Analytics supported by third party report
Posted by statsoftsa
Analytics supported by third party report
The non-profit Electric Power Research Institute (EPRI) recently conducted a study of the StatSoft technology to determine its suitability for optimizing the performance (heat-rate, emissions, LOI) in an older coal-fired power plant. EPRI ordered from StatSoft an optimization project to be conducted under scrutiny of their inspectors.
Using nine months worth of detailed 6-minute interval data describing more than 140 parameters of the process, EPRI found that process data analysis using STATISTICA is a cost-effective solution for optimizing the use of current process hardware to save cost and reduce emissions.
Overview of the Approach
StatSoft Power Solutions offer solution packages designed for utility companies, for optimizing power plant performance, increasing the efficiency, and reducing emissions. Based on over 20 years of experience in applying advanced data-driven, data mining optimization technologies for process optimization in various industries, these solutions will allow power plants to get the most out of their equipment and control systems, by leveraging all data collected at your site to identify opportunities for improvement, even for older designs such as coal-fired Cyclone furnaces (as well as wall-fired or T-fired designs).
Opportunities for Data Driven Strategies to Improve Powerplant Performance
Many (most) power generation facilities are collecting “lots of data” into dedicated historical process data bases. (such as OSI PI) However, in most cases, only simple charts and “after-the-fact” ad-hoc analyses are performed on a small subset of those data; most information is simply not used.
For example, for coal fired power plants, our solutions can help you identify optimum settings for stoichiometric ratio, primary/tertiary air flows, secondary air biases, distribution of OFA (overfired air), burner tilts and yaw positions, and other controllable parameters to reduce NOx, CO, and LOI, without requiring any re-engineering of existing hardware.
What is Data Mining? Why Data Mining?
Data Mining is the term used to describe the application of various machine learning and/or pattern recognition algorithms and techniques, to identify complex relationships among observed variables. These techniques can reveal invaluable insights when the data contain meaningful information which is “hidden” deep inside your data set, and cannot be identified with simple methods. Advanced data mining can reveal those insights by processing many variables and complex interrelations between them, all at the same time.
Unlike CFD, data mining allows you to model the “real world” from “real data,” describing your specific plant. Using this approach, you can:
- Identify from among hundreds or even thousands of input parameters those that are critical for low-emissions efficient operations
- Determine the ranges for those parameters, and combinations of parameter ranges that will result in robust and stable low-emissions operations, without costly excursions (high-emissions events, unscheduled maintenance and expensive generation roll-backs).
These results can be implemented using your existing closed-or-open loop control system to achieve sustained improvements in power plant performance, or you can use StatSoft MultiStream to create a state-of-the-art advanced process monitoring system to achieve permanent improvements.
How is this Different from “Neural Nets” for Closed Loop Control?
One frequently asked question is: How do these solutions differ from neural networks based computer programs that can control critical power plant operations in a closed loop system (an approach used at some plants, often with less than expected success)?
The answer is that, because those systems are based on relatively simple, traditional neural networks technology which typically can only simultaneously process relatively few parameters, they are not capable of identifying the important parameters from among hundreds of possible candidates, and they will not identify specific combinations of parameter ranges (“sweet spots”) that make overall power plant operations more robust.
The cutting-edge technologies developed by StatSoft Power Solutions will not simply implement a cookie-cutter approach to use a few parameters common to all power plants to achieve some (usually only very modest) overall process performance improvements. Instead, our approach allows you to take a fresh look at all your data and operations, to optimize them for best performance. This will allow you to focus your process monitoring efforts, operator training, or automation initiatives only on those parameters that actually drive boiler efficiency, emissions, and so on at your plant and for your equipment.
What we are offering is not simply another neural net for closed loop control; instead, it provides flexible tools based on cutting-edge data processing technologies to optimize all systems, and also provides smart monitoring and advisory options capable of predicting problems, such as emissions related to combustion optimization or maintenance issues.