Diagnostics of the information subsystem of an automatic process control system using artificial intelligence technologies

Repin, A. I.; Sabanin, V. R.; Smirnov, N. I.; Andreev, Sergey

doi:10.1134/s0040601506060152

Cited by 5 publications

(2 citation statements)

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“…An important factor that hinders gathering statistical data for making managerial decisions directly within the TCS is measurement errors of quantitative parameters of conversion products. This is a strong reason to use the optimization criterion, since the use of direct methods for determining the optimum requires by orders of magnitude more experiments with the need to process the "cloud" of measurements [8].…”

Section: Analysis Of the Literature Data And Problem Statementmentioning

confidence: 99%

Optimal control of systems engineering. development of a general structure of the technological conversion subsystem (Part 2)

Lutsenko¹

2015

EEJET

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Запропоновано архітектуру технологічної підсистеми, особливістю якої є наявність максимальної кількості ступенів свободи. Це забезпечує можливість незалежної зміни всіх важливих параметрів технологічного процесу. Вбудовані можливості оцінки економічних параметрів технологічної операції вирішують завдання оптимізації технологічного процесу за критерієм ефективності використання ресурсів. Всі рішення перевірені з використанням спеціального системного конструктора EFFLI Ключові слова: технологічна підсистема, структура оптимальної технологічної підсистеми, оптимізація технологічного процесу Предложена архитектура технологической подсистемы, особенностью которой является наличие максимального количества степеней свободы. Это обеспечивает возможность независимого изменения всех значимых параметров технологического процесса. Встроенные возможности оценки экономических параметров технологической операции решают задачу оптимизации технологического процесса по критерию эффективности использования ресурсов. Все решения проверены с иcпользованием специального системного конструктора EFFLI Ключевые слова: технологическая подсистема, структура оптимальной технологической подсистемы, оптимизация технологического процесса

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Section: Analysis Of the Literature Data And Problem Statementmentioning

confidence: 99%

Optimal control of systems engineering. development of a general structure of the technological conversion subsystem (Part 2)

Lutsenko¹

2015

EEJET

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“…On the other hand, models based on Computational Intelligence (CI) algorithms are very useful to deal with non-linear and complex problems because they are based on the input-output behaviour of the process and consequently the model describes the overall performance [6][7][8][9][10], but no prior model is available.…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence techniques for maximum energy efficiency of an internal combustion engine and a steam turbine of a cogeneration process

Fernández

Campo

Echanobe

et al. 2014

Int J Energy Environ Eng

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This paper discusses the development of a model of a real cogeneration plant based on Computational Intelligence (CI) algorithms. In particular, two CI strategies are used: one based on artificial neural network and the other one based on a neuro-fuzzy system. Both systems are trained with a data collection from the cogeneration plant. Data mining techniques are applied to remove erroneous and redundant data, and also to obtain information about the variables and its behaviour. This task allows to select only the relevant information and is also a way to decrease the complexity of the model. In this first approach on the work, two separate subsystems of the cogeneration process are considered: an engine and a steam turbine. The obtained models are used to analyze the role of each involved variable and to derive a set of recommendations (i.e., changes in some of the input variables) to optimize the performance of the system. The recommendations applied to the models improve the behaviour of the plant providing higher energy production with a lower cost.

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Enhancing the calculation accuracy of performance characteristics of power-generating units by correcting general measurands based on matching energy balances

Shchinnikov

Сафронов

2014

Therm. Eng.

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Diagnostics of the information subsystem of an automatic process control system using artificial intelligence technologies

Cited by 5 publications

References 0 publications

Optimal control of systems engineering. development of a general structure of the technological conversion subsystem (Part 2)

Optimal control of systems engineering. development of a general structure of the technological conversion subsystem (Part 2)

Computational Intelligence techniques for maximum energy efficiency of an internal combustion engine and a steam turbine of a cogeneration process

Enhancing the calculation accuracy of performance characteristics of power-generating units by correcting general measurands based on matching energy balances

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