The Use of Uncertainty Quantification and Numerical Optimization to Support the Design and Operation Management of Air-Staging Gas Recirculation Strategies in Glass Furnaces

Abstract

The reduction in energy consumption and the increasingly demanding emissions regulations have become strategic challenges for every industrial sector. In this context, the glass industry would be one of the most affected sectors due to its high energy demand and emissions productions, especially in terms of NOx. For this reason, various emission abatement systems have been developed in this field and one of the most used is the air staging system. It consists in injecting air into the upper part of the regenerative chamber on the exhaust gases side in order to create the conditions for combustion that reduces NOx emissions. In this work, the combined use of CFD with data analysis techniques offers a tool for the design and management of a hybrid air staging system. Surrogate models of the bypass mass flow rate and uniformity index in the regenerative chamber have been obtained starting from DoE based on different simulations by varying the air mass flow rate of the two injectors located in a bypass duct that connects the two regenerative chambers. This model allows a UQ analysis to verify how the uncertainty of the air injectors can affect the bypass mass flow rate. Finally, an optimization procedure has identified the optimal condition for the best bypass mass flow rates and uniformity of the oxygen concentration in the chamber. High values of the mass flow rate of the pros injector and medium-low values for the cons injectors are identified as operating parameters for best conditions.

Aerodynamic Study of MotoGP Motorcycle Flow Redirectors

“…Another important advantage of the Taguchi method is that it is an open tool unrestricted by software and accessible to any end-user. The disadvantage is that the obtained solution is just close to the optimal, does not introduce constraints, and it is difficult to use it to solve multi-objective problems and uncertainty quantification [44,45].…”

Uncertainty Quantification Analysis of Exhaust Gas Plume in a Crosswind

“…A more representative analysis is performed with the introduction of a normal probability density function (pdf) for the input variables, which can more realistically simulate the uncertainty related to the gas velocity found at the exit of an exhaust funnel. An example is shown in Figure 16: it has a mean value centered in the range of (20 [m/s]) with a standard deviation of 2 [m/s], which is the estimated uncertainty of the variable, selected from the experience of previous works [37][38][39]; the aim is to verify the effect of a small deviation from the mean. These results confirm the hypothesis that a relatively small change in the vf can cause a significant change in the QoI and, consequently, in the plume evolution.…”

“…Computational fluid dynamics (CFD) is one of the latest disciplines where UQ techniques have been exploited with increasing relevance. The recent developments in response surface methodology (RSM), which is normally very effective for optimization in design processes [35,36], have also led the way for the application of UQ to this field [37][38][39]. Several UQ methods have been applied in stochastic fluid dynamics problems, including Monte Carlo (MC-whose accuracy is affected by the sampling point number), perturbation method, moment method and surrogated models.…”

Topology Optimization of Hydrodynamic Body Shape for Drag Reduction in Low Reynolds Number Based on Variable Density Method

“…As the fluid stream passes the topmost part of the cylinder, it tends to separate from the top surface and peel off in a clockwise motion as it approaches the rear end of the cylinder, ending up as a shed vortex which can be quantified [33] and represents a dangerous structural problem [34]. In relevant fluid dynamics problems, uncertainty quantification is often used to introduce the uncertainty into a mathematical and physical model to make it more realistic [35,36]. In the topology optimization of fluid, Borrvall and Peterson [37] develop a porous media model for the Stokes flow, which was soon extended to the Navier-Stokes flow [38,39].…”