Determination of Hopper Fullness of Smart Screw Press Using Machine Learning

Abstract

Problem statement. This research addresses the challenge of accurately determining the fullness of the hopper within a screw press for optimal oil extraction efficiency and quality. Existing weight or volume-based measurement methods can often struggle with determining the feed hopper fullness due to variable oil weights during extraction stages, material heterogeneity, environmental influences and imprecise instrument calibration. Purpose. The study proposes a novel solution via the application of machine learning, specifically aiming to develop and validate a technique that uses acoustic signals to calculate screw press bowl load. Methodology. To implement this solution, the study uses quantitative research, data collection and data analysis, supervised learning. The method is based on the processing of audio data received from microphones located near the auger and the use of machine learning algorithms, such as sound classification. Model training process was facilitated by ML tool Arduino. Findings. The results of this study, facilitated by effective data analysis via ML tools, demonstrate that the evaluated filling level of the screw press hopper can effectively be determined by the sound signals produced and corresponding machine learning algorithms. Originality. The distinct advantage of this approach lies in its ability to automate the monitoring and operational control process of the oil press, thereby improving device efficiency and resource conservation. Practical value. The proposed approach allows to automate the process of determining the fullness of the bowl and monitor the condition of the auger by its sound characteristics. This solution can be utilized in the oil production industry to enhance the productivity of the screw presses. This research underscores the promise of machine learning applications and the potential for future research focusing on improving model adaptability and developing predictive maintenance systems. These future investigative scopes could essentially revolutionize monitoring and operational practices within the oil extraction industry.