Abstract
The article proposes a model of the diffusion station of a sugar factory based on the neural network, which predicts the main technological indicators of resource efficiency. In contrast to existing solutions, forecasting is performed in real time, which allows one to increase the informative support of the operator-technologist on the enterprise quality measurements without additional workload on the industrial laboratory. The proposed model predicts the loss of sugar in the pulp and the amount of sugar in the diffusion juice with an error of less than 2%. The input variables of the forecast model are automatically measurable technological variables of the process, a total of eleven. On the basis of such information one can make a corresponding decision to change technological modes (temperature and consumption) at the station, thereby increasing the quality of beet processing. The article consists of two parts: analysis of process variables, including statistical and definition of input and output model variables; development and verification of the forecast model.
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Zaiets, N., Vlasenko, L., Lutska, N., Shtepa, V. (2022). Resource Efficiency Forecasting Neural Network Model for the Sugar Plant Diffusion Station. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2022: New Solutions and Technologies for Automation, Robotics and Measurement Techniques. AUTOMATION 2022. Advances in Intelligent Systems and Computing, vol 1427. Springer, Cham. https://doi.org/10.1007/978-3-031-03502-9_16
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