Hydrodynamic and heat transfer study in corrugated wall bubbling fluidized bed experiments and CFD simulations

Considérant l'effet des parois sur la croissance des bulles, nous avons récemment utilisé des plaques ondulées comme cloisons dans des lits minces fluidisés gaz-solide à multi-compartiments. Des analyses approfondies de la dynamique des bulles et du transfert de chaleur paroi-lit dans des lits fluidisés à cloisons planes (FWBFB) ou ondulées (CWBFB) ont été effectuées pour une variété de déclinaisons pariétales et des conditions d'opération couvrant une large gamme d'angles d'ondulation (θ = 120 °, 90 °), de distances moyennes inter-paroi (C), de hauteurs initiales de repos du lit et des rapports de vitesses superficielles du gaz aux vitesses minimales de barbotage, Ug/Umb. Il a été observé que le débit de gaz nécessaire pour amorcer le barbotage était plus faible dans le cas du CWBFB. Par ailleurs, un réseau de zones cou (distance minimale) et hanche (distance maximale) du CWBFB a également favorisé la rupture des bulles, une haute fréquence des bulles, une faible vitesse de montée des bulles et donc tous convergeant vers une meilleure distribution de gaz. Le CWBFB a offert un fonctionnement de fluidisation gaz-solide stable, une faible hauteur de désengagement de transport (HDT) comparé à FWBFB. CWBFB offert significanlty supérieur coefficient de transfert de mur-à-lit chaleur comapred à FWBFB. Des simulations complètes de type mécanique des fluides numériques (CFD) transitoires Euler-Euler 3-D ont également été menées, ce qui a permis de mieux comprendre les effets de parois ondulées sur l'augmentation de la force de traînée sur les particules dans des zones à hautes pressions convergentes-divergentes des parois ondulées. Ceux-ci ont indiqué des changements notables dans le régime de fluidisation en remplaçant les parois planes par des parois ondulées et ont en outre révélé que les zones cou étaient responsables de la création des instabilités comparativement aux zones hanche. La moyenne dans le temps des contours de la fraction volumique simulée de gaz a corroboré avec les résultats expérimentaux que le CWBFB a offert la meilleure distribution de gaz par rapport à FWBFB. Les profils axiaux de la moyenne dans le temps de la fraction volumique solide simulée ont montré que CWBFB réduit de façon nette la HDT.With the endeavor of approaching an ideal allothermal gasifier, recently our group proposed a reactor concept of allothermal cyclic multi-compartment bubbling fluidized beds for biomass gasification with steam. The concept consisted of multiple intercalated parallelepipedic slim gasification and combustion compartments to enhance unit heat integration and thermal efficiency while preventing contact between flue gas and syngas to generate a N2-free high-quality biosyngas. However, the efficiency of contacting between gas and particles in bubbling fluidized beds is dictated to a large extent by the bubble dynamics which impacts mixing, heat and mass transfers. Literature showed that the decrease in clearance between flat walls for slim fluidization enclosures or in diameter for cylindrical vessels would make fluidized beds very sensitive to wall effects and prone to operate in slug flow regime. Since the occurrence of slugging in multi-compartment slim beds could reduce their thermal and chemical efficiency, the objective of current work was to devise suitable strategies in treating the incipient bubbles to suppress the slugging behavior of bed. By considering the effect of walls on bubble growth, we recently employed corrugated plates as separating walls in slim multi-compartment gas solid fluidized beds. Thorough analyses of bubble dynamics and wall-to-bed heat transfer in flat- (FWBFB) and corrugated- (CWBFB) wall bubbling fluidized beds were performed for a variety of wall declinations and operating conditions covering a range of corrugation angles (θ=120o, 90o), average inter-wall clearances (C), initial rest bed heights (Hi) and ratios of gas superficial velocity to minimum bubbling velocity, Ug/Umb. It was observed that gas flowrate required to achieve the incipient bubbling condition was lower in case of CWBFB. A network of neck (minimum clearance) and hip (maximum clearance) locations in CWBFB also promoted bubbles breakup, higher bubble frequency, lower bubble rise velocity and thus all converging into a better gas distribution. CWBFB offered stable gas-solid fluidization operation and lower transport disengagement height as compared to FWBFB. During the experimental work, digital image analysis technique and fast response heat flux probes were employed to study the effects of operating and geometrical parameters on bubble dynamics and wall-to-bed heat transfer. Two artificial neural network correlations valid both for FWBFB and CWBFB were recommended for the estimation of bubble frequency and size (equivalent diameter). Full 3-D transient Euler-Euler CFD simulations with kinetic theory of granular flow were also carried out which helped shaping an understanding of the effects of corrugated walls on increasing the drag force on particles in the converging-diverging high-pressure zones in corrugated walls. The dynamic fluctuations in the simulated solid phase volume fraction, granular temperature and granular pressure were monitored to determine their standard deviations. These revealed notable shifts in the fluidization regime by replacing flat walls with corrugated walls and further revealed that necks were responsible for inception of instabilities as compared to hips. Time averaged contours of simulated gas volume fraction corroborated with experimental findings that CWBFB offered better gas distribution as compared to FWBFB. Axial profiles of simulated time averaged solid volume fraction and granular temperature showed that CWBFB significantly reduced the transport disengagement height as compared to FWBFB

