Artificial Intelligence in the Chemicals: How AI Benefits the Chemical Industry

Artificial Intelligence in the Chemicals: How AI Benefits the Chemical Industry

Dorota Owczarek - May 12, 2022

The chemical industry is one of the most important and largest industries globally. It is a critical part of our economy and our way of life. The production and manufacture of chemicals is a complex process, and it has always been challenging to find ways to improve efficiency and productivity while reducing costs.

This article will explore how artificial intelligence can be used in the chemical sector to bring significant improvements. We will look at some specific use cases where AI has been successfully applied to chemicals. We will see why it is vital for companies in this industry to invest now in machine learning solutions.

The Shape and Multiple Subbranches of the Chemical Industry

In recent years, we had a chance to observe a dynamic evolution of the chemical industry due to increasing consumer awareness. The solutions we have been relying on for decades are beginning to fall by the wayside and the new ones are emerging. How does the situation look like across the subbranches of the chemical industry?

Petrochemicals

Petrochemicals

Numerous products across industries rely heavily on the ingredients derived from petroleum and other fossil fuels (gas, coal). Their big part serves for the manufacturing processes of everyday products, like cosmetics, detergents, and drugs. Take the shampoo or that superlight water-resistant jacket you use for the mountain trips – the currently used methods of their production enforce the usage of carbon-based resources.

With an ongoing debate on the need to turn back from the non-renewable resources, the manufacturers are starting to experiment with alternatives. It’s crucial since the petrochemical manufacturing process is not only toxic but also energy-intensive. Artificial intelligence can help refineries switch to renewable fuels like palm oil or maze, taking advantage of the existing infrastructure.

It’s worth noting, however, that these renewables exclude the toxicity issue but still contribute to environmental exploitation. Synthetic biology is an emerging field of science that may solve this issue by coming up with commercially valuable alternatives to petrochemicals that do not affect the environment and demand low energy.

Agrochemicals and Fertilizers

Agrochemicals and Fertilizers

As of today, worldwide welfare is strongly dependent on agrochemicals and fertilizers. Within the current consumption system, efficient large-scale farming seems the only way to fulfill the constantly growing demand for crops. Agrochemicals enable it - but at a high cost. After decades of the excessive usage of such products, the farmers are experiencing their negative impact - which is scientifically proven. The biggest producers are getting sued for causing health damage with their widely available products (the most renowned case is Monsanto’s Roundup in the US).

Toxicity is one side of the coin - and on the other, we have the dependency created by the frequent use of these products. Artificially supported, the plants lose abilities to defend themselves against infections and pests. And on the other hand, the bacteria and fungi that attack them are becoming increasingly resistant, challenging the manufacturers of agrochemicals. Artificial intelligence can be a tool to fight this process by facilitating research on less harmful, safer chemicals that do not affect the environment but also do not stimulate fast immunization of the pests.

Commodity Chemicals

Commodity Chemicals

Commodity chemicals (or bulk chemicals) are essential for manufacturing products such as rubber, plastics, chemical fibers, detergents, solvents, adhesives, or cleaning compounds mainly used for industrial purposes. Many of these products, as we’ve already mentioned, rely heavily on fossil fuels, and changing it is currently one of the priorities among the manufacturers due to the increasing pressure on reducing the environmental impact of the chemical industry.

Commodity chemicals, however, can also come from different sources, and not all of them are organic compounds. Among the inorganic chemicals most commonly used in manufacturing, we can point out ammonia, nitric acid, sodium carbonate, and chlorine. The spectrum of organic chemicals in common use is much broader, including alcohols (ethanol, methanol, butanol), acids (acetic acid, acrylic acid, terephthalic acid), glycols, and ketones, among others.

Specialty Chemicals

Specialty Chemicals

Contrary to the commodity chemicals that find various applications across chemical industries, specialty chemicals are manufactured for a particular purpose, serving a specific function or industry. That means upgrading them and finding more efficient and safer alternatives may take a little more time, considering that fewer companies can contribute to it.

