Catalytic Flow Reversal Reactor Research Articles

A study on thermal combustion of lean methane–air mixtures: Simplified reaction mechanism and kinetic equations

Homogeneous combustion of lean methane–air mixtures occurs in both thermal and catalytic reverse-flow reactors. The modeling and design of such reactor requires a reliable and relatively simple kinetic model of the combustion. Although a number of detailed descriptions of the kinetics of homogeneous combustion are available in the literature (including those which take into account complex free-radical reactions), their practical usefulness is doubtful. In the present study a simple one- or two-stage model is proposed which directly describes global kinetics and neglects intermediates and radicals. Special attention is paid to the mechanism of the possible formation of carbon monoxide. The studies were done both for the combustion in the free space and for the process taking place over a monolith packing.

LQ-feedback control of a reverse flow reactor

In this paper, a linear-quadratic controller is formulated for a catalytic flow reversal reactor (CFRR) using an infinite-dimensional Hilbert space representation of the system. The controller is developed on the basis of the catalytic reactor model with unidirectional flow and is formulated to keep the distribution of the temperature along the axis of the reactor at stationary state by using the fluid flow velocity. To formulate the controller, a linear infinite-dimensional state space description is used. Using the infinite-dimensional state space representation, a state LQ-feedback operator is computed via the solution of a Riccati differential equation. The controller is formulated and tested numerically for the catalytic combustion of lean methane emissions.

Homogeneous vs. catalytic combustion of lean methane—air mixtures in reverse-flow reactors

To carry out a comparative assessment of a recently proposed idea of using thermal flow-reversal reactors (TFRR) for mine ventilation air, the results for the catalytic flow-reversal reactor (CFRR) investigated within the European Project (2003) are briefly presented. Next, experimental investigations of thermal combustion are presented in this paper. These consisted of the kinetic study of homogeneous combustion in the pelletized bed and in the monolith. Kinetic equations for the two cases are derived and discussed. Experimental autothermal reverse-flow operation in a laboratory setup was performed. Due to the high heat capacity of the wall and insulation of the pelletized bed reactor, with considerable heat losses to the surroundings, autothermal operation was successful only in the monolithic reactor. It is finally concluded that the thermal combustion can be competitive compared with the catalytic oxidation.

Characteristics‐Based Model Predictive Control of a Catalytic Flow Reversal Reactor

AbstractThis paper describes the formulation and tuning of a model‐based controller for a catalytic flow reversal reactor (CFRR). A plug flow non‐linear pseudo‐homogeneous mathematical representation of the process is used to model the mass and energy transport phenomena for the model‐based controller. A combination of the method of characteristics and model predictive control (MPC) technology is used to formulate the controller (Shang et al., Ind. Eng. Chem. Res. 43(9) 2140–2149 (2004)). Mass extraction from the midsection of the reactor is used as the manipulated variable. Numerical simulations are used to show the performance of the formulated controller. The performance of the controller is evaluated on a simulated catalytic flow reversal reactor unit for combustion of lean methane streams for reduction of greenhouse gases emissions.

Repetitive model predictive control of a reverse flow reactor

This paper deals with the control of a catalytic reverse flow reactor (RFR) used for methane combustion. The periodic flow reversals effected on the system makes it both continuous and discrete in nature (i.e., a hybrid system). Control of this system is challenging due to the unsteady state behavior of the process along with its mixed discrete and continuous behavior. Although model predictive control (MPC) is proven to be a powerful technique for several processes it becomes less effective in systems such as the RFR where the model prediction errors and the effect of disturbances on the plant output repeat from time to time. In such cases, control can be improved if the repetitive error pattern is exploited. A novel repetitive model predictive control (RMPC) strategy, that combines the basic concepts of iterative learning control (ILC) and repetitive control (RC) along with the concepts of MPC, is proposed for such systems. In the proposed strategy, the state variables of the model are reset periodically along with predictive control action such that the process follows the reference trajectory as closely as possible. The results obtained prove that the RMPC approach provides an excellent performance for the control of the RFR.

CONTROL OF A CATALYTIC FLOW REVERSAL REACTOR MODEL BY LINEAR QUADRATIC REGULATOR

A linear infinite dimensional state space representation of a catalytic flow reversal reactor is used to formulate a state LQ-feedback operator via the solution of a Riccati differential equation. Flow velocity is used to keep the temperature and mole fraction of reactant in the reactor moving along a desired stationary state. Numerical simulations are used to show the performance of the formulated controller.

