With advanced robotics, genetic engineering, artificial intelligence (AI), and other rapidly advancing cutting-edge technologies, we are now in the fourth Industrial Revolution.
We are seeing fundamental shifts in how we work, the most pronounced of which is in manufacturing.
An example of this is in the many industries that are now integrating AI into their operations, and the chemical manufacturing industry is no exception.
AI can make chemical manufacturing processes more efficient and cost-effective. Machine learning capabilities make it possible for manufacturers to achieve greater precision and accuracy, and AI can help when we look at pursuing greener options, such as reducing our carbon footprint.
In this post:
Key Takeaways
AI can help make chemical manufacturing more energy-efficient and cost-effective
AI allows for continuous improvements in chemical manufacturing processes
It facilitates better predictive maintenance and quality control
Integrating AI systems will help chemical manufacturers sector to be more environmentally-friendly
There are several challenges to this, such as added costs of retrofitting existing machinery
How can AI be used in chemistry?
Chemistry requires a high level of accuracy and precision, and that is an area in which AI excels.
From professional chemists to chemistry students, synthesising and analysing substances is a core part of chemistry. However, the consistency and reliability of your results are likely to vary depending on your level of skills and knowledge – and that gains even more complexity when you’re looking at it on an industrial scale.
AI can significantly improve the consistency and reliability of results by automating different aspects of chemical manufacturing while adapting to new situations and anticipating problems.
But integrating AI isn’t just about automation and pre-programming different aspects of chemical manufacturing. The entire process can be continuously improved through machine learning. AI can continually adapt based on the huge amounts of data that can be collected during manufacturing processes