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Introduction

Malaysia has a high concentration of palm oil mill industry. Malaysia's position as the world's second top producer of palm oil, Malaysia supplied a third of the world's palm oil in 2021, second only to Indonesia.  Currently, there is a market trend of growing palm oil prices, which is being driven by increased demand and better profitability for palm oil industry stakeholders, especially palm oil mill operators. To meet this increased demand, there have been requests for palm oil mills to modernise their "stone age technologies" in order to remain competitive. The sustainability of future palm oil mill operations must be addressed today, in tandem with the push to use new IR4.0 technologies, particularly the Internet of Things (IoT). IoT use in the palm oil mill will result in new synergies in production, operation, and maintenance management. The palm oil business necessitates stringent management, operational, and production processes to ensure palm oil mill productivity and optimum oil extraction rate (OER) with optimum management efficiency (Kadir et al., 2018). However, the majority of today's palm oil mills function conventionally, requiring a large amount of manual labour and the use of outdated technologies to keep the entire process running.

Feasible IoT Implementation in the Milling Process

The most common IoT application is for process monitoring. In mill process monitoring, IoT is used in conjunction with cloud computing to optimise dynamic processes (Yew et al, 2022). IoT can also be used in conjunction with smart image processing to detect equipment defects. IoT can also be utilised for remote process control and smart production management in conjunction with big data analytics. The use of IoT sensors in the area improves task efficiency by reducing any redundant measure readings in applications like temperature, humidity, current, voltage, vibration, water level, and other applications. Sensors by themselves do not fall under the category of Internet of Things; they must be coupled with a network that can send data signals to cloud databases, enabling a larger range of read and write operations. If the data can identify the facility's health state, users and the owner of the facility will be able to view the pertinent data whenever and wherever they are required to guarantee the facility is in excellent condition. Although RFID is an older technology, it is nevertheless seen as a component of the Internet of Things (Musabyli et al., 2020).

By scanning the RFID tag or using the sensors to identify the transporter, RFID and IoT-enabled sensors lessen the burden of the weighing procedure. To calculate the net weight of the FFB, each transporter must pass across the weighbridge twice (Yacob et al., 2005; Ferdous Alam et al., 2015). Machine learning can recognise the details of each vehicle that passes through the weighbridge thanks to the integration of IR4.0 into video surveillance systems. The programme should have aggregate data, such as the total number of palm fruit transactions and the frequency, in order to enable data analytics. The maintenance management team will be able to keep track of the equipment's performance with the use of IoT-enabled sensors installed on machinery like hydraulic equipment, capstan motors, thresher, digester, pressing machine, vibration screen, and others. The maintenance management will schedule preventative maintenance when abnormal sensor data from pertinent equipment is received in order to minimise unforeseen repair. Inspection of the wire rope used in the capstan motor is required since it frequently poses a serious health risk, such as the possibility of cable wires snapping and causing serious harm. Hall sensors employ an electromagnetic detection technique to find wire rope flaws (Zhou et al., 2019; Kaur et al., 2018).

Data from sensors can be used to schedule inspections to lessen the risk of an accident happening. Any unscheduled maintenance, such as lifting cranes, will cause the factory to shut down. There may not be backup units for every equipment in the palm oil mill. According to Shen et al. (2021), temperature sensors can help keep the temperature of the solvent used to extract the mesocarp fibres from palm oil at 68°C. However, the most yielding supercritical CO2 technology (Mohamed, Yusup, Wahyudiono, Machmudah, & Goto, 2014) requires a temperature of 90°C to extract residual oils from mesocarp fibre. As a result, different extraction techniques and solvents need different optimal temperatures to provide the best yield. 

Conclusion

Because the palm oil sector contributes significantly to the Malaysian economy, palm oil mill operations must adapt and adopt as much digitisation and automation as possible. Furthermore, because palm oil mills rely heavily on labour (which is now in short supply), it is critical for the palm oil business to digitise their operations as the next step towards enhancing productivity. As a result, technologies such as IoT have the potential to be game changers, allowing producers to be more efficient and productive. It is also unavoidable that the sector will need to capitalise on the most recent technology developments in order to remain competitive.

References:

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