Dynamic process modeling and hybrid intelligent control of ethylene copolymerization in gas phase catalytic fluidized bed reactors

BACKGROUND: Polyethylene (PE) is the most extensively consumed plastic in the world, and gas phase‐based processes are widely used for its production owing to their flexibility. The sole type of reactor that can produce PE in the gas phase is the fluidized bed reactor (FBR), and effective modeling and control of FBRs are of great importance for design, scale‐up and simulation studies. This paper discusses these issues and suggests a novel advanced control structure for these systems. RESULTS: A unified process modeling and control approach is introduced for ethylene copolymerization in FBRs. The results show that our previously developed two‐phase model is well confirmed using real industrial data and is exact enough to further develop different control strategies. It is also shown that, owing to high system nonlinearities, conventional controllers are not suitable for this system, so advanced controllers are needed. Melt flow index (MFI) and reactor temperature are chosen as vital variables, and intelligent controllers were able to sufficiently control them. Performance indicators show that advanced controllers have a superior performance in comparison with conventional controllers. CONCLUSION: Based on control performance indicators, the adaptive neuro‐fuzzy inference system (ANFIS) controller for MFI control and the hybrid ANFIS–proportional‐integral‐differential (PID) controller for temperature control perform better regarding disturbance rejection and setpoint tracking in comparison with conventional controllers. © 2019 Society of Chemical Industr

Process Analytics from Passive Acoustic Emissions Monitoring during Fluidized Bed Pellet Coating in Pharmaceutical Manufacturing

Piezoelectric microphones were attached to a top spray fluidized bed to provide valuable process signatures. Relationships were developed between sound waves and conditions within the fluidized bed to relay critical quality and performance information. Deep learning analytics were used to extract valuable information from experimental data. Advancements in passive acoustic emissions monitoring will play a key role in optimizing pharmaceutical manufacturing pathways to ensure drug quality and performance

Developing a Smart Proxy for Fluidized Bed Using Machine Learning

Using fossil fuel which has been grown dramatically during the recent century, causes an increase in greenhouse gas emission. The global warming issue pushes the engineers toward the cleaner type of energy like Hydrogen. Coal gasification is one of the cheapest methods to obtain Hydrogen. Coal gasification is a special case of more general problem called fluidized bed. In order to design and optimize a gasification process, a deep understanding of multiphase flow in a gasifier is needed. MFiX is a commercial multi-phase flow simulator which has been used to simulate the gas and solid transport and reaction in the gasifier using Computational Fluid Dynamics (CFD). Although simulating multiphase flow using commercial CFD software has a lot of flexibilities, it is really time-consuming and some other way could be implemented to reduce the run time. The effort of this project is to develop an alternate method to perform the same analysis but with much lower computational cost. A data-driven approach is used to build a smart proxy by employing the knowledge of Artificial Intelligence (AI) and Data Mining (DM).;In this project, a smart proxy will be developed to study and analyze the fluidized bed problem. This smart proxy is then will be used as a replicate of the CFD solver, with a good accuracy and faster speed. This proxy needs an incredible less amount of time in comparison to the CFD solver with a reasonable error (less than 10%). MATLAB neural network toolbox is used for training.;The goal of this project is to prove the concept of using AI&DM; for computational fluid dynamics especially predicting multiphase flow. Multiphase flow has a wide range of application in petroleum industry such as multi-phase flow in the wellbore, surface lines, and hydraulic fracturing such as proppant transport in the hydraulic fracture. This project opens a new way to accelerate the fluid dynamics analysis and reduce its costs

Biomass gasification for syngas and biochar co-production: Energy application and economic evaluation

Syngas and biochar are two main products from biomass gasification. To facilitate the optimization of the energy efficiency and economic viability of gasification systems, a comprehensive fixed-bed gasification model has been developed to predict the product rate and quality of both biochar and syngas. A coupled transient representative particle and fix-bed model was developed to describe the entire fixed-bed in the flow direction of primary air. A three-region approach has been incorporated into the model, which divided the reactor into three regions in terms of different fluid velocity profiles, i.e. natural convection region, mixed convection region, and forced convection region, respectively. The model could provide accurate predictions against experimental data with a deviation generally smaller than 10%. The model is applicable for efficient analysis of fixed-bed biomass gasification under variable operating conditions, such as equivalence ratio, moisture content of feedstock, and air inlet location. The optimal equivalence ratio was found to be 0.25 for maximizing the economic benefits of the gasification process