Under this umbrella term, we can distinguish such products as paints, coatings, colors, cosmetic additives, flavors, food additives, fragrances, etc. Pharmaceuticals are a significant branch of this category, requiring a particular emphasis on security measures and quality assurance.

Can AI Be the Catalyst of Innovation for the Chemical Industry?

We believe so! Artificial intelligence is revolutionizing numerous sectors, and the chemical industry is one of these where we see the most dynamic AI-driven development in recent years. In chemical manufacturing, it finds its applications in predictive analytics, quality control and quality assurance, and automation of production-related processes. AI is successfully adopted for commercial purposes but also in scientific research, helping the scientists come up with new molecules and reactions in a relatively short time.

With more and more emphasis being put on the environmental responsibility of the chemical industry, the companies can use artificial intelligence-based tools to search for environmentally friendly alternatives for polluting chemicals and solve the most pressing resource and waste-related problems.

The chemical industry involves big money, and costs are usually the main factor that stops the companies from investing in innovations to implement in their manufacturing processes. With artificial intelligence, they can fall significantly, unlocking funds for improvement. That’s because the innovations in this field are highly dependent on time-consuming, detailed scientific research, and AI has the power to speed it up without compromising accuracy. All while cutting the workforce demand since a big part of the process is automatized.

AI Applications in Chemistry and the Chemical Industry

To illustrate the potential of the artificial intelligence in the chemical industry, we’ve gathered examples of its use for different purposes.

Predicting Properties of New Molecules and Compounds

Artificial intelligence can streamline the early drug discovery process and design of new chemical combinations, predicting the properties of the chemical products that would otherwise have to be subjected to a time-consuming manual analysis. In the pharmaceutical sector, this way, the drug’s standard road to market can be cut even by half or more.

With the property prediction, the manufacturers can estimate the physicochemical and structural properties of chemicals. It helps them determine the drug-likeness of the pharmaceutical product, its toxicity, chemical reactions, pharmacological AMDE properties (absorption, distribution, metabolism, and excretion), and other features that may have significance for the manufacturing process right at the beginning of the discovery phase.

This way, data scientists can exclude the combinations that do not meet the standards or have a low potential before the process begins and moves to the development phase. Considering that only 25 to 30 percent of drugs pass from the III phase to the drug approval process (Delloite), the game is worth the candle.

How is it done? The property prediction analytics uses deep learning algorithms that get trained with properties-related data to find patterns on the basis of which they can predict the behavior of the chemical under different conditions. Machine learning models can assist in exercising super-fast research with the help of computerized permutations and combinations. Deep learning models can also support identifying the right molecules, generating formulas, designing new chemical processes, and aid in knowing the precise quantities of different chemicals required. AI-based advanced analytics can aid in predicting chemical combinations that can be a breakthrough in terms of innovation in the chemical sector.

Discovering New Compounds and Designing New Chemicals

There are billions of drug-like molecules yet to be discovered, and their discovery is slowed down by the limited capabilities of the experimental facilities. Deep learning models can speed chemical production up by coming up with a new combination that represents a set of specific properties.

It can be done via different approaches, with the most effective being the so-called “de novo drug design” which applies generative models like GAN to develop the new compound based on the set of the provided criteria. Let’s say you want a non-toxic, highly soluble compound resistant to stratification and with adhesive properties. Provided with these requirements, the generative model can carry out an inverted engineering process, coming up with a desired combination.

Our blog article on drug discovery and development sheds more light on this fascinating process if you’re interested in reading more about it.

Improving the Chemical Manufacturing Process for Better Quality and Increased Yields

The role of machine learning goes far beyond the discovery process. Once the chemical gets approved for production, the manufacturer can control its quality on the assembly line using AI-fueled tools.

Defect and Anomaly Detection

Most modern factories use IoT solutions to gather real-time data from the equipment and shorten reaction time via automation. With the use of visual inspection, they can also identify the defects and anomalies in the machinery. It also serves for evaluating chemical products based on their properties (for example, color, texture, stratification, solidity, etc.).

This way, there’s no need to test every product individually at the early stages of production. By preselecting the defective products and removing them from the assembly line before they get into the further manufacturing stage, the companies can significantly reduce their operational costs and reduce the risk of returns from the market.

