Fuzzy-neural-net-based inferential control for a high-purity distillation column

Luo, Rongfu; Shao, Hongda; Zhang, Z.J.

doi:10.1016/0967-0661(94)00061-k

Cited by 25 publications

(8 citation statements)

References 13 publications

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“…In [41] a soft-sensor based on Fuzzy-Neural-Net multimodel approach is proposed. More precisely, the softsensor is composed of fuzzy classifier and local models (ANNs) which outputs are weighted and combined to produce model output.…”

Section: Reported Applications Of the Soft-sensors In Process Industrymentioning

confidence: 99%

Methods for Plant Data-Based Process Modeling in Soft-Sensor Development

2011

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Original scientific paper There has been an increased use of soft-sensors in process industry in recent years. These soft-sensors are computer programs that are used as a relatively cheap alternative to hardware sensors. Since process variables, which are concerned with final product quality, cannot always be measured by hardware sensors, designing the appropriate soft-sensor can be an interesting solution. Additionally, a soft-sensor can be used as a backup sensor, when the hardware sensor is in fault or removed due to maintenance or replacement. Soft-sensor is based on the mathematical model of the process. Since industrial processes are generally quite complex, a theoretical modeling approach is often impractical, expensive or sometimes even impossible. Therefore, process model building is based on measured data. This approach significantly gets complicated if only plant data, taken from the process database, are available. In this paper the most popular methods for plant data-based modeling that appeared in the last two decades are summarized and briefly explained. Apart from giving a short survey of the most important papers, tips about choosing the appropriate methodology for process model building are also provided.

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Section: Reported Applications Of the Soft-sensors In Process Industrymentioning

confidence: 99%

Methods for Plant Data-Based Process Modeling in Soft-Sensor Development

2011

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“…The sampling is represented mathematically as 2.32 (43) 2.32 (44) where U′ is the unit impulse. We may now define f * (t) as the product of the input signal and the switching function:…”

Section: Z Transformsmentioning

confidence: 99%

Control Theory

2005

Instrument Engineers' Handbook, Fourth Edition, Volume Two

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“…The most successful advanced control strategies applied in industrial distillation columns are model predictive controls (Foss and Cong, 1999), inferential controls (Luo et al, 1995) and some orthers. When a model predictive control strategy is used, online identification of the model is necessary.…”

Section: The Advanced Control Project and Its Applicationmentioning

confidence: 99%

The Advanced Process Control System for an Industrial Distillation Column

Shi

et al. 2005

IFAC Proceedings Volumes

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Fuzzy-neural-net-based inferential control for a high-purity distillation column

Cited by 25 publications

References 13 publications

Methods for Plant Data-Based Process Modeling in Soft-Sensor Development

Methods for Plant Data-Based Process Modeling in Soft-Sensor Development

Control Theory

The Advanced Process Control System for an Industrial Distillation Column

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