How will new industrial revolutions impact the palm oil industry? Cognitive computing involves self-learning systems that use data mining and pattern recognition, and natural language processing to model how the human brain works.
A term used to refer to the Industrial Revolution from around 1760 to 1820-1840, the First Industrial Revolution saw the adoption of steam and water-powered machinery and mechanization. Farming has improved through industrial technologies such as seed drill and iron plow.
During the 19th century, machines and metalworking techniques developed, leading to agricultural equipment like reapers, binders, and combine harvesters.
Manufacturing 2.0 developed around the late 1800s. When assembly lines, mass production, and electricity emerged in the cattle and meatpacking industries, tallows became more readily available, particularly in the oils and fats industry.
Industry 3.0 revolutionized the automation industry using computers and automation in the 1980s. Recently, Industry 4.0 has introduced important concepts in manufacturing, such as cyber technologies and cyber communication through the Internet of Things (101). Also, cloud computing and cognitive computing are embedded in Industry 4.0.
Technology meets manufacturing in a way that yields advanced manufacturing techniques combined with information technology (IT). With the availability of advanced manufacturing techniques, businesses can add value in new and different ways. IT and operations technologies are combined to create value in new and different ways.
Technology like Industry 4.0 can enhance productivity and reduce risk. These risks may include inventory, quality, process safety, and contamination.
A smart manufacturing process could be implemented along with robust supply chain management. Workers could be identified and removed from dangerous or tiring jobs and reskilled with tablets and other technological tools.
When it comes to Industry 4.0, palm oil is at various stages when it comes to Google Glass and applying advanced analytics.
Essentially, a palm oil plantation would suit the Industry 1.0 stage. a mill would suit the Industry 2.0 stage, and refineries and oleo chemicals would fit within Industry 3.0. Oleo chemical operations would be closer to Industry 4.0.
In 1956, palm oil plantations were a priority for the 1st Malaysian Plan to reduce dependence on natural rubber. Palm oil development was initiated with milling and crushing in 1960. This was followed by refining in the 1970s and oleo chemicals in the 1980s.
It takes labor to tap or harvest oil palms, but this kind of planting is labor-intensive. Regular harvesting is key to ensuring quality while reducing process contaminants’ impact:– MCPDs and GEs.in response.
This is one of the bigger challenges for the oil palm industry that may require government support and sovereign funds. The Malaysian Palm Oil Board offered a USS1M prize for 2014 for a mechanized harvesting solution. But the competition was not successfully performed, and the prize has been closed since 2017.
A drone can be employed on plantations for many applications, such as applying fertilizer and pesticides. Moreover, as this is a machine operated by another machine, measurements can be made and used to identify how to proceed in a future application.
It can also eliminate a safety hazard for human workers. We know from industry research that applying fertilizers and pesticides to palm oil can increase the level of chloride, leading to an increase in 3.MCPD levels.
MICRONES is one efficient palm oil extraction method. Kernels are separated from mesocarp before pressed, used in a process known as Maceration Induced Cell Rupturing Oil Nut Extraction Synthesis.
Another mill that was previously unequipped with SCADA is about to receive the upgrade to Industry 3.0 – which consists of SCADA technology that is new to the sector -, and it will make the mill more efficient and effective.
The difficulty of measuring multiple phase systems and the variability of the fresh fruit bunch means that many milling processes are not automated. Here, soft sensors can be used. Soft sensors are inferential models in which easily measured variables can yield a way to estimate complex measurement variables because of technological limitations.