction on environmental impact

Modification of coke by different additives to improve anode properties

Aluminum is produced in electrolytic cells using carbon anodes, which consist of a mixture of coke, pitch, and recycled carbon material. Anodes play an important role in aluminum production. The quality of raw materials can vary based on the source and the process parameters. In spite of the variations in the raw material properties, the industry has to maintain the quality of anodes. In order to manufacture good quality anodes, coke and pitch must interact well with each other. The affinity between these two components depends on good wetting properties, which will lead to good binding of the particles. The main objective of this work is to modify the coke in order to improve its wetting properties using different additives. An FT-IR study was done to identify certain functional groups in non-modified and modified coke as well as in pitch. The wetting tests were carried out using the sessile-drop method to measure the contact angle between cokes and pitch. Based on FT-IR and wettability results, an additive was selected and used for the fabrication of anodes, which were characterized before and after baking. The modification of coke with the selected additive improved the anode properties

Application of the artificial neural network (ANN) in predicting anode properties

Carbon anodes are a major part of the cost of primary aluminum production. The focus of the industry is to minimize the consumption of anodes by improving their quality. Therefore, the determination of the impact of quality of raw materials as well as process parameters on baked anode properties is important. The plants have a large data base which, upon appropriate analysis, could help maintain or improve the anode quality. However, it is complex and difficult to analyze these data using conventional methods. The artificial neural network (ANN) is a mathematical tool that can handle such complex data. In this work, Matlab software was used to develop a number of ANN models. Using published data, linear multi-variable analysis and ANN were applied to assess the advantages of custom multilayered feed-forward ANN. Results are presented which show a number of industrial applications

Study of the effect of granulometry on coke bulk density using artificial neural network

Carbon anode is one of the key components in the production of primary aluminum. The important desired properties of the anodes are high density, low electrical resistivity, low air and CO2 reactivities, and high mechanical strength. The anodes consist of pitch as binder and dry aggregate (coke, butts, and recycled anodes) as filler material. Granulometry of the dry aggregate is one of the key parameters that control the anode properties. In this article, a multilayer feed forward artificial neural network with backpropagation training has been used to correlate the dry aggregate granulometry with its bulk density. Experimental bulk density values of different size fractions of the dry aggregate were used for the training of the neural network. The model helps understand and predict the effect of different dry aggregate size fractions on its bulk density. This article presents the model and the results of the study