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.
[1]. Kachur, O., Korendiy, V., Havran, V. (2023). Designing and simulation of an enhanced screw-type press for vegetable oil production. Computer Design Systems. Theory and Practice 5(1), 128–136. https://doi.org/10.23939/cds2023.01.128
[2]. Pedretti, E. F., Del Gatto, A., Pieri, S., Mangoni, L., Ilari, A., Mancini, M., Duca, D. (2019). Experimental study to support local sunflower oil chains: Production of cold pressed oil in Central Italy. Agriculture (Switzerland), 9(11). https://doi.org/10.3390/agriculture9110231
[3]. Melnyk, M., Pytel, K., Orynchak, M., Tomyuk, V., Havran. V. (2022). Analysis of Artificial Intelligence Methods for Rail Transport Traffic Noise Detection. Computer Design Systems. Theory and Practice 4 (1), 107-116. https://doi:10.23939/cds2022.01.107.
[4]. Sharan, R. V., Rahimi-Ardabili, H. (2023, August 1). Detecting acute respiratory diseases in the pediatric population using cough sound features and machine learning: A systematic review. International Journal of Medical Informatics. Elsevier Ireland Ltd. https://doi.org/10.1016/j.ijmedinf.2023.105093
[5]. Wardhany, V. A., Subono, Hidayat, A., Utami, S. W., Bastiana, D. S. (2022). Arduino Nano 33 BLE Sense Performance for Cough Detection by Using NN Classifier. In Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022 (pp. 455–458). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICITISEE57756.2022.10057829
[6]. Brusa, E., Delprete, C., Di Maggio, L. G. (2021). Deep transfer learning for machine diagnosis: From sound and music recognition to bearing fault detection. Applied Sciences (Switzerland), 11(24). https://doi.org/10.3390/app112411663
[7]. Meiners, M., Mayr, A., & Franke, J. (2020). Process curve analysis with machine learning on the example of screw fastening and press-in processes. In Procedia CIRP (Vol. 97, pp. 166–171). Elsevier B.V. https://doi.org/10.1016/j.procir.2020.05.220
[8]. Dobrojevic, M., & Bacanin, N. (2022, April 1). IoT as a Backbone of Intelligent Homestead Automation. Electronics (Switzerland). MDPI. https://doi.org/10.3390/electronics11071004
[9]. Jaiman, A., & Sharma, R. (2021). Optimizing The Smart Farming Using Artificial Intelligence Based Arduino Controller. Solid State Technology. Retrieved from http://www.solidstatetechnology.us/index.php/JSST/article/view/9972
[10]. M. Fadhil, H., Kadhum, A., & Abdulkadhum, R. (2017). Multi-effectiveness Smart Home Monitoring System Based Artificial Intelligence through Arduino. Journal of Software, 12(7), 546–558. https://doi.org/10.17706/jsw.12.7.546-558
[11]. Barrett, S. F. (2023). Artificial Intelligence and Machine Learning. In Synthesis Lectures on Digital Circuits and Systems (pp. 95–122). Springer Nature. https://doi.org/10.1007/978-3-031-21877-4_4
[12]. Preprint, E., Bharath Gowda, M., Abhilash, M. K., Pakeerappa, K., Bharath, B. M., Suchithra, M., K, A. M. (2022). A Review on Smart Warehouse Management System. Easy Chair Preprint.
[13]. Edwin, B., Veemaraj, E., Parthiban, P., Devarajan, J. P., Mariadhas, V., Arumuganainar, A., Reddy, M. (2022). Smart agriculture monitoring system for outdoor and hydroponic environments. Indonesian Journal of Electrical Engineering and Computer Science, 25(3), 1679–1687. https://doi.org/10.11591/ijeecs.v25.i3.pp1679-1687
[14]. Garett, R., Young, S. D. (2023). The role of artificial intelligence and predictive analytics in social audio and broader behavioral research. Decision Analytics Journal, 6. https://doi.org/10.1016/j.dajour.2023.100187
[15]. AlShorman, O., Alkahatni, F., Masadeh, M., Irfan, M., Glowacz, A., Althobiani, F., Glowacz, W. (2021). Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study. Advances in Mechanical Engineering. SAGE Publications Inc. https://doi.org/10.1177/1687814021996915
[16]. Household All Stainless Steel Oil Press Ltp200 Electric Small Household Commercial Cold And Hot Pressing Fully Automatic - Specialty Tools - AliExpress [Internet]. [cited 2024 Jan 10]. Available from: https://www.aliexpress.com/item/1005004330106945.html#navspecification
[17]. Edge Impulse [Internet]. Available from: https://mltools.arduino.cc/