The development of efficient operation and control of a biomass boiler requires extensive knowledge of the combustion process inside the boiler furnace However, it is not possible to obtain the required knowledge through process measurements because the high temperatures and aggressive environment inside the furnace prevent taking accurate sensor readings. Instead, the process can be studied with the help of mathematical modeling. This paper describes dynamic modeling of bed combustion in a BioGrate boiler furnace. The developed dynamic model is heterogeneous, including solid and gas phases and corresponding reactions. The model is used for process phenomena investigation; the results are presented and discussed. Keywords

No abstract

Sirkka-Liisa. 2015. Experiments and modeling of fixed-bed debarking residue pyrolysis: The effect of fuel bed properties on product yields. Chemical Engineering Science. Volume 138. 581-591. ISSN 0009-2509 (printed This paper presents a study on the fixed-bed pyrolysis of debarking residue obtained from Norway spruce. 9Analysis is based on the dynamic model of packed bed pyrolysis which was calibrated by determining 10 appropriate reaction rates and enthalpies to match the model predictions with the experimental data. The model 11 comprises mass, energy and momentum equations coupled with a rate equation that describes both the primary 12 and secondary pyrolysis reactions. The experiments used for the model calibration determined the yields of 13 solid, liquid and gaseous pyrolysis products as well as their compositions at three distinct holding temperatures. 14 Subsequently, the dynamic model was used to predict the product yields and to analyze the underlying 15 phenomena controlling the overall pyrolysis reaction in a fixed-bed reactor.

Increasing utilization of intermittent energy resources requires flexibility from energy boilers which can be achieved with advanced control methods employing dynamic process models. The performance of the model-based control methods depends on the ability of the underlying model to describe combustion phenomena under varying power demand. This paper presents an approach to the simplification of a mechanistic model developed for combustion phenomena investigation. The aim of the approach is to simplify the dynamic model of biomass combustion for applications requiring fast computational times while retaining the ability of the model to describe the underlying combustion phenomena. The approach for that comprises three phases. In the first phase, the main mechanisms of heat and mass transfer and limiting factors of the reactions are identified in each zone. In the second phase, each of the partial differential equations from the full scale model are reduced to a number of ordinary differential equations (ODEs) defining the overall balances of the zones. In the last phase, mathematical equations are formulated based on the mass and energy balances formed in the previous step. The simplified model for online computations was successfully built and validated against industrial data.

This paper presents a novel, effective method to handle critical sensor faults affecting a control system devised to operate a biomass boiler. In particular, the proposed method consists of integrating a data reconciliation algorithm in a model predictive control loop, so as to annihilate the effects of faults occurring in the sensor of the flue gas oxygen concentration, by feeding the controller with the reconciled measurements. Indeed, the oxygen content in flue gas is a key variable in control of biomass boilers due its close connections with both combustion efficiency and polluting emissions. The main benefit of including the data reconciliation algorithm in the loop, as a fault tolerant component, with respect to applying standard fault tolerant methods, is that controller reconfiguration is not required anymore, since the original controller operates on the restored, reliable data. The integrated data reconciliation-model predictive control (MPC) strategy has been validated by running simulations on a specific type of biomass boiler-the KPA Unicon BioGrate boiler.