The chemical manufacturing industry is as broad as the process optimization challenges it faces. Unsurprisingly, AI can be a life saver!

AI in chemical engineering

The chemical manufacturing industry is as broad as the process optimization challenges it faces. To optimize production lines, chemical manufacturing companies need to address different process inefficiencies like forming undesired side products, process instabilities, or losses due to impurities. 

Given the complexities of chemical manufacturing, it’s incredibly time-consuming and challenging to understand the root causes of these process inefficiencies, let alone anticipate when they will happen. The specific behavior of the combination of multiple production parameters, or tags, cause the inefficiency to happen. 

A growing number of chemical manufacturers turn to Industrial AI solutions to identify and anticipate process inefficiencies leveraging supervised and unsupervised ML methods.

Recent research by Accenture shows that companies that have implemented Industrial AI in the chemical sector are seeing significant benefits—a whopping 72 percent report a minimum 2x improvement in some process KPIs, and 37 percent a 5x improvement. 

With the capabilities Industrial AI has to offer, chemical manufacturers can utilize their data, improve their processes, and continually adapt them.

Applications of AI in the chemical industry

Molecular activity

Understanding chemical products and designing the right formulas can take years of analytical studies, laboratory experiments, and trials. However, faster results that can mitigate manual errors and minimize efforts are required. ML and AI tools are effectively used to feed and process, and analyze large volumes of data systematically. It helps to separate chemicals in a way that is effective with fewer or no side-effects compared to the ones that do not have desired effects or are toxic. Applying AI and ML in the chemical industry can also help fight climate change by estimating the damage done by harmful pollutants and remedy it by assisting companies to make essential changes in their machinery and processes.

Research and development

R&D is the backbone of the chemical industry. Prominent players in this industry are looking for focused R&D and innovation to yield faster and more accurate results following AI in the chemical industry. ML tools can help exercise this type of quick research with the help of computerized permutations and combinations. It can also help recognize the right molecules, generate formulas, and help know the precise quantities of different chemicals required. On the other hand, AI can predict chemical combinations that can be a breakthrough in innovation. 

Human-machine collaboration

Digital transformation and the introduction of AI and ML can make human-machine collaboration more successful than ever before. Any idea or hypothesis can be analyzed, tested, and streamlined to mitigate risks or errors even before it is physically put to the test. While humans take care of the creative side of things, machines take care of the drudgery. It saves time and effort. Such a combination of human and machine efforts can contribute to fast-paced innovations, productions, operations optimization, and other concurrent developments in the chemical industry.


Competition is steep in the chemical industry. Companies need to be prepared and proactive. And AI can help to predict future maintenance needs. Advanced AI models can estimate raw material demands so that the chemical companies can streamline their supply chain to avoid delays and last-minute cost hikes.

These AI and ML applications cover the broad spectrum when it comes to the chemical industry. That said, there is still a lot of R&D going on in this field. As the world embarks upon a new journey of Industry 4.0, it would be interesting to see more transformations brought in by the use of AI in the chemical industry.


By using process-based ML, manufacturers get focused and contextual predictive alerts. It is a massive opportunity for chemical manufacturers since operational technology (OT) data is already well organized and captured within data historians.

Leveraging this data with process-based AI means being able to pinpoint the root cause of process disturbances with extreme accuracy and predict process instabilities and failures before they have the chance to affect production.

So with industrial AI, chemical manufacturers can reduce quality and production losses, saving them significant amounts of time and money.

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