JS energy Team has developed Advanced Combustion Control (ACC) solution that ensures highest performance of fossil-, oil-, waste- and biomass-fuel fired boilers. Based on analysis of systems equipment, boiler configuration and its operation, we tailor-make the ACC product for a specific boiler. Boilers firing single or multiple fuels can be handled, e.g. co-firing of coal and biomass (wood pallets, leftovers etc.).
The ACC solution is flexible in design with possibility of making a custom-made user interface directly in the DCS/PLC systems.
Principle
ACC solution applies available system measurements and uses them in intelligent real-time control. The information about the combustion process are gathered from different measuring systems and led into a mathematical model of a boiler.
By applying Model Predictive Control (MPC) technology that uses cost function in conjunction with specific combustion constraints (multi-criteria optimization), the optimal settings of the manipulated variables are identified and fed back into the plant DCS/PLC system.
MPC is used in a majority of existing multivariable control applications and it is technology of choice for many new advanced multivariable control applications. Its success rides on the increase of computing power with many important practical advantages. The MPC algorithm for the calculation of an optimal strategy of input process variables uses:
- Mathematical model of a system
- Historical process data
- Cost function over a controlled horizon
A general formula of a cost function can be expressed with the following equation:
The following figure schematically represents an idea of the MPC control technology.
Schematic representation of the Model Predictive Control Technology
ACC technology is thus based on iterative optimization procedure over a predicted horizon Hp, the result of which are optimal settings of manipulated variables - dampers, conveyor belt speeds, excess oxygen, over-fire air etc. At present time t the current state of the system is applied to calculate optimal strategy based on cost function for the future interval [t, t+Hc]. It is online calculation of optimal settings considering current and past measurements of controlled variables, desired goals and system limitations.
In such a way optimal operational settings are achieved at all times ensuring sustainable and efficient use of resources, thus reducing energy consumption and environmental pollution.
