Year: 2023
Type: conference-paper
Abstract: The paper is devoted to the development of an intelligent control system for the spray drying machine producing tomato powder. Since the technological process is quite complex, the input is uncertain, and the output is unpredictable, there is no possibility to use mathematical or other formal models to control it. The development task was proposed to be solved in two stages. At the stage of perception, methods for recognizing images captured by the electro- optical and infrared sensors were used. A four-layer fully connected backpropagation neural network was used to identify the state of a mixture consisting of tomato paste particles and superheated droplets, which allows the detection of some deviation in the process flow. At the decision-making stage, a neuro-fuzzy approach based on the Sugeno model was chosen. The neuro-fuzzy controller was implemented based on the ANFIS model represented by a five-layer forward propagation neural network. The ANFIS-based neuro-fuzzy controller was modeled, generated, and trained using the Fuzzy Logic Toolbox. The experiment was shown that a spray drying machine equipped with the developed intelligent control system can produce tomato powder of high quality without human intervention that excludes operator errors.
Authors: Katerina Zubkova, Volodymyr Sherstjuk
Institution: Kherson National Technical University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W7166323941