A Novel Voltage and Air Pressure Regulator with Machine Learning for Auto-Adjusting on Bagging System

This study presents the development of a machine learning-based system for regulating voltage and air pressure to automate the fertilizer bagging process. This system integrates voltage and air pressure regulator with Random Forest Regression algorithm to accurately predict the weight of fertilizer within the package and to optimize the parameters associated with the bagging process. The experiment was conducted at PT Petrokimia Gresik, using 5,000 sensor readings stored in the SCADA system, which recorded the response time of the gate valve, air pressure, and the weight of the fertilizer in the package. The findings indicate that the Random Forest Regression model, comprised of 150 decision trees, accomplishes an RMSE of 0.043 and a MAPE of 0.085%. Notably, an increase in voltage from 22V to 26V decreases response time from 40 ms to 20 ms, thereby ensuring the stability of gate valve operations. Furthermore, every additional 0.01 seconds of gate valve opening time correlates with an increase in fertilizer weight by 0.142 kg, thus highlighting the significance of voltage stability. The open-loop servo control valve system guarantees optimal air pressure during bagging. Overall, the integration of machine learning techniques and Random Forest-based control systems enhances the consistency of bagging, minimizes weight variability, and boosts production efficiency, thereby contributing to the sustainability of the fertilizer industry.

Air pressure control; Fertilizer bagging; Machine learning; Random forest regression; Voltage regulator

Ali, F., Sarwar, A., Ilahi Bakhsh, F., Ahmad, S., Ali Shah, A., & Ahmed, H. (2023). Parameter extraction of photovoltaic models using atomic orbital search algorithm on a decent basis for novel accurate rmse calculation. Energy Conversion and Management, 277, 116613. https://doi.org/10.1016/j.enconman.2022.116613

Allix, K., Bissyandé, T. F., Jérome, Q., Klein, J., State, R., & Le Traon, Y. (2014). Large-scale machine learning-based malware detection: Confronting the 10-fold cross validation scheme with reality. Proceedings of the 4th ACM Conference on Data and Application Security and Privacy (CODASPY ’14), 163–166. https://doi.org/10.1145/2557547.2557587

AlShannaq, H., & Aly, A. (2024). Review of artificial neural networks for wind turbine fatigue prediction. Structural Durability and Health Monitoring, 18, 707–737. https://doi.org/10.32604/sdhm.2024.054731

Amalia, R., Ushada, M., & Pamungkas, A. P. (2023). Development of artificial neural networks model to determine labor rest period based on environmental ergonomics. International Journal of Technology, 14(5), 1019–1028. https://doi.org/10.14716/ijtech.v14i5.3854

Arianti, N. D., Muslih, M., Sitorus, A., & Bulan, R. (2021). Oscillation effect dataset on the measurement accuracy of load-cell sensor applied to the weigh basket. Data in Brief, 38, 107453. https://doi.org/10.1016/j.dib.2021.107453

Borghetti, M., Cantù, E., Ponzoni, A., Sardini, E., & Serpelloni, M. (2022). Aerosol jet printed and photonic cured paper-based ammonia sensor for food smart packaging. IEEE Transactions on Instrumentation and Measurement, 71, 1–10. https://doi.org/10.1109/TIM.2022.3161695

Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324

Chaomuang, N., Laguerre, O., Denis, A., Paviet Salomon, Y., Mawilai, P., Srisawat, K., Derens-Bertheau, E., Ndoye, F. T., Pongsuttiyakorn, T., Rakmae, S., Pun, U. K., Sirisomboon, P., Pornchaloempong, P., & Duret, S. (2024). Experimental study of air cargo temperature variations and its impact on mango quality. International Journal of Refrigeration, 160, 129–140. https://doi.org/10.1016/j.ijrefrig.2024.02.009

Chekenbah, H., Maataoui, Y., Abderrahmani, A. E., & Lasri, R. (2024). Simulating the effects of global warming on the agricultural yields in a reel greenhouse controlled via a new self-adaptive fuzzy controller. International Review on Modelling and Simulations (IREMOS), 17, 86–96. https://doi.org/10.15866/iremos.v17i2.24141

Chen, Q., Qian, J., Yang, H., Li, J., Lin, X., & Wang, B. (2024). Multiscale coupling analysis and modeling of airflow and heat transfer for warehouse-packaging-kiwifruit under forced-air cooling. Biosystems Engineering, 244, 166–176. https://doi.org/10.1016/j.biosystemseng.2024.06.007

Chung, H.-T., Tsai, C.-C., Jen, K.-K., Huang, Y.-S., Ferng, Y.-C., Lo, C.-Y., Chen, T.-W., Chang, K.-H., & Yeh, A.-C. (2024). Optimization of process parameters of selective laser melted nickel-based superalloy for densification by random forest regression algorithm and response surface methodology. Results in Engineering, 22, 102182. https://doi.org/10.1016/j.rineng.2024.102182

