Design and Development of Control and Monitoring System for Plastic Shredding Machine Based on the Internet of Things Using the MQTT Protocol

Sa'adilah Rosyadi, Hafidz Nur Ihsa Ananta, Aris Nasuha, Hartoyo Hartoyo, Saifullizam Puteh, Joko Slamet Saputro

Abstract

Plastic shredding machines require appropriate control and monitoring to maintain stable operation and provide mechanical performance information. This study aims to design and develop an Internet of Things (IoT)-based control and monitoring system for a plastic shredding machine using the Message Queuing Telemetry Transport (MQTT) protocol. The system integrates a NodeMCU ESP8266 V3, PZEM-004T sensor, alternating current (AC) light dimmer, relay module, 16 by 2 Inter-Integrated Circuit (I2C) liquid crystal display (LCD), Adafruit IO platform, and an Android application. It enables users to remotely control the motor and monitor voltage, current, and power. System performance was evaluated based on measurement accuracy, response times, motor rotational speed, and plastic fragment dimensions. The PZEM-004T tests showed average errors of 0.065 percent for voltage and 1.599 percent for current. Control response times averaged 1.52 seconds (local) and 1.71 seconds (remote), while monitoring response times averaged 10.34 seconds and 10.43 seconds, respectively. Increasing the duty cycle from 75 to 99 percent increased the motor speed from 1035 to 1397 revolutions per minute, and the shaft speed from 1381 to 1840 revolutions per minute. Consequently, average fragment widths decreased from 1.08 to 0.50 centimeters for plastic bottles, and from 0.68 to 0.26 centimeters for plastic cups. The results demonstrate that the developed MQTT-based IoT system successfully provides remote motor control and electrical monitoring while enabling the evaluation of motor speed impacts on fragment size.

Keywords

Contriol, Monitoring, Internet of Things

Full Text:

PDF

References

[1] E. Setyaningsih, S. D. Hariyanto, D. Wahyuningtyas, and S. Kristiana, “Performance improvement of the shredder machines using IoT-based overheating controller feature,” Jurnal Infotel, vol. 14, no. 4, pp. 329–337, 2022, doi: 10.20895/infotel.v14i4.812.

[2] A. Gabriel and F. Cruz, “Open source IoT-based collection bin applied to local plastic recycling,” HardwareX, vol. 13, Art. no. e00389, 2023, doi: 10.1016/j.ohx.2022.e00389.

[3] A. Henaien, H. Ben Elhadj, and L. Chaari Fourati, “A sustainable smart IoT-based solid waste management system,” Future Generation Computer Systems, vol. 157, pp. 587–602, 2024, doi: 10.1016/j.future.2024.03.056.

[4] A. Daas, B. Sari, F. Semchedine, and M. Amad, “Optimizing waste management with integrated AIoT, edge computing, and LoRaWAN communication technologies,” Internet of Things, vol. 31, Art. no. 101546, 2025, doi: 10.1016/j.iot.2025.101546.

[5] C.-M. Rosca and A. Stancu, “Innovative AIoT solutions for PET waste collection in the circular economy towards a sustainable future,” Applied Sciences, vol. 15, Art. no. 7353, 2025, doi: 10.3390/app15137353.

[6] A. Garcés-Jiménez et al., “Industrial Internet of Things embedded devices fault detection and classification. A case study,” Internet of Things, vol. 25, Art. no. 101042, 2024, doi: 10.1016/j.iot.2023.101042.

[7] J. Singh, A. Singh, H. Singh, and P. Doyon-Poulin, “Implementation and evaluation of a smart machine monitoring system under Industry 4.0 concept,” Journal of Industrial Information Integration, vol. 43, Art. no. 100746, 2025, doi: 10.1016/j.jii.2024.100746.

[8] R. Pučnik et al., “A waste separation system based on sensor technology and deep learning: A simple approach applied to a case study of plastic packaging waste,” Journal of Cleaner Production, vol. 450, Art. no. 141762, 2024, doi: 10.1016/j.jclepro.2024.141762.

[9] J. Son and Y. Ahn, “AI-based plastic waste sorting method utilizing object detection models for enhanced classification,” Waste Management, vol. 193, pp. 273–282, 2025, doi: 10.1016/j.wasman.2024.12.014.

[10] L. M. C. P. L. de Freitas et al., “OPC UA and MQTT performance analysis within a unified namespace context,” Internet of Things, vol. 33, Art. no. 101734, 2025, doi: 10.1016/j.iot.2025.101734.

[11] L. Wibowo, R. Wahyusari, T. Yuwono, and A. Shofia, “IoT-based high-accuracy monitoring system for on-grid photovoltaic power system using NodeMCU ESP8266 and PZEM004T,” Journal of Mechatronics, Electrical Power, and Vehicular Technology, vol. 15, no. 2, pp. 230–241, 2024, doi: 10.55981/j.mev.2024.823.

[12] S. Gil, G. D. Zapata-Madrigal, R. García-Sierra, and L. A. Cruz Salazar, “Converging IoT protocols for the data integration of automation systems in the electrical industry,” Journal of Electrical Systems and Information Technology, vol. 9, Art. no. 1, Feb. 2022, doi: 10.1186/s43067-022-00043-4.

[13] N. Ferraz Junior, A. A. A. Silva, A. E. Guelfi, and S. T. Kofuji, “Performance evaluation of publish-subscribe systems in IoT using energy-efficient and context-aware secure messages,” J. Cloud Comput. (Heidelb.), vol. 11, Art. no. 6, Jan. 2022, doi: 10.1186/s13677-022-00278-6.

[14] A. Cho, T. Y. Kim, C. K. Kim, S. Choi, and S. K. Lee, “IoT data dissemination scheme for reducing delay in multi-broker environments,” Internet of Things, vol. 25, Art. no. 101025, 2024, doi: 10.1016/j.iot.2023.101025.

DOI: https://doi.org/10.20961/jeeict.8.2.125375

Refbacks

  • There are currently no refbacks.