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.
|Numero di pagine||18|
|Rivista||COMPUTERS & CHEMICAL ENGINEERING|
|Stato di pubblicazione||Published - 2013|
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