Fuzzy control of furnace temperature in a zinc roaster

Rauma, Tapio; Nyberg, Jens; Herronen, Jarmo

doi:10.1016/s1474-6670(17)36983-5

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…A fuzzy logic controller has been in operation in the zinc roaster furnaces since 1994 with good results (Rauma et al 2000). Furnaces are operated closer to their theoretical maximum temperature.…”

Section: Other Processesmentioning

confidence: 99%

Industrial Applications of Soft Computing

Leiviskä¹

2001

Studies in Fuzziness and Soft Computing

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Section: Other Processesmentioning

confidence: 99%

Industrial Applications of Soft Computing

Leiviskä¹

2001

Studies in Fuzziness and Soft Computing

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“…Another widely use temperature control strategy is using fuzzy logic. Examples of successful 679 application of fuzzy logic controller were reported in [10] for greenhouse application, in [11] for industrial roaster application, in [12] for furnace application, in [13] for an electric vehicle, in [14] for pantographcatenary system, in [15] for swarm robotics and in [16] for the superheated steam application. Application of fuzzy logic controller (FLC) in vacuum distiller was reported in [17].…”

Section: Introductionmentioning

confidence: 99%

Feedback-feedforward fuzzy logic approach for temperature control in bioethanol vacuum distiller

Muslim

Yudha

Ibrahim

2019

IJEECS

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<span>Energy conservation and diversification are becoming a major research issue. Awareness of the limited sources of energy from fossil fuels encourages research on renewable energy. Bioethanol is a promising fuel substitute for gasoline. Bioethanol processing includes sugar extraction, fermentation, distillation, and absorption. Temperature and pressure controls are essential in bioethanol processing. This paper presents a feedback-feedforward fuzzy logic approach for temperature control in a bioethanol vacuum distiller. In this study, vacuum pressure is employed as feedforward inputs for a fuzzy logic controller. The feedforward input directly modifies the main controller, i.e., fuzzy logic controller, through fuzzy rules. The controller is implemented using Arduino Mega 2560 microcontroller. The results show that the proposed feedback-feedforward fuzzy logic controller could successfully maintain the temperature at the desired setpoint value with small steady-state error (3.85%) and relatively shorter settling time compared to classical PID controller and fuzzy logic controller.</span>

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