This study is part of the re-valorisation of the dairy waste industry through the use of membrane ultrafiltration (UF), in order to recover whey proteins and remove as much water as possible from the permeate. This study aimed to predict and control the permeate flux decline in cross-flow whey UF through a step procedure, and to compare different Artificial Neural Networks (ANNs), followed by a genetic algorithm (GA), as the optimization strategy. Models were developed in Matlab® Neural Network Toolbox. ANNs of one or two hidden layers were trained and simulated. A trial-and-error procedure identified the best network based on its performance values. The networks were trained through a selected set of experimental data obtained for lab-scale hollow-fibre membrane modules used to re-value scotta, the final waste of the dairy industry. The operating conditions considered as the input of the ANN were: operating time (top), sampling time (tsample), cross-flow velocity (CFV) and transmembrane pressure (TMP), while the output of the network was exclusively the normalized permeate flux (Jn). GA optimization was carried out to the following range of operating conditions to reach the best performances and to manage the fouling effect: 225 < top < 300 min, 8.33 < tsample < 15.9 min, 6.25 < CFV < 8.33 L/min, and TMP equal to 1.33 bar, otherwise it can be ignored. In fact, it has been noted that the networks with only three inputs, without TMP, predict and control Jn output better. Moreover, considering the normalized flux, it was possible to ignore some other important operating conditions, such as the membrane geometry. Consequently, the proposed general solution could also be used for other kinds of membrane applications. Finally, a hybrid approach among the ANN networks and a theoretical model was also used to better predict the resistance trend. It also returned more evident correspondence results than the ANN simulation alone, especially in the initial drop of Jn. The use of the theoretical part in the hybrid approach acts as a filter and returned the following order of significance of the operational input conditions on the resistance: top, tsample, CFV and TMP.
Application of Artificial Neural Networks for Modelling and Control of Flux Decline in Cross-Flow Whey Ultrafiltration
Gaudio M. T.;Curcio S.;Chakraborty S.;Calabro V.
2023-01-01
Abstract
This study is part of the re-valorisation of the dairy waste industry through the use of membrane ultrafiltration (UF), in order to recover whey proteins and remove as much water as possible from the permeate. This study aimed to predict and control the permeate flux decline in cross-flow whey UF through a step procedure, and to compare different Artificial Neural Networks (ANNs), followed by a genetic algorithm (GA), as the optimization strategy. Models were developed in Matlab® Neural Network Toolbox. ANNs of one or two hidden layers were trained and simulated. A trial-and-error procedure identified the best network based on its performance values. The networks were trained through a selected set of experimental data obtained for lab-scale hollow-fibre membrane modules used to re-value scotta, the final waste of the dairy industry. The operating conditions considered as the input of the ANN were: operating time (top), sampling time (tsample), cross-flow velocity (CFV) and transmembrane pressure (TMP), while the output of the network was exclusively the normalized permeate flux (Jn). GA optimization was carried out to the following range of operating conditions to reach the best performances and to manage the fouling effect: 225 < top < 300 min, 8.33 < tsample < 15.9 min, 6.25 < CFV < 8.33 L/min, and TMP equal to 1.33 bar, otherwise it can be ignored. In fact, it has been noted that the networks with only three inputs, without TMP, predict and control Jn output better. Moreover, considering the normalized flux, it was possible to ignore some other important operating conditions, such as the membrane geometry. Consequently, the proposed general solution could also be used for other kinds of membrane applications. Finally, a hybrid approach among the ANN networks and a theoretical model was also used to better predict the resistance trend. It also returned more evident correspondence results than the ANN simulation alone, especially in the initial drop of Jn. The use of the theoretical part in the hybrid approach acts as a filter and returned the following order of significance of the operational input conditions on the resistance: top, tsample, CFV and TMP.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