Environmental friendly fluidized bed combustion of solid fuels: a review about local scale modeling of char heterogeneous combustion

Purpose: Fluidized bed combustion is currently intensively developed throughout the world to produce energy from several types of solid fuels, while significantly reducing pollutant emissions with respect to conventional combustion units. Accurate models must be formulated at both bed and particle levels to operate efficiently such units, since local phenomena such as particle temperature and combustion rate are crucial aspects for process improvement and control. In this sense, this article proposes a classification of local scale models to represent the evolution of char heterogeneous combustion of any carbonaceous particles. Methods: Existing models are described and classified based on the characteristics of the governing equations, the thermal behavior of the gas and solid phases and the description of both the burning particle and the surrounding gas, under a heterogeneous or pseudo-continuous assumption. Criteria for choosing one model instead of others are also considered, depending on the case. The so-called Intrinsic Reactivity Models are described in detail for evaluating the pertinence of their simulated results. The use of CFD to build a simulation scheme of the solid combustion process at local scale is also presented and discussed. Results: A complete description of the solid fuel burning process is given, along with useful information concerning the evolution of different variables, such as particle internal temperature that governs the reaction rate and gas composition. Conclusions: This comparative analysis gives a strong basis to select the appropriate modeling approach. Finally, recommendations are proposed for model application and future development.Fil: Mazza, German Delfor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; ArgentinaFil: Soria, Jose Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; ArgentinaFil: Gauthier, Daniel. Centre National de la Recherche Scientifique; FranciaFil: Reyes Urrutia, Ramón Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; ArgentinaFil: Zambon, Mariana Teresa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; ArgentinaFil: Flamant, Gilles. Centre National de la Recherche Scientifique; Franci

Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review

Solar thermal systems (STS) are efficient and environmentally safe devices to meet the rapid increasing energy demand now a days. But it is very important to optimize their performance under required operating condition for efficient usage. Hence intelligent system-based techniques like artificial neural network (ANN) play an important role for system performance prediction in accurate and speedy way. In present paper, it is attempted to scrutinize the approach of ANN as an intelligent system-based method to accurately optimize the performance prediction of different solar thermal systems. Here, 25 research works related to various solar thermal systems have been reviewed and summarized to understand the impact of different ANN models and learning algorithms on performance prediction of STS. Using ANN, a brief stepwise summary of researchers’ work on various STS like solar air heaters, solar stills, solar cookers, solar dryers and solar hybrid systems, their predictions (results) and architectures (network and learning algorithms) in the literature till now, are also discussed here. This paper will genuinely help future researchers overview the work concisely related to solar thermal system performance prediction using various types of ANN models and learning algorithm and compare it with other global methods of machine learning. Citation: Ahmad, A., Ghritlahre, H. K., and Chandrakar, P. (2020). Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review. Trends in Renewable Energy, 6, 12-36. DOI: 10.17737/tre.2020.6.1.0011

A Deep Neural Network Model of Particle Thermal Radiation in Packed Bed

Prediction of particle radiative heat transfer flux is an important task in the large discrete granular systems, such as pebble bed in power plants and industrial fluidized beds. For particle motion and packing, discrete element method (DEM) now is widely accepted as the excellent Lagrangian approach. For thermal radiation, traditional methods focus on calculating the obstructed view factor directly by numerical algorithms. The major challenge for the simulation is that the method is proven to be time-consuming and not feasible to be applied in the practical cases. In this work, we propose an analytical model to calculate macroscopic effective conductivity from particle packing structures Then, we develop a deep neural network (DNN) model used as a predictor of the complex view factor function. The DNN model is trained by a large dataset and the computational speed is greatly improved with good accuracy. It is feasible to perform real-time simulation with DNN model for radiative heat transfer in large pebble bed. The trained model also can be coupled with DEM and used to analyze efficiently the directional radiative conductivity, anisotropic factor and wall effect of the particle thermal radiation

Application of Machine Learning Models with Numerical Simulations of an Experimental Microwave Induced Plasma Gasification Reactor

This thesis aims to contribute to the future development of this technology by providing an in-depth literature review of how this technology physically operates and can be numerically modeled. Additionally, this thesis reviews literature of machine learning models that have been applied to gasification to make accurate predictions regarding the system. Finally, this thesis provides a framework of how to numerically model an experimental plasma gasification reactor in order to inform a variety of machine learning models