Second, we improved the controls by upgrading the software configuration in the Emerson DeltaV control system. Essentially, we used mathematical and model predictive tools to do what our best operators were doing, but with added speed, precision and repeatability.
Analyzing the Control Problem
Before embarking on a major change to the boiler control strategy, it was important to first understand the dynamics of the new OFA system and the other mechanical modifications we made to the boiler. We wanted a holistic control system that included an integrated solution of hardware and software.
Jansen's engineers had already done an extensive study of the air/fuel requirements when designing the OFA system and the ductwork, so we had an excellent foundation from which to start.
We retained Emerson Process Management to do a survey of our boiler and new air system. Emerson has engineers that specialize in industrial powerhouse controls and multi-fuel boilers. The Emerson consultants worked with Jansen and Minnesota Power engineers to analyze the process performance and make recommendations.
From the survey, Emerson identified the need for a combustion control strategy specifically designed to address the challenges of biomass burning. Control functionality was needed to automatically handle variable Btu in real time, chase load swings with wood fuel primarily and prioritize wood use in general while operating within all constraints.
The decision was made to implement Emerson's SmartProcess Boiler solution for multi-fuel boilers. This control strategy would leverage the model predictive control (MPC) tools called Predict-Pro within the DeltaV control system, and provide full-automatic wood-burning optimization. No changes would be needed in the DeltaV hardware for boiler control.
Simulating the System
After the engineering survey was completed, Emerson was engaged to configure a SmartProcess Boiler implementation for our boiler. This provides a Btu-based combustion control strategy that eliminates the traditional air-to-fuel curves and allows use of variable fuels such as wood to be improved.
The Btu from wood that is available for combustion is mathematically derived in real time by the control strategy. With this, adjustments to operation of the boiler can be made automatically by the controls to compensate for wood-fuel quality variations and wood supply changes or interruptions. The control strategy will automatically make air adjustments or pick up coal when needed to maintain stable operation.
In accomplishing Btu-based control, the SmartProcess Boiler configuration also makes use of PredictPro. Having advanced control functionality in the DeltaV allowed the boiler optimization to be implemented right at the controller level to simplify training and maintenance.
DeltaV also has a simulation environment, which allowed us to test our new advanced control strategies 100 times faster than real time. We set up our control model on a DeltaV system at the local Emerson representative, Novaspect Inc. (www.novaspect.com) in Grand Rapids, Minn..
SmartProcess Boiler allowed us to set up various control scenarios, including firing mostly biomass, firing all coal, firing mixtures of biomass and coal, and changing economic scenarios based on the price of coal or biomass. Although the cost of biomass and coal is usually similar, prices can vary, and sometimes coal becomes an economically attractive fuel, but using too much coal carries high emissions costs. All of these factors enter into the advanced control calculations.
We could also simulate changing process conditions, such as swings in steam demand from the paper mills and varying ambient temperatures. For example, when the temperature drops to -40 ºF in the winter, the paper mills obviously use much more steam than in the summer.
Operators were naturally quite wary of transferring control to a fully automated system. But running test simulations prior to start-up convinced them that the system was indeed capable of automatic control through all manner of changes in steam demand, fuel quality and other factors.
The simulations showed that the new control system—theoretically, of course—was capable of handling process upsets, reducing emissions, reducing ash carryover, starting up the boiler automatically and adjusting for the varying Btu content of wood. The acid test would occur when we installed the system and ran it on the actual boiler.
Installation and Start-Up
Installation of the Predict-Pro and SmartProcess Boiler optimization solution into the boiler's existing DeltaV control system was simple—we just unlocked the advanced control license and downloaded the control configuration program that had been developed and tested.
The system came on line, we discovered a few glitches, did some tuning of the control algorithms, and everything was up and running within a few days.
The control system now operates the air systems in cascade mode; that is, air flow adjusts automatically according to demand and fuel conditions, and under-grate air is now used to improve the unit's speed of response to load swings. Fuel control remains in cascade mode, and fuel is controlled according to cost priorities.
The Jansen OFA system is leveraged by the DeltaV SmartProcess Boiler controls to deliver proper air placement for optimized combustion throughout the entire load range, and boiler start-up is also automated.