The hybrid approaches for forecasting real time multi-step-ahead boiler efficiency

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099106" target="_blank" >RIV/61989100:27240/16:86099106 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/2857546.2857563" target="_blank" >http://dx.doi.org/10.1145/2857546.2857563</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/2857546.2857563" target="_blank" >10.1145/2857546.2857563</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The hybrid approaches for forecasting real time multi-step-ahead boiler efficiency

Popis výsledku v původním jazyce

We study how to optimize the boiler efficiency of a steam boiler which is the most important component in a fertilizer plant. In particular, we have proposed several methods for forecasting when the trend of the boiler efficiency is going down so that some control parameters of the boiler are adjusted to keep its efficiency stably. This is a challenging task since the boiler efficiency is a noisy time series data. In this paper, we propose two different methods for forecasting the boiler efficiency by multi-step-ahead (MSA) in real time. The first method, namely RTRL-RFNN, that applies a MSA reinforced real time learning algorithm for recurrent fuzzy neural networks (RFNNs). RTRL-RFNN repeatedly adjusts model parameters of RFNNs according to the latest observed values. The second method, namely SE-RFNN, is a hybrid of stochastic exploration and RFNNs. To demonstrate the performance of our methods we implement two proposed methods and an existent method called RFNN. Moreover, we illustrate the experimental results on the same dataset collected from Phu My Fertilizer Plant, Petro Vietnam Fertilizer and Chemical Corporation, Petro Vietnam Group, Vietnam. The experimental results show that three methods are appropriate to be employed for forecasting the real time MSA boiler efficiency and both proposed SE-RFNN and RTRL-RFNN outperform RFNN. (C) 2016 ACM.

Název v anglickém jazyce

The hybrid approaches for forecasting real time multi-step-ahead boiler efficiency

Popis výsledku anglicky

We study how to optimize the boiler efficiency of a steam boiler which is the most important component in a fertilizer plant. In particular, we have proposed several methods for forecasting when the trend of the boiler efficiency is going down so that some control parameters of the boiler are adjusted to keep its efficiency stably. This is a challenging task since the boiler efficiency is a noisy time series data. In this paper, we propose two different methods for forecasting the boiler efficiency by multi-step-ahead (MSA) in real time. The first method, namely RTRL-RFNN, that applies a MSA reinforced real time learning algorithm for recurrent fuzzy neural networks (RFNNs). RTRL-RFNN repeatedly adjusts model parameters of RFNNs according to the latest observed values. The second method, namely SE-RFNN, is a hybrid of stochastic exploration and RFNNs. To demonstrate the performance of our methods we implement two proposed methods and an existent method called RFNN. Moreover, we illustrate the experimental results on the same dataset collected from Phu My Fertilizer Plant, Petro Vietnam Fertilizer and Chemical Corporation, Petro Vietnam Group, Vietnam. The experimental results show that three methods are appropriate to be employed for forecasting the real time MSA boiler efficiency and both proposed SE-RFNN and RTRL-RFNN outperform RFNN. (C) 2016 ACM.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ACM IMCOM 2016: Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication

ISBN

978-1-4503-4142-4

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Danang

Datum konání akce

4. 1. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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