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- W4289209265 abstract "It is desired to model the effect of three input features, the LPG flow rate, drum rotation speed and blower speed on the measured temperature profile in a rotating drum coffee roaster of a 30 kg capacity for operator prediction purposes. The performance of a first-principles model of this rotating drum coffee roaster (Schwartzberg, 2002) is compared to the performance of a composite/hybrid empirical model. The hybrid empirical model is composed of two gradient boosted regression trees and an artificial feedforward neural network which predicts two characteristic portions of the measured temperature profile by accurate prediction of the minimum in measured temperature. The first-principles model under-predicts the heat transfer to the coffee beans and as a result over-predicts the minimum temperature point when compared to true data. The relevant root mean squared error (RMSE) between the Schwartzberg model and the true measured temperature of an unseen test set of data is calculated as 23.05 as compared to the RMSE between the hybrid empirical model and the true measured temperature of 9.153. It is recommended that a larger set of data be used to train the empirical model to improve the generalisation of the model to new sets of input features." @default.
- W4289209265 created "2022-08-01" @default.
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- W4289209265 date "2022-01-01" @default.
- W4289209265 modified "2023-10-12" @default.
- W4289209265 title "A Practical Guide to Coffee Roaster Modelling" @default.
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- W4289209265 doi "https://doi.org/10.1016/b978-0-323-95879-0.50025-4" @default.
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