{"id":3159,"date":"2023-07-07T09:14:05","date_gmt":"2023-07-07T09:14:05","guid":{"rendered":"https:\/\/nag.com\/?post_type=case-studies&p=3159"},"modified":"2023-07-07T09:29:55","modified_gmt":"2023-07-07T09:29:55","slug":"powergen-optimizes-power-plant-performance-using-nags-algorithms","status":"publish","type":"case-studies","link":"https:\/\/nag.com\/case-studies\/powergen-optimizes-power-plant-performance-using-nags-algorithms\/","title":{"rendered":"PowerGen optimizes power plant performance using n<\/i><\/span>AG’s Algorithms"},"content":{"rendered":"\n
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PowerGen optimizes power plant performance using n<\/i><\/span>AG’s Algorithms<\/h1>\n \n

Case Study<\/p>\n<\/div>\n\n \n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n\n\n\n

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The Challenge<\/h3>\n
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Faced with an increasingly competitive power supply market and stricter environmental targets, optimizing the performance of its power plants has become a major challenge for PowerGen, a global generator, distributor and supplier of electricity.<\/p>\n

Playing a major part in achieving optimization at power stations is Dr Ian Mayes, Senior Engineer in the Software Engineering Group at PowerGen\u2019s Power Technology Centre in Nottingham. With a background in physics, Dr Mayes develops mathematical models of engineering processes and, in particular, the use of these models to optimize performance.<\/p>\n

Commenting Dr Mayes said: “In view of today\u2019s commercial and environmental pressures, the criteria for optimization in a power plant are typically based on either minimizing the NOx emissions whilst limiting the amount of unburnt fuel (carbon) left in the boiler ash or minimizing the amount of unburnt fuel whilst setting a limit on the NOx emissions<\/em>.”<\/p>\n

Optimization of power plant performance based on these criteria represents a major challenge since it is very difficult to reduce both unburnt carbon and NOx at the same time.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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The Solution<\/h3>\n
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The combustion process within a coal-fired power plant is complex and subject to a number of variable parameters that can affect performance. These include the distribution of coal in the boiler, the amount and distribution of the air in the boiler and, sometimes the particle size of the ground coal, which can be sourced from several different coal mines.<\/p>\n

There are constraints, however, on which parameters can be adjusted to achieve optimization. Fuel flow, for example, needs to be maintained in order to keep the amount of power generated constant. If this constraint is removed, the model would simply shut the plant down \u2013 great for reducing NOx emissions but not appropriate for generating power or profitability.<\/p>\n

There are also constraints on steam temperatures which can limit the range of operation and also constraints on engineering the upper and lower boundaries of the control variables.<\/p>\n

With such a complex process, in which both dependent and independent variables can play an important part in overall performance, it is clear that optimization represents a genuine challenge. And, it is a challenge that needs to be tackled, if the balance between commercial and environmental targets is to be achieved.<\/p>\n

There was really only one effective way forward \u2013 the development of a software model of the combustion process in the boiler of the coal-fired power plant that can account for all these variables.<\/p>\n

Dr Mayes explained: “With constantly changing environmental legislation and commercial pressures, there is a need for continuous optmization of the power station boiler and traditional combustion testing only provided a snap-shot. It was also too expensive to frequently run tests on the actual plant \u2013 it simply wasn\u2019t feasible. So, we\u2019ve developed mathematical models to enable the process to be optimized based on certain criteria \u2013 a general concept that can be applied across many different industrial systems. The key feature is the need for a model of the process that allows us to use optimization techniques to find better solutions<\/em>.”<\/p>\n

In writing and developing the model, PowerGen turned to n<\/i><\/span>AG for mathematical analysis software that could be integrated within its specialist application.<\/p>\n

Dr Mayes explained: “With so many variables and parameters that can influence performance, a large degree of number crunching is required within our model. Whilst we concentrated on developing the specialist software for our application, we used the n<\/i><\/span>AG Fortran Library to carry out mathematical manipulation of data within the model. After all, there is little point in reinventing the wheel. We looked at what was available and n<\/i><\/span>AG offered precisely what we required.<\/em>“<\/p>\n

“Not only that, n<\/i><\/span>AG has a reputation for high quality, tried and tested software that is well documented and supported by first class technical help, if required. In fact, whenever a mathematical operation of any complexity is required, we check to see whether there are n<\/i><\/span>AG routines that can do the job<\/em>.”<\/p>\n<\/div>\n<\/div>\n

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The Results<\/h3>\n
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Today, PowerGen has developed boiler modelling software, incorporating n<\/i><\/span>AG algorithms, that enables them to optimize the performance of their power plants based on specific criteria.<\/p>\n

If legislation and commercial pressures change, then the criteria for optimum performance will change and parameters have to be altered. Importantly, the model enables PowerGen to determine the effects on performance of these changes, before applying them to the actual power plant. Tests can be run quickly and cost effectively, so that the parameters can be set to achieve optimum performance based on specific criteria.<\/p>\n

Discussing the benefits of n<\/i><\/span>AG and the contribution the company has made to PowerGen, Dr Mayes said: “There is no doubt that by using n<\/i><\/span>AG products our development time for software applications involving complex mathematics has been reduced significantly. There is also a great comfort factor in using n<\/i><\/span>AG software. We know their products are accurate, reliable and robust, and they will not fall down<\/em>.”<\/p>\n<\/div>\n<\/div>\n

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The n<\/i><\/span>AG Fortran Library<\/h3>\n
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The n<\/i><\/span>AG Fortran Library contains over 1,700 complex and highly sophisticated user callable routines for mathematical and statistical computation, which many organisations integrate with a variety of applications including Visual Basic, VBA, Excel, Fortran and C\/C++ programs.<\/p>\n

The routines cover the following areas: Eigenvalues and Eigenvectors; FFTs; Interpolation; Linear Algebra; Optimization; Partial and Ordinary Differential Equations; Quadrature, Curve and Surface Fitting; Random Number Generation; and, Statistics.<\/p>\n

The correctness of each Library routine is evaluated and verified by specifically written test programs that are performed on each of the machine ranges for which the Library is available, and\u00a0only when an implementation satisfies n<\/i><\/span>AG\u2019s stringent accuracy standards is it released.<\/p>\n

Also available in C, Fortran 90 and high-performance computing versions, n<\/i><\/span>AG\u2019s algorithms are backed-up by an extensive range of services and support facilities including customization. Underpinning the quality of all n<\/i><\/span>AG software is our renowned and comprehensive documentation.<\/p>\n

Dr Ian Mayes Senior Engineer
Software Engineering Group
PowerGen Power Technology Centre Nottingham<\/p>\n<\/div>\n<\/div>\n <\/div>\n <\/div>\n<\/div>\n\n\n

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<\/div>\n\n \n Learn more about the n<\/i><\/span>AG Library <\/i><\/a> <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Faced with an increasingly competitive power supply market and stricter environmental targets, optimizing the performance of its power plants has become a major challenge for PowerGen, a global generator, distributor and supplier of electricity.<\/p>\n","protected":false},"author":3,"featured_media":3161,"parent":0,"menu_order":0,"template":"","meta":{"content-type":"","footnotes":""},"post-tag":[27,21],"class_list":["post-3159","case-studies","type-case-studies","status-publish","has-post-thumbnail","hentry"],"yoast_head":"\nPowerGen optimizes power plant performance using NAG's Algorithms - nAG<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/nag.com\/case-studies\/powergen-optimizes-power-plant-performance-using-nags-algorithms\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PowerGen optimizes power plant performance using NAG's Algorithms - 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