20-06-2011, 12:40 PM
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1.1 INTRODUCTION
Overall optimization of a coal-fired power plant is a highly complex process. One must first decide what constitutes optimal performance. Obvious answers include maximum thermal efficiency, lowest possible emissions, lowest possible cost, readily marketable by-products, and maximum system availability for power generation. In reality, these goals—and others—are interrelated. In some cases, however, these optimization goals are at odds with each other. For example, high excess air will result in better carbon burnout and less carbon monoxide but will also result in higher emissions of nitrogen oxides (NOX). These interactions must be kept in mind and addressed with any optimization program.
There are a number of relatively fixed items that affect overall plant operation. These include boiler design, cooling water conditions, burner type, design steam conditions, and environmental control systems that capture and remove particulate matter, sulfur dioxide (SO2), NOX, and mercury. Coal quality is also a major factor that affects plant performance. High moisture and/or ash content decreases efficiency and increases wear and power requirements on the pulverisers. High sulphur content results in more reagent consumption and increased by-product generation.
The benefits of optimizing the overall process of generating power from coal are significant. Efficiency is increased, total maintenance costs are reduced, emissions are decreased, and reliability is improved. While the greatest benefit can be achieved by optimizing the overall operation, important benefits can also be achieved by optimizing one or more of the factors that contribute to the overall efficiency of the plant.
Many optimizations can yield substantial positive results. For instance, use of lignite and sub-bituminous coals, which are high in moisture, lowers the boiler efficiency, increases the load on the pulverisers, and increases flue gas volume. Drying the coal before it is fed to the preparation system is generally not practical due to the energy required. Switching to a higher quality coal, even if available, is often not practical either due to cost or to the fact that a boiler designed for a specific coal may not function as well with other coals. If such a switch is made, the unit may need to be de-rated. So, if drying the coal can be economically integrated into the overall power plant process, potential benefits are substantial.
There are several obvious systems that can be optimized independently and result in better performance. Some involve simply upgrading a specific piece of equipment.
For example, a refurbished steam turbine will improve heat rate and result in less fuel consumption per megawatt (MW). The cost of electricity is then reduced, as are the emission rates of some pollutants.
In some cases, optimizing one aspect of boiler operation can have several benefits. For example, during boiler operation, ash slowly builds up on boiler tubes. This causes reduced heat transfer to the boiler feed water and steam which results in lower efficiency and higher NOX emissions. The build up is removed by blowing it off the tubes with high-pressure steam. But when soot blowing occurs, the electrostatic precipitator (ESP), or bag house, is temporarily overwhelmed by the high particulate load at the inlet. Soot blowing is traditionally done on a set schedule rather than as needed. This results in some boiler sections accumulating excessive ash on the tubes while others, having little ash build up, are serviced when it is not required. Optimized soot blowing can solve these problems by using sensors and artificial intelligence (AI) software to determine when a particular section of the boiler needs to have the ash removed from the tubes, thus minimizing steam consumption (and improving heat rate), reducing the frequency of a high particulate load in the flue gas, and reducing NOX formation.
As one would expect, optimizing the operation of multiple components normally gives better results than optimizing one aspect of the operation. Maximizing the overall performance of multiple pieces of equipment does not normally have an adverse effect on the other power plant components. However, when using AI/neural network systems to optimize multiple aspects of power plant operation, care must be taken to consider the possible negative impact on other parameters. This can best be accomplished by designing the software packages to communicate with each other through a management software package.
This document describes four optimization projects within the PPII and CCPI programs. The following are brief descriptions of the four projects:
In the Lignite Fuel Enhancement project, Great River Energy has installed a full-scale prototype dryer module to supply one-sixth of the coal required for a 546 MW unit. Results to date have shown improved performance in overall operation of this unit. In the next phase of this project, Great River Energy will design, construct, and perform full-scale, long-term operational testing on a complete set of dryer modules to supply all the coal needed for the full operation of this unit.
The Neural Network-Intelligent Soot blower (NN-ISB) project with the Tampa Electric Company (TECO) Big Bend Power Station is complete. This project showed that the concept of using a neural network system to optimize the soot blowing process is sound but that additional development and better equipment are needed. Mechanical problems with sensors and water cannons were encountered and overall results were affected by these issues. However, some benefit was obtained with respect to stack opacity and nitrogen oxides reduction.
In the Mercury Specie and Multi-Pollutant Control project, Pegasus Technologies will utilize state-of-the-art sensors and neural-network-based optimization and control technologies to maximize the proportion of mercury species that are easy to remove from the boiler flue. This project will demonstrate how integrating sensors, controls, and advanced analysis techniques into multiple facets of plant operation can lead to improved economics and environmental compliance.
With the Demonstration of Integrated Optimization Software project at Dynegy Midwest Generation’s Baldwin Energy Complex, NeuCo, Inc., is integrating and optimizing their software, SCR-Opt™, Combustion-Opt, Soot-Opt, Performance-Opt., and Maintenance-Opt. Process-Link is the integration software that coordinates these programs to achieve overall plant goals. The project is ongoing as of this time and results to date appear promising.