In this article, we take a closer look at the ways artificial intelligence can improve the chemical manufacturing process. If you want to know more about this aspect, give it a go!

Predictive Maintenance

Aside from identifying the issues on the assembly line on the go, the chemical companies can go a step further and prevent them with predictive maintenance. Machine learning algorithms can estimate the end of equipment’s lifecycle and point out issues that may lead to costly downtimes in the future.

AI-Based Chemical Process Control

Chemical manufacturers have to deal with high-level unpredictability on a daily basis, which can affect the quality of the end product. In order to ensure satisfactory results, they need to engage methodologies that take this variability into account, like AI-fuelled PAT (Process Analytical Technology).

As we wrote previously in our process control-focused article, by adopting PAT tools, the chemical companies can develop a dynamic manufacturing process adjusted to the variance in raw materials and equipment. With each testing cycle, these tools verify the post-process quality and introduce adjustments, continuously learning about the impact of each variable on the effectiveness of the system.

Process Analytical Technology (PAT) as a continuous learning framework

Process Analytical Technology (PAT) as a continuous learning framework

Efficient Production Process Planning

Cost-effectiveness is every manufacturing facility’s ultimate goal, attainable thanks to predictive analytics. The AI-driven tools enable them to plan the production process accordingly to dynamically changing market demand. They can combine historical and real-time data to come up with accurate predictions and adjust to them, reducing the likelihood of overproduction and generating waste as a result.

Minimizing Excessive Waste

Since we got to the topic of waste – AI is a powerful tool for wastewater management, enabling chemical companies to reduce their environmental impact related to water usage through advanced analytics. With AI-driven tools, they can prepare complex treatment plants that cover water reuse, and pollutant removal while maintaining cost-effectiveness.

Minimizing Energy Use

That’s not the end of the possibilities of predictive analytics in terms of manufacturing efficiency. Using machine learning algorithms, the manufacturers can forecast and predict energy consumption. With increasing pressure on reducing carbon footprint among manufacturers (particularly in the EU and the US), such practices based on ongoing data analysis are becoming a standard. Automated software can analyze real-time data from the equipment to identify the most inefficient areas and processes in terms of energy use, sometimes even suggesting energy-saving solutions.

Would you like an even bigger picture of predictive analytics in chemical manufacturing? Here’s our take on it.

AI-Based Digital Twins for Chemical Manufacturing

Processes gain more visibility if they are transferred to digital. By creating a digital twin of the manufacturing facility, the companies can gain a better understanding of the ongoing issues and find safe and efficient solutions via simulation. Using their digital twins, they can create numerous scenarios with slightly different variables to get prepared for the possible market changes and predict the impact of any particular decision. This way, it becomes possible to make decisions that are almost entirely risk-free as they were previously tested on the exact digital copy of a physical facility.

Improving Chemical Supply Chains

Due to the complexity of the chemical manufacturing process that involves various ingredients, the availability of which is often fluctuating, the chemical companies have to pay particular attention to securing their supply chain. With AI-fuelled systems with advanced analytics, they can keep track of their diversified sources of materials and optimize planning production schedules according to the market changes. You can read more about AI in the chemical supply chain here.

The chemical supply chain diagram

The chemical supply chain diagram

Successful Use Cases of AI in the Chemical Industry

That all sounds very promising, but at this point, you probably would like to see a practical example of AI’s application for chemical manufacturing industry to get a better picture of the most common challenges and issues. If so, we are redirecting you to our case study that documents a successful implementation of predictive data analytics for the batch production in pharma.

Predictive Analytics in Chemical Batch Manufacturing

Predictive Analytics in Chemical Batch Manufacturing

Related case study: APIs Production Process Predictive Monitoring

To improve the current repeatable batch production processes, a producer of active pharmaceutical ingredients approached us to implement AI models and utilize predictive modeling.

Our challenge? Building a model that analyzes real-time data streams from the production process and identifies potential outliers that may lead to deterioration of quality, based on historical data. The benefits are improved effectiveness, predictability, and efficiency of manufacturing operations and yields. Read more on this case study.