Expósito, A., & Velasco, F. (2020). Exploring environmental efficiency of the European agricultural sector in the use of mineral fertilizers. Journal of Cleaner Production, 253, 119971. https://doi.org/10.1016/j.jclepro.2020.119971

Fang, J., & Deng, B. (2024). Theoretical design and experimental verification of control system for building material packaging unit based on risk management. Scientific Reports, 14, 462. https://doi.org/10.1038/s41598-023-51102-3

Farahani, S., Khade, V., Basu, S., & Pilla, S. (2022). A data-driven predictive maintenance framework for injection molding process. Journal of Manufacturing Processes, 80, 887–897. https://doi.org/10.1016/j.jmapro.2022.06.013

Fellows, P. J. (2022). Packaging. In Food processing technology (5th ed., pp. 633–709). Woodhead Publishing. https://doi.org/10.1016/B978-0-323-85737-6.00001-7

Feng, H., Chen, D., & Lv, H. (2021). Sensible and secure IoT communication for digital twins, cyber twins, web twins. Internet of Things and Cyber-Physical Systems, 1, 34–44. https://doi.org/10.1016/j.iotcps.2021.12.003

Ghiat, I., Govindan, R., & Al-Ansari, T. (2025). Data-driven model predictive control (MPC) for irrigation management in agricultural greenhouse under CO? enrichment. Smart Agricultural Technology, 101071. https://doi.org/10.1016/j.atech.2025.101071

Huda, A. S. N., & Zivanovi?, R. (2017). Large-scale integration of distributed generation into distribution networks: Study objectives, review of models and computational tools. Renewable and Sustainable Energy Reviews, 76, 974–988. https://doi.org/10.1016/j.rser.2017.03.069

Iakymchuk, M., Gavva, O., & Bespalko, A. (2014). Research of mechatronic linear module with pneumatic drive based on the use of proportional pressure regulators.

Irianto, B. G., Maghfiroh, M., A. M. and Sofie, & Kholiq, A. (2023). Baby incubator with overshoot reduction system using PID control equipped with heart rate monitoring based on internet of things. International Journal of Technology, 14(4), 811–822. https://doi.org/10.14716/ijtech.v14i4.5678

Ismail, A. B., Bakar, H. B. A., & Shafei, S. B. (2020). Comparison of LDPE/corn stalk with eco degradant and LDPE/corn stalk with MAPE: Influence of coupling agent and compatibiliser on mechanical properties. Materials Today: Proceedings, 31, 360–365. https://doi.org/10.1016/j.matpr.2020.06.234

Kaewluan, S., Unsomsri, N., & Wiriyasart, S. (2025). PID air pressure controller in a positive pressure screening cabinet (PPS-Cabinet) for healthcare operator screening of SARS-CoV-2 infection. Perioperative Care and Operating Room Management, 40, 100497. https://doi.org/10.1016/j.pcorm.2025.100497

Kang-Xing, Z., Shu-Tong, W., & Wen-Xing, Z. (2025). Longitudinal velocity car-following control and optimization based on distributed model predictive control: Modeling, stability analysis and joint simulation. Physica A: Statistical Mechanics and its Applications, 673, 130708. https://doi.org/10.1016/j.physa.2025.130708

Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32, 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003

Kryvoplias-Volodina, L. (2016). Modeling packaging machine systems on the basis of proportional pressure regulator.

Li, Y., Zhang, L., Wu, D., Li, X., Zhang, X., & Liu, X. (2024). Dynamic synchronization optimization of beef atmosphere packaging system. Journal of Food Engineering, 380, 112162. https://doi.org/10.1016/j.jfoodeng.2024.112162

Malasl?, M. Z., Akkoyunlu, M. C., Pekel, E., Ta?ova, M., Kaya Dursun, S., & Akkoyunlu, M. T. (2025). Prediction of drying kinetics and energy consumption values of purple carrots dried in a temperature-controlled microwave dryer by decision tree, random forest and adaboost approaches. Chemometrics and Intelligent Laboratory Systems, 260, 105352. https://doi.org/10.1016/j.chemolab.2025.105352

Ma’ruf, A., Nasution, A. A. R., & Leuveano, R. (2024). Machine learning approach for early assembly design cost estimation: A case from make-to-order manufacturing industry. International Journal of Technology, 15(4), 1037–1047. https://doi.org/10.14716/ijtech.v15i4.5675

Mary, M. C., Jennath, H. S., & Asharaf, S. (2025). Explainable optimal random forest model with conversational interface. Engineering Applications of Artificial Intelligence, 145, 110134. https://doi.org/10.1016/j.engappai.2025.110134

