Determination of Kinetic Parameters for Heterogeneous Reaction System Employing Discrete Element Methods under HPC Platforms

The complex processes of heterogeneous reactions of granular materials such as occurring during metals-ore reduction or biomass gasification involve numerous physical phenomena. The combination of elevated temperature, complex flow, aggressive atmosphere and heterogeneous chemistry make it difficult to study these industrial processes. One of the most important aspects f heterogeneous reactions is to understand and quantify the evolution of the different transformations. For instance, during metal-oxides reduction processes, it is of high importance to quantify the rate at which the pure metal is formed. Nevertheless, it is almost impossible, by experimental means only, to separately observe, accurately quantify and gain insight into these mingled nonlinear physical and chemical processes. In the last decade, numerical simulation tools for particulate processes, such as the eXtended Discrete Element Method (XDEM), have become indispensable to study complex systems without the need of costly experimental practices. In the past, the XDEM has been employed to predict the reduction of tungsten trioxide (WO 3) in dry hydrogen (H2) atmospheres [1] and reduction of iron ores [2]. In the before-mentioned research works, it was employed kinetic data extracted from literature. On one hand, in these processes the kinetic data differ from each other. This is due to the fact that the experimental data in the literature is interpreted with lumped models and empirical models bonded to the specific experimental conditions. On the other hand, advanced simulation tools, such as XDEM, account for all the influencing phenomena (e.g. species and energy distribution, flow conditions, particles shape, rheological properties) constantly interacting in time and space. In these advanced simulation tools, each particle is treated and solved as individual entities and an accurate prediction of the species formation and transport in time and space is provided. Thus, in such advanced numerical tools, the reaction rate parameters representative of the kinetics alone of the involved chemical reactions must be employed. In this contribution, two XDEM simulation case studies accounting for the industrial reduction of WO 3 are presented. The first case study is employed to determine the reaction rate parameters of the four prevalent reduction steps (WO 3↔WO2.9↔WO2.72↔WO2↔W) upon the H 2 reduction of O3. Where the reaction rates are modeled following an Arrhenius law with two parameters per step i.e. pre-exponential factor and activation energy). The constituted optimization problem of minimization of error of the XDEM simulations vs experimental data, implemented and solved in a High Performance Computing (HPC) cluster, is presented and discussed. The determined parameters are later assessed by comparison to a secondly presented case study

A Discrete-Continuous Method for Predicting Thermochemical Phenomena in a Cement Kiln and Supporting Indirect Monitoring

Thermochemical phenomena involved in cement kilns are still not well understood because of their complexity, besides technical difficulties in achieving direct measurements of critical process variables. This article addresses the problem of their comprehensive numerical prediction. The presented numerical model exploits Computational Fluid Dynamics and Finite Difference Method approaches for solving the gas domain and the rotating wall, respectively. The description of the thermochemical conversion and movement of the powder particles is addressed with a Lagrangian approach. Coupling between gas, particles and the rotating wall includes momentum, heat and mass transfer. Three-dimensional numerical predictions for a full-size cement kiln are presented and they show agreement with experimental data and benchmark literature. The quality and detail of the results are believed to provide a new insight into the functioning of a cement kiln. Attention is paid to the computational burden of the model and a methodology is presented for reducing the time-to-solution and paving the way for its exploitation in quasireal-time, indirect monitoring

A Discrete-Continuous Method for Predicting Thermochemical Phenomena in a Cement Kiln and Supporting Indirect Monitoring

Thermochemical phenomena involved in cement kilns are still not well understood because of their complexity, besides technical difficulties in achieving direct measurements of critical process variables. This article addresses the problem of their comprehensive numerical prediction. The presented numerical model exploits Computational Fluid Dynamics and Finite Difference Method approaches for solving the gas domain and the rotating wall, respectively. The description of the thermochemical conversion and movement of the powder particles is addressed with a Lagrangian approach. Coupling between gas, particles and the rotating wall includes momentum, heat and mass transfer. Three-dimensional numerical predictions for a full-size cement kiln are presented and they show agreement with experimental data and benchmark literature. The quality and detail of the results are believed to provide a new insight into the functioning of a cement kiln. Attention is paid to the computational burden of the model and a methodology is presented for reducing the time-to-solution and paving the way for its exploitation in quasi-real-time, indirect monitoring

XDEM for Tuning Lumped Models of Thermochemical Processes Involving Materials in the Powder State

Processes involving materials in gaseous and powder states cannot be modelled without coupling interactions between the two states. XDEM (Extended Discrete Element Method) is a valid tool for tackling this issue, since it allows a coupled CFD- DEM simulation to be run. Such strength, however, mainly finds in long computational times its main drawback. This aspect is indeed critical in several applications, since a long computational time is in contrast with the increasing demand for predictive tools that can provide fast and accurate results in order to be used in new monitoring and control strategies. This paper focuses on the use of the XDEM framework as a tool for fine tuning a lumped representation of the non-isothermal decarbonation of a CaCO3 sample in powder state. The tuning of the lumped model is performed exploiting the multi-objective optimization capability of genetic algorithms. Results demonstrate that such approach makes it possible to estimate fast and accurate models to be used, for instance, in the fields of virtual sensing and predictive control

Computational Study of the Industrial Synthesis of Tungsten Powders

Discrete Element Method (DEM) is a highly employed Lagrangian technique to represent particulate systems. When DEM techniques are extended by adding thermochemical conversion of solid particles as well as their interaction with the surrounding fluid, numerous challenging applications can be numerically studied. Nevertheless, industrial applications with large number of particles, such as powder synthesis or blast furnaces, are often time or size limited due to the high computational efforts that these simulations demand. This contribution introduces the Agglomerated Particle Method (APM) as a numerical technique aiming to reduce the computational costs of coupled Discrete Element Method and Computational Fluid Dynamics (DEM-CFD) approaches for the thermochemical conversion of powder beds. From experimental and numerical investigation on thermochemical conversion of packed beds has been observed that the temperature or composition of particles in a small spatial domain do not vary significantly. Consequently, one single numerical solution may be representative for all the particles on such a domain. Thus, a collection of neighbor particles are represented by one single agglomerated particle solved by eXtended Discrete Element Method (XDEM) techniques. The proposed model is firstly assessed with classic benchmark problems for heating and drying of packed beds. Later, the model approach is employed for predicting the industrial synthesis of metallic tungsten powder. The comparison of APM predictions with resolved XDEM predictions and experimental data shows the proposed model as a viable technique to solve large scale powder applications, such as tungsten powder production, at feasible time