Winning formula: How AI is changing the chemical sector

Nils Janus | Updated on July 05, 2021

It is helping manufacturers make profits while improving quality and efficiency, alongside environment preservation

Artificial Intelligence (AI) has had a significant impact on all areas in the chemical industry with a great potential that has changed the value chain management, innovation, increased productivity and new channels to reach the market.

There has been a lot of enthusiasm to transform the industry using the latest technologies for generating, collecting and storing data at cheaper prices, together with advancements in computational power to address previously unsurmountable challenges. Companies have shown willingness to engage with technology to improve quality, level of service and operational efficiency.

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AI has huge potential to be applied in the early stages of product development, which helps to beef up innovation drastically. Productivity research can also be increased through AI-enabled strategies that demand quicker responses during the experimental design phase. Additionally, it has also enabled the capability to integrate the product life cycle and sustainability goals to arrive at a targeted solution. In cases of predictive manufacturing, reliable data becomes an essential factor that can be freely used through well-planned AI models.

In international markets, AI has made industrial work much cleaner and safer. It has also led to the invention of advanced molecules that suit customer needs. Obviously, there are uncertainties considering the complex use of technology and its rapid evolution, but understanding AI is also critical to face competition in the industry.

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Revolutionising impact

An unintentional event in chemical production often leads to an unplanned process stoppage, rework or scrap, a major downfall that halts the project. A common problem faced by chemical manufacturers is the need to recognise and avoid inefficiencies so as to improve chemical process control. For the implementation of AI solutions, manufacturers have started leveraging technologies that help in identifying and optimising production proficiency through:

Machine learning process: Through AI, a process-based technique can be established to give manufacturers all-around visibility into in-depth detailing, issues arising in the process, and stages where urgent attention is required.

Real-time data: Access to capturing and connecting data helps manufacturers build a system to use secure production line assets — to safely store data and create a central time series repository.

Visualisation through digital twin: An AI method that enables the visual representation of matching attributes and operational metrics. This helps production teams to easily identify anomalies in performance, causes, and actionable insights, thus reducing dependency on data scientists.

Industry application

Every organisation has a pre-determined blueprint to adhere to when it comes to applying processes, tools, and techniques. Let’s take a look at how AI can be used for broader areas in the chemical industry:

Innovation through R&D: The majority of players are focusing on research that can provide fast and accurate results through AI. Machine learning tools and the use of computerised permutations and combinations help in advanced research to recognise molecules, generate a formula and ascertain the quantity of a chemical. AI helps in providing predictive analysis of whether the combinations used will lead to an innovation breakthrough. Innovations in the chemical industry can boost the efforts of various supplementary industries that are dependent on the chemical sector.

Forecasting: AI tools provide many insights into preventative measures and predictive forecasting, to enable industries to be proactively prepared for a challenge, in advance. Advanced analytics help in estimating demands for raw materials, streamline the supply chain that could lead to a potential delay and avoid last-minute price hikes. Several transformations at every step of molecule development in the chemical industry are possible through forecasting methods of AI.

Data analysis: Understanding complex products often involves laboratory experiments, clinical trials, and analytical know-how. However, there has been a shift in the timeline required to understand things. It is now faster to avoid errors through minimum efforts. AI tools feed and churn enormous amounts of data that can be systematically arranged and analysed. The application of AI and machine learning in the chemical industry also accelerates the efforts to combat climate change by guessing the damage done by harmful pollutants. This encourages companies to make essential changes in machinery and processes to reduce pollutants being released into water bodies and the environment.

Both economic and human investments in AI are generally very huge. The development of AI technologies is happening at a fast rate. However, adoption in the chemical sector can often be a challenge in terms of adequate support for innovation and research. Many industry-specific AI solutions will create a huge demand for digitally skilled operators to monitor, develop and maintain AI operations. Investments in skills and training are a constant priority for companies using AI. Already now, it is extremely critical for chemical industries to not just find technologies that bring profits but also aim at reducing energy consumption, realising circular economy responsibilities and reducing environmental impact. Saving costs without compromising on quality and safety is also an aspect to be taken into consideration.

The author is head of advanced analytics at Covestro

Published on July 05, 2021

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