Abstract
AbstractBACKGROUNDCheese production occupies a small but growing share of the current dairy industry. Industrial cream cheese production involves the complex scheduling of multiple batch fermenters and various downstream units. However, significant end‐time variation from different fermentation batches makes downstream scheduling challenging and thus decreases the process throughput.RESULTSThis research addressed this challenge by using an artificial neural network (a Long‐Short Term Memory Network, LSTM) in combination with a mechanistic model describing the changes in biomass, lactose, and lactic acid concentrations. The LSTM network/mechanistic modelling approach shows an end‐time difference of 3 min over batch times of 6 to 7 h compared to the laboratory experiment, with an overall accuracy (R2) of over 0.99.CONCLUSIONThe proposed hybrid fundamental/artificial neural network (ANN) model framework could reasonably predict the cheese fermentation pH with limited data. The outcome of this research enables fermentation end‐time prediction, makes the downstream scheduling possible, and thus, could help to improve the process throughput. The developed model could also be used for further cheese digital twin development, based on how better product quality control and higher process efficiency could be achieved. © 2020 Society of Chemical Industry
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