Saving energy using fuzzy control applied to a chiller: an experimental study

Silva, F. V.; Schmitz, Jones Erni; Filho, Lincoln C. Neves; Fileti, Ana Maria Frattini; Júnior, Vivaldo Silveira

doi:10.1007/s10098-011-0409-6

Cited by 10 publications

(9 citation statements)

References 7 publications

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“…Preglej et al [5] have announced a reduction of 44 % by using a fuzzy multivariable strategy, but it must be mentioned that they have compared it with a simple PI controller in experimental conditions. The superior performance of fuzzy controllers over PID controllers and the energy savings enabled by fuzzy controllers in refrigeration systems have been already reported by Preglej et al [5], Aprea et al [2] and by Silva et al [6] in experimental conditions. These advantages were also observed for the proposed approach.…”

Section: Control Effortmentioning

confidence: 68%

“…However, refrigeration systems regulated by on-off controllers are still typical and that is a source of inefficiency in such systems [3,5,6]. Besides, a well-controlled process is also more safer and reduce the need for emergency repairs.…”

Section: Introductionmentioning

confidence: 98%

“…Their results demonstrated the superiority of the proposed NN-control strategy over conventional PID controller. Single input single output (SISO) control systems based on fuzzy logic are typical examples of intelligent control and are widely used in refrigeration systems [2,6]. Preglej et al [5] reported the results of the application of a fuzzy model-based multivariable predictive functional control in a heating ventilating and air conditioning (HVAC) system.…”

Section: Introductionmentioning

confidence: 99%

“…According to Schurt et al [1] 10 % of all energy produced in the world is consumed by refrigeration systems. This issue has motivated many researchers to investigate ways of carrying out the refrigeration process while saving energy [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fuzzy Multivariable Control Strategy Applied to a Refrigeration System

Vidal

Schmitz

Franco

et al. 2016

Chemical Product and Process Modeling

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The refrigeration process involves complex systems exhibiting nonlinearities and coupled behavior, so this paper aims to evaluate the comparative performance of a multivariable fuzzy logic-based control system and a classic multi loop PID. The process variables were the temperature of the secondary fluid (propylene glycol) outlet and the evaporating temperature. The manipulated variables were the compressor frequency speed and the pump frequency speed. Aspen Plus and Aspen Dynamics simulators were used to simulate the experimental prototype. The model was previously validated and linked with MATLAB software, where the controllers were implemented. Tuning of the fuzzy controller was performed through the membership functions and gains adjustments. The tuning of the multi loop PID controller was performed using the Ziegler-Nichols method and then a fine tuning was carried out. In order to fairly compare energy consumption and control effort, the tune of PIDbased strategy was finished when similar values of Integral of Squared Error were achieved. Thus, very similar behavior for the process variables in both controllers. On the other hand, a great improvement in the control effort and energy saving was observed when the multivariable fuzzy controller was used in comparison to classic PID. The energy consumption was reduced by 25 % and the control effort by 96 % when the proposed strategy was used.

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Section: Control Effortmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Multivariable Control Strategy Applied to a Refrigeration System

Vidal

Schmitz

Franco

et al. 2016

Chemical Product and Process Modeling

View full text Add to dashboard Cite

show abstract

“…These applications include control in refrigeration systems (APREA et al, 2004;EKREN and KUÇUKA, 2010;SILVA et al, 2011;VIDAL et al, 2016;FRANCO et al, 2017); in biodiesel reactor (BABUSKA and VERBRUGGEN, 1996;EKER and TORUN, 2006;FONSECA, et al, 2016); in polymerization reactor (FILETI et al, 2007;WAKABAYASHI et al, 2009);in bioreactor (FONSECA et al, 2013) and successful application to nonlinear and real-world processes control (FUENTE et al, 2006;LIAO et al, 2008;PRECUP and HELLENDOORN, 2011).…”

Section: Introductionmentioning

confidence: 99%