If chemical companies want to remain competitive and move forward in a changing world, they must quickly adopt innovative technologies. Integrating the Internet of things (IOT) among these companies can provide important benefits.
If chemical companies want to remain competitive and move forward in a changing world, they must quickly adopt innovative technologies. Integrating the Internet of things (IOT) among these companies can provide important benefits. The combination of Internet of things and machine learning can promote the chemical industry to work more efficiently and create better results.
How can Internet of things improve chemical production? How can chemical companies use Internet of things and machine learning in their processes?
Using Internet of things to improve chemical production in chemical companies
Although many industries are embracing the Internet of things, its relationship with the chemicals business seems unclear. Andy chatha, President and CEO of arc, explained in a keynote speech at the industry forum of arc consulting group that the Internet of things can simplify many parts of industrial companies, including providing intelligent machines, providing better big data storage capacity, and helping optimize systems and assets. The benefits of the Internet of things in this industry are far-reaching. They include higher productivity, better asset utilization and higher income.
There are significant opportunities for faster R & D of products with higher value and profits, especially in specialty chemicals and crop protection chemicals. Advanced analysis and machine learning enable high-throughput optimization of molecules, as well as simulated laboratory testing and systematic optimization of formulations, from tube to tablet performance and cost.
BASF, for example, has worked with HP to develop a supercomputer that can simulate and predict the performance and performance of new industrial catalysts, crop protection products, materials and formulations. In addition, advanced analysis and machine learning can promote the allocation of the best available resources to research projects that meet portfolio priorities. It is possible to screen internal knowledge and patent databases to maximize the use of intellectual property and fill in gaps. Machine learning can also help chemical manufacturers simulate sustainability and environmental impacts over the life cycle of their products.
Changing the rules of the game for factory operations
The Internet of things has laid a foundation for machine learning in manufacturing and asset management. It can capture real-time data of asset status and performance, process parameters, product quality, production cost, storage capacity and inventory (telemetry), inbound / outbound logistics, worker safety, product and service matching, etc.
With the advanced functions of capturing, storing, processing and analyzing data, a large number of factory, asset and operation data can be combined with advanced algorithms to simulate, predict and specify the maintenance requirements of assets. This advantage increases availability, optimizes uptime, improves operational performance, and extends the life cycle of assets. Digital asset networks are emerging, bringing original equipment manufacturers (OEMs), operators and service providers together on a platform to promote collaboration on common standards and improve operation and maintenance efficiency.
In this case, digital twins play an important role in managing asset performance and maintenance. Once plants and processes are designed and engineered, digital twins can train operators by simulating special plant and process conditions related to safety and / or performance - just as flight simulators are used to train pilots. Digital asset twins can be used to predict the impact of some process parameters on asset performance, asset life cycle and maintenance requirements.
An industry report in 2016 explained the concept of digital twins, in which organizations create value from information by moving from entity to digital and back to entity. Another industry report pointed out that a petrochemical company used digital twin model to improve product transition by 20%. 2 even digital twin networks (Figure 1) have been proposed to improve interoperability throughout the asset life cycle and ultimately maximize asset performance
Distributed manufacturing / 3D printing brings new opportunities for the chemical industry in developing innovative raw materials and driving new revenue streams. More than 3000 materials are used in traditional parts manufacturing, while only about 30 materials are used in 3D printing. From this perspective, the market of chemical powder materials is expected to exceed US $630 million per year by 2020.
Worker safety can be enhanced by adding smart tags to wearable devices, which can help alert workers to exposure to hazardous substances (e.g., toxic gases), impending fatigue, and help locate employees and contract workers in emergency situations.