A neural network-based optimizing control system for a seawater-desalination solar-powered membrane distillation unit

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Abstract

Several schemes have been proposed so far for coupling desalination processes with the use of renewable energy. One of their main drawbacks, however, is the nature of the energy source that requires a discontinuous and non-stationary operation, with some control and optimization problems. In the present work, a solar powered membrane distillation system has been used for developing an optimizing control strategy. A neural network (NN) model of the system has been trained and tested using experimental datapurposely collected. Afterwards, the NN model has been used for the analysis of the process performance under various operating conditions, namely distillate production versus feed flow rate, solar radiation and cold feed temperature. On this basis, a control system that optimizes the distillate production under variable operating conditions has been developed, implemented and tested.
Lingua originaleEnglish
pagine (da-a)79-96
Numero di pagine18
RivistaCOMPUTERS & CHEMICAL ENGINEERING
Volume54
Stato di pubblicazionePublished - 2013

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Desalination
Seawater
Distillation
Neural networks
Membranes
Control systems
Solar radiation
Flow rate
Temperature

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Cita questo

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title = "A neural network-based optimizing control system for a seawater-desalination solar-powered membrane distillation unit",
abstract = "Several schemes have been proposed so far for coupling desalination processes with the use of renewable energy. One of their main drawbacks, however, is the nature of the energy source that requires a discontinuous and non-stationary operation, with some control and optimization problems. In the present work, a solar powered membrane distillation system has been used for developing an optimizing control strategy. A neural network (NN) model of the system has been trained and tested using experimental datapurposely collected. Afterwards, the NN model has been used for the analysis of the process performance under various operating conditions, namely distillate production versus feed flow rate, solar radiation and cold feed temperature. On this basis, a control system that optimizes the distillate production under variable operating conditions has been developed, implemented and tested.",
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T1 - A neural network-based optimizing control system for a seawater-desalination solar-powered membrane distillation unit

AU - Cipollina, Andrea

AU - Micale, Giorgio Domenico Maria

AU - Galluzzo, Mose'

PY - 2013

Y1 - 2013

N2 - Several schemes have been proposed so far for coupling desalination processes with the use of renewable energy. One of their main drawbacks, however, is the nature of the energy source that requires a discontinuous and non-stationary operation, with some control and optimization problems. In the present work, a solar powered membrane distillation system has been used for developing an optimizing control strategy. A neural network (NN) model of the system has been trained and tested using experimental datapurposely collected. Afterwards, the NN model has been used for the analysis of the process performance under various operating conditions, namely distillate production versus feed flow rate, solar radiation and cold feed temperature. On this basis, a control system that optimizes the distillate production under variable operating conditions has been developed, implemented and tested.

AB - Several schemes have been proposed so far for coupling desalination processes with the use of renewable energy. One of their main drawbacks, however, is the nature of the energy source that requires a discontinuous and non-stationary operation, with some control and optimization problems. In the present work, a solar powered membrane distillation system has been used for developing an optimizing control strategy. A neural network (NN) model of the system has been trained and tested using experimental datapurposely collected. Afterwards, the NN model has been used for the analysis of the process performance under various operating conditions, namely distillate production versus feed flow rate, solar radiation and cold feed temperature. On this basis, a control system that optimizes the distillate production under variable operating conditions has been developed, implemented and tested.

UR - http://hdl.handle.net/10447/78428

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SP - 79

EP - 96

JO - COMPUTERS & CHEMICAL ENGINEERING

JF - COMPUTERS & CHEMICAL ENGINEERING

SN - 0098-1354

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