What Are the Benefits of AI in Chemicals?

With such a broad spectrum of applications, the range of benefits of AI for the chemical industry is naturally wide, too. Chemical manufacturers can benefit from employing AI-based tools in many different ways, many of which have a positive impact on the customers and the environment. Most of these benefits contribute to the cost-cutting potential of artificial intelligence. The company’s operational costs fall with AI-driven:

  • productivity and operational efficiency increase (automation of processes)
  • optimize operations and laboratory experiments (predictive modeling)
  • reduced downtimes (predictive maintenance)
  • reduced workforce demand (automation of processes)
  • reduced waste (defect and anomaly detection/forecasting)
  • reduced energy consumption (process control/forecasting)
  • secured supply chains (predictive analytics/compound discovery)
  • reduced environmental impact (predictive analytic/compound discovery)
  • better end-product quality (process control/quality assurance with visual inspection)

Improved product quality reduces the probability of returns from the market and contributes to a positive image of the company. Advanced AI-based quality assurance techniques also help chemical manufacturers fulfill the demanding standards enforced by the local regulatory bodies.

What Chemical Manufacturers Can Do to See the Immediate ROI of AI?

Some factors influencing ROI are unpredictable, but with a few simple steps, the chemical company can secure the profitability of the investment. The most essential thing is to gain a deep understanding of the problem you want to solve with the introduction of the AI-based tool and set up the goals to fulfill. Such a solid framework will help you avoid missed decisions and wasting your budget on innovations that do not bring business value.

If working with an external software house on the AI and machine learning projects, it’s essential to arrange a project kickoff meeting during which the goals, expectations, KPIs, project timeframes, and budget are discussed by both parties. We also advise working on the initial idea in a collaborative format by joining AI Design Sprint workshops. A 2 or 3 days-long discovery workshop is a perfect tool for carrying out a brainstorming session and coming up with collective conclusions in this regard. This way, you can explore your machine learning proposal and experiment on your data in a super fast and agile way.

Since the success of every AI system depends on an effective data strategy, the company should make sure they choose the right one for them so that the AI and machine learning models provide them with the most accurate results and learn fast enough. Data analysis should be approached early on in the project to ensure a smooth machine learning process.

We can help you measure the ROI of AI – reach out to us any time! And if you want to delve deeper into the ROI-related issues, we suggest going over our article.

Why It Is Vital for Companies in This Industry to Invest Now in AI and Machine Learning Solutions?

Considering the current situation in the market, it is about time for the chemical companies to switch to AI-based solutions since they enable them to adopt more dynamic manufacturing strategies.

With predictive analytics and production planning, they can gain immunity against increasing fluctuations in gas prices caused by the dynamically changing geopolitical situation. Using their digital twins, they can prepare themselves for different possible scenarios to maintain their position on the market regardless of what happens. AI technologies may help them reduce their energy consumption to keep the costs low when energy prices are rising.

Cutting energy consumption is also significant in terms of increasingly strict regulations on CO2 emissions. In some countries (for example, Germany) chemical manufacturers are obliged to reduce their carbon footprint year by year, and with manufacturing management based on AI, it becomes much easier.

If you would like to prevent these factors from influencing your business, drop us a line!

About the author

Dorota Owczarek

Dorota Owczarek

AI Product Lead & Design Thinking Facilitator

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With over ten years of professional experience in designing and developing software, Dorota is quick to recognize the best ways to serve users and stakeholders by shaping strategies and ensuring their execution by working closely with engineering and design teams.
She acts as a Product Leader, covering the ongoing AI agile development processes and operationalizing AI throughout the business.

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This article is a part of

AI in Chemicals
6 articles

AI in Chemicals

Artificial Intelligence is a powerful tool that can help chemical companies work smarter and faster. The technology allows for more productive processes by automating tasks, providing insights into how chemicals react or improving manufacturing environments.

Follow our article series to find out the applications of AI in Chemicals and how this tech benefits companies from the whole sector that operate in petrochemicals, agrochemicals, commodity, and specialty chemicals.

check it out

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