A computational framework for combustion of powdered solid fuels in a MILD reactor using a novel devolatilisation model. Issue 3 (16th April 2022) - Ambatipudi, Mani Kalyani; Sivakumar, Varunkumar
Abstract : In this paper, a computational framework for modelling the thermochemical conversion of powdered solid fuels in a MILD reactor is presented. Specific fuels of interest are high ash coals and lignocellulosic biomass. The novelty of the current framework lies in recognising that the devolatilisation process of charring fuels like biomass and coal is surface heat transfer limited at heating rates relevant to practical systems. The in-house unified ignition devolatilisation model developed for pelletised biomass in a packed bed configuration is extended to powdered fuels. Comprehensive modelling of a 50 kW solid fuel MILD reactor is accomplished by integrating the particle model with a commercially available CFD code. Unsteady Lagrangian particle tracking in an Eulerian gas phase is used to resolve the temperature profile in the reactor. Heat loss from the reactor walls, an essential parameter for obtaining accurate temperature profiles, is obtained by striking an energy balance over the entire domain using experimental data (from the earlier work of the authors). Results of numerical simulations with two different fuels (powdered coal and groundnut shells) covering a wide range of flow conditions obtained with the framework are presented in the current study. Normalised spatial temperature variation and the O 2 mole fraction profiles with the two fuels indicate that the reactor operates in MILD mode. The temperature profiles predicted with the framework are in satisfactory agreement with the experimental results. The modelling approach successfully predicts the operational regimes of the reactor. A noteworthy feature of the new framework is its applicability in predicting devolatilisation of different fuels without the need for any adjustable constants. This enables the use of this framework as a predictive design tool.