Model predictive control utilizing fuel and moisture soft-sensors for the BioPower 5 combined heat and power (CHP) plant

This paper presents a model predictive control (MPC) strategy for efficient energy production in BioGrate boiler. In addition to compensating for the main disturbances caused by variations in fuel quality such as fuel moisture content, and variations in fuel feed, this strategy models water evaporation, and models and controls the fuel bed height of the grate. Usually, combustion power in a furnace have been estimated by utilizing oxygen consumption. There is however a need for more accurate prediction and control of combustion power, which is greatly affected by the fuel bed height and fuel moisture content. It is shown that water evaporation and thermal decomposition of dry fuel can be estimated by utilizing fuel moisture soft-sensor and oxygen consumption calculations respectively. As a result, the primary air can be adjusted to produce the necessary combustion power, and the power output of the boiler can be accurately predicted. This enables efficient stabilization of plant operations. To verify the model, experiments were performed at a BioPower 5 CHP plant, which utilizes BioGrate combustion technology to enable the use of wet biomass fuels with a moisture content as high as 65%. Then the MPC strategy was compared with the currently used control strategy. Finally, the results are presented, analyzed, and discussed.