Introduction:

Chemical manufacturing industries are facing numerous challenges, product complexities, and stringent environmental regulations. They strive to remain competitive in the difficult marketplace by increasing their operational efficiency at reduced costs. The advanced software systems and automation technologies enable horizontal integration of systems, and harmonization of processes while maintaining quality and production optimization. This article covers the automation technologies adopted by some of the leading market players in the chemical sector and the challenges of implementing the technologies.

“Siemens’ software manages horizontal integration and harmonization amongst processes”

The Siemens OPcenter: The solution for production manufacturing in the chemical industry:

Chemical industries are facing challenges in their process of reducing costs, and increasing efficiency while maintaining high product quality. Siemens’ Opcenter portfolio is a one-stop solution for all these problems. Here are a few sectors or ways of how it works in the chemical industry.
 

Rosemount 628 Universal Gas sensors by Emerson Automation Solutions:

In July 2019, leading market player Emersion Electric Co. launched the universal gas sensor for manufacturing plants that aids in measuring carbon monoxide and oxygen depletion. The sensor has helped industries in optimizing processes, environmental monitoring, early detection of gas leaks, equipment malfunctions, process deviations and prevention of downtime. These factors have helped in saving pricey repairs, prevention of equipment damages and infrastructure.

Thanks to the power of wireless network technologies the sensors have potentially increased the toxic gas safety at the manufacturing sites.

The key advantage of using the sensor is its simplified installation and maintenance. The Rosemount 928 Gas monitor sensor is equipped with hot-swappable main components, which include the power module and the sensor itself. They are intrinsically safe and can be easily replaced without the help of any tools. Additionally, the parameters/information are stored in the sensor and not in the transmitter, therefore the users can calibrate the sensors in a non-hazardous location and take it to the field for installation. This factor enhances personnel safety by cutting down their time spent in hazardous locations.

On the other hand, the sensors face similar challenges as any other sensors such as drifts, environmental interference, and cross-sensitivity, where the sensors respond to multiple gases present in the environment leading to false readings or inaccurate readings.
 
Automation solves complexity… Emerging digital technologies play a vital role in process integration, one of the leading market players ABB has observed that the power management systems and integrated process management systems cut down on energy usage by up to 10%.

BASF uses Machine learning - Prediction of chemical processes:

Researchers at BASF are after real process data to assist them in their work, thus they utilize machine learning to predict the solubility of complex chemical mixtures, dyes, aging catalysts and so on, and the technology has brought in concrete industrial benefits.

Machine learning technology helps in understanding or predicting the relevant properties even when there is an inclusion of a large number of components in the process.

BASF Automotive Refinish solutions – leveraging AI:

BASF is a major market player in the automotive paint industry whose products are highly regarded as they are in line with environmentally friendly standards. They offer high-quality products and optimize paint application processes which adds value to their service.

The company employs AI to enhance its automotive paint colour-matching processes. The conventional colour-matching processes are carried out by simple visual inspection and subjective judgment, which are prone to heavy human error. However, the BASF’s Automotive colour matching AI employs machine learning algorithms that automate and enhance the overall process as the system is provided with a vast dataset of automotive paint colours, shades, finishes and substrates. These data are collected from real-time samples and automotive applications. Then the system performs color analysis with the existing database and it uses advanced image process techniques and spectral analysis. The researchers can distinguish between various colours as the system tags the colours with specific paint formulae, mixing rations, application techniques and other characteristics.

The automation technique has minimized the requirement for manual adjustments and reworks.

Conclusion:

Chemical manufacturers easily navigate a fast-changing consumer marketplace. The difficult marketplace with reducing margins encourages industries to please consumers who are inclined towards healthy living and sustainability. Therefore, the industries are trying to understand the environmental impact created by their facilities. Overall automation in chemical industries is highly appreciated as they have aided in safer manufacturing as it reduces human intervention in hazardous environments or prevents them from carrying out risky tasks.