Mercaldi, H. V., Peñaloza, E. A. G., Mariano, R. A., Oliveira, V. A., & Cruvinel, P. E. (2017). Flow and pressure regulation for agricultural sprayers using solenoid valves. IFAC World Congress, 50, 6607–6612. https://doi.org/10.1016/j.ifacol.2017.08.693

Moran, A. M., Vo, V. T., McDonald, K. J., Sultania, P., Langenbrunner, E., Chong, J. H. V., Naik, A., Kinnicutt, L., Li, J., & Ranzani, T. (2024). An electropermanent magnet valve for the onboard control of multi-degree of freedom pneumatic soft robots. Communications Engineering, 3, 1–13. https://doi.org/10.1038/s44172-024-00251-y

Nadi, A., & Moradi, H. (2019). Increasing the views and reducing the depth in random forest. Expert Systems with Applications, 138, 112801. https://doi.org/10.1016/j.eswa.2019.07.018

Nieto, P. J. G., Gonzalo, E. G., García, L. A. M., Prado, L. A., & Sánchez, A. B. (2024). Predicting the critical superconducting temperature using the random forest, MLP neural network, M5 model tree and multivariate linear regression. Alexandria Engineering Journal, 86, 144–156. https://doi.org/10.1016/j.aej.2023.11.034

Oshiro, T. M., Perez, P. S., & Baranauskas, J. A. (2012). How many trees in a random forest? In Machine learning and data mining in pattern recognition (pp. 154–168). Springer. https://doi.org/10.1007/978-3-642-31537-4_13

Parent, B., Shneider, M. N., & Macheret, S. O. (2011). Generalized Ohm’s law and potential equation in computational weakly-ionized plasmadynamics. Journal of Computational Physics, 230, 1439–1453. https://doi.org/10.1016/j.jcp.2010.11.012

Primantara, A., Ciptomulyono, U., & Al Kindhi, B. (2024). Bagging system performance analysis using artificial neural network, random forest regression, linear regression, and support vector regression. 2024 IEEE International Symposium on Consumer Technology (ISCT), 618–622. https://doi.org/10.1109/ISCT62336.2024.10791247

PT Petrokimia Gresik. (2023). Annual report PT Petrokimia Gresik. https://petrokimia-gresik.com/page/laporan-tahunan-2023/print

Qu, M., & Zhang, X. (2020). Design of sealing transformer for vacuum packaging machine based on single chip microcomputer control. In Multimedia technology and enhanced learning (pp. 204–213). Springer. https://doi.org/10.1007/978-3-030-51100-5_18

Qu, M., & Zhao, D. (2020). Design of anti-interference control system for vacuum packaging machine based on wireless network. In Multimedia technology and enhanced learning (pp. 193–203). Springer. https://doi.org/10.1007/978-3-030-51100-5_17

Swamidass, P. M. (2024). Manufacturing variability. In Encyclopedia of production and manufacturing management (p. 825). Springer US. https://doi.org/10.1007/1-4020-0612-8_1032

Vandeput, N. (2021). Data science for supply chain forecasting (2nd edition). De Gruyter.

Wang, W., Zhang, Z., Wang, L., Lan, H., & Xiong, Y. (2024). Forecasting the market value of power battery industry chain: A novel RRMIDAS-SVR model. Journal of Management Science and Engineering, 9(4), 474–489. https://doi.org/10.1016/j.jmse.2024.06.004

Yashin, S., Koshelev, E., Ivanov, A., Borisov, S., Karimov, D., & Babkin, I. (2025). Evolutionary neural simulation of import substitution in the radio electronics industry using adaptive network-based fuzzy inference system. International Journal of Technology, 16(3), 758–768. https://doi.org/10.14716/ijtech.v16i3.7402

Zerehsaz, Y., Sun, W., & Jin, J. (2024). Quality prediction using functional linear regression with in-situ image and functional sensor data. Journal of Quality Technology, 56, 195–213. https://doi.org/10.1080/00224065.2023.2293869

Zermani, M. A., Miled, A. B., Elhossiny, M. A., Elghazawy, M. A. I., Mahmoud, A. F. A., & Abdalla, F. A. (2025). Fractional order jellyfish optimizer on pid cascade controller for new born incubator system. Alexandria Engineering Journal, 125, 1–19. https://doi.org/10.1016/j.aej.2025.03.111

Zhu, Z., Geng, Y., & Sun, D.-W. (2021). Effects of pressure reduction modes on vacuum cooling efficiency and quality related attributes of different parts of pakchoi (Brassica chinensis L.) Postharvest Biology and Technology, 173, 111409. https://doi.org/10.1016/j .postharvbio.2020.111409