Optimization of a flow reversal reactor for the catalytic combustion of lean methane mixtures

This paper describes a parametric study of a catalytic flow reversal reactor used for the combustion of lean methane in air mixtures. The effects of cycle time, velocity, reactor diameter, insulation thickness, thermal mass and thermal conductivity of the inert sections are studied using a computer model of the system. The effects on the transient behaviour of the reactor are shown. Emphasis is placed on the effects of geometry from a scale-up perspective. The most stable system is obtained when the thermal mass of the inert sections is highest, while thermal conductivity has only a minor effect on reactor temperature. For a given operation, the stationary state depends on the combination of velocity and switch time. Provided that complete conversion is achieved, highest reactor temperature is achieved with the highest switch time. The role of the insulation is not only to prevent heat loss to the environment, but also to provide additional thermal mass. During operation heat is transfer to and from the insulation. The insulation effect leads to higher reactor temperature up to a maximum thickness. The insulation effect diminishes as the reactor diameter increases, and results in higher temperatures at the centreline.

MODEL PREDICTIVE CONTROL OF A CATALYTIC FLOW REVERSAL REACTOR WITH HEAT EXTRACTION

This paper presents the formulation of a controller for a Catalytic Flow Reversal Reactor (CFRR) with heat extraction. The controller is based on the Model Predictive Control (MPC) concept. The MPC scheme uses a model that assumes plug flow and neglects radial gradients in the reactor but accounts for the two phases within the reactor. The prediction of the future output behavior from the model is obtained by using the Method of Characteristics as proposed by Shang et al. (2004) for convection dominated distributed parameter systems. The formulated controller is applied to a CFRR unit for the catalytic oxidation of fugitive lean methane mixtures. The objective of the control algorithm is to maintain stable reactor operation, while extracting the maximum amount of useful energy by hot gas removal from the mid-section of the reactor. Simulations are used to show the performance of the designed controller.

Combustion of methane lean mixtures in reverse flow reactors: Comparison between packed and structured catalyst beds

The scope of this work is to compare systematically the performance of particle beds and monolithic beds in catalytic reverse flow reactors used for combustion of lean methane/air mixtures, using alumina-supported palladium as catalyst. Different values of gas surface velocity (0.1–0.3 m/s), particle diameter (3–6 mm, for particle bed), cell density (200–400 cpsi, for structured bed) and catalyst/inert ratio (0.4–1) were used for the simulation of the combustion of 3500 ppm methane in both kinds of reverse flow reactor. An unsteady one-dimensional heterogeneous model has been developed and solved using a MATLAB code. The model, physical parameters and transport properties used had been experimentally validated in a previous work, operating with a particle bed reverse flow reactor. Results obtained indicate that the reverse flow reactor is more stable when the catalyst particle beds are use, although the difference with the monolith bed decreases as surface velocity increases. In contrast, pressure drops in the bed are higher for the particle bed.

Observer based multivariable control of a catalytic reverse flow reactor: comparison between LQR and MPC approaches

This paper is intended to show the comparison of two closed-loop controllers performances for a catalytic reverse flow reactor (RFR): a linear quadratic regulator (LQR) and a model predictive control (MPC) strategy that both use a high-gain observer. The aim of the RFR is to reduce, by catalytic reaction, the amount of volatile organic compounds (VOCs) released in the atmosphere. The particularity of this process is that the gas flow inside the reactor is periodically reversed in order to trap the heat released during the reaction inside the process. Very few papers are dealing with the control of the RFR. The new multivariable optimal control issue tackled here is to confine the hot spot temperature within two temperature limits, in order to ensure complete conversion of the pollutant and to prevent catalyst overheating, while optimizing the process yield and the consumption of electrical power. Both control laws aim to optimize the tuning of the dilution and the internal electric heating. The model considered here for control of the RFR is obtained from a countercurrent pseudo-homogeneous partial differential equation (PDE) model. The feed concentration (acting as an important input disturbance) and the temperature profile in the RFR are estimated on-line using a high-gain observer based on three temperatures measurements. In order to maintain the safe use of the reactor, the estimate state is injected in the LQR whereas the MPC is based on the estimated input disturbance. Simulation results allow comparing performances of the LQR and the MPC.

Some Considerations on the Design and Operation of High-Temperature Catalytic Reverse-Flow Reactors

Reverse-flow operation of catalytic reactors is known to result in very efficient regenerative heat integration. While reverse-flow operation is in particular ideally suited for high-temperature short-contact-time reactions, the application of this reactor concept under such extreme reaction conditions also requires particular care in the design and operation of the reactor. We report results from an experimental study on some of the main design and operating parameters for high-temperature catalytic reverse-flow reactors. Results for reverse-flow operation are compared to conventional stationary reactor operation. Temporal and spatial temperature profiles at periodic steady state are presented, and the influence of the structure of the catalyst and inert zones, the role of homogeneous reactions, and the frequency of flow switching are investigated and discussed.

Multivariable model predictive control of a catalytic reverse flow reactor

This paper is devoted to the multiple input multiple output (MIMO) model predictive control (MPC) of a catalytic reverse flow reactor (RFR). The RFR aims to reduce, by catalytic reaction, the amount of volatile organic compounds (VOCs) released in the atmosphere. The peculiarity of this process is that the gas flow inside the reactor is periodically reversed in order to trap the heat released during the reaction. The objective of this paper is to propose a solution to avoid the limitation seen in a previous study for the single input single output (SISO) control case. It was dealing with the impossibility to avoid degradation of the catalytic elements due to the excessive heating induced by the exothermic reaction. In order to overcome this issue, a ratio of the inlet (cold) gas flow is now bypassed into the central zone. This allows introducing a second manipulated variable: the dilution rate. The phenomenological model considered here for the MIMO MPC of the RFR is obtained from a rigorous first principles modeling. The resulting accurate nonlinear partial differential equation (PDE) model can be a drawback for the model based controller implementation. To overcome this issue, we use a MPC strategy previously developed: it combines a two phase approximation of the PDE model in an internal model control (IMC) structure. This strategy allows using a less accurate model and a less time-consuming control algorithm. Finally, efficiency of the control approach is shown in simulation.

Multivariable model predictive control of a catalytic reverse flow reactor

This paper is devoted to the multiple input multiple output (MIMO) model predictive control (MPC) of a catalytic reverse flow reactor (RFR). The RFR aims to reduce, by catalytic reaction, the amount of volatile organic compounds (VOCs) released in the atmosphere. The peculiarity of this process is that the gas flow inside the reactor is periodically reversed in order to trap the heat released during the reaction. The objective of this paper is to propose a solution to avoid the limitation seen in a previous study for the single input single output (SISO) control case. It was dealing with the impossibility to avoid degradation of the catalytic elements due to the excessive heating induced by the exothermic reaction. In order to overcome this issue, a ratio of the inlet (cold) gas flow is now bypassed into the central zone. This allows introducing a second manipulated variable: the dilution rate. The phenomenological model considered here for the MIMO MPC of the RFR is obtained from a rigorous first principles modeling. The resulting accurate nonlinear partial differential equation (PDE) model can be a drawback for the model based controller implementation. To overcome this issue, we use a MPC strategy previously developed: it combines a two phase approximation of the PDE model in an internal model control (IMC) structure. This strategy allows using a less accurate model and a less time-consuming control algorithm. Finally, efficiency of the control approach is shown in simulation.

Model predictive control of a catalytic reverse flow reactor

This paper deals with the control of a catalytic reverse flow reactor. The aim of this process is to reduce, by catalytic reaction, the amount of volatile organic compounds (VOCs) released into the atmosphere. The peculiarity of this process is that the gas flow inside the reactor is periodically reversed in order to trap the heat released during the reaction. This allows use of the reactor in a heat saving mode. The goal of this work is to provide a model predictive control (MPC) framework to significantly enhance the poor overall performance currently obtained through the actual control strategy. It is directly addressed for the nonlinear parabolic partial differential equations (PDEs) that describe the catalytic reverse flow reactor. In the context of the application of MPC to this particular distributed parameter system, we propose a method that aims to reduce the online computation time needed by the control algorithm. The nonlinear model is linearized around a given operating trajectory to obtain the model to be solved on-line in the approach. MPC strategy combined with internal model control (IMC) structure allows using less accurate and less time-consuming control algorithm. Method efficiency is illustrated in simulation for this single-input-single-output system.

Transient behaviour and start-up of periodic flow reversal reactors for catalytic decontamination of waste gases

A full dynamic one-dimensional model for the simulation of the catalytic reverse-flow reactor and a code for its solution have been developed. A wide set of simulations has been carried out for the combustion of lean mixtures of methane. The influence of changes in the design parameters (bed length, cycle period and fraction of inert material) and in the operating parameters (inlet concentration and flow rate) on the transient starting has been investigated in detail.

Operation of a catalytic reverse flow reactor for the purification of air contaminated with volatile organic compounds

AbstractCatalytic oxidation in a reverse flow reactor is an attractive process for the decontamination of air polluted with volatile organic compounds (VOCs). In this paper several aspects of operating this type of reactor for air purification under strongly varying conditions will be discussed.For a successfit operation of such reactor a minimum amount of combustibles is required, and a simple theory is developed to predict this minimum value. This minimum amount is strongly influenced by the reaction kinetics, the heat transport in the packed bed and the adiabaticity of the reactor. To cope with a too low temperature level in the reactor for a complete conversion, an electrical heating device should be installed in the centre of the reactor bed to increase locally the temperature rapidly and efficiently. To control the maximum temperature in case of too high concentrations, extra air should be added to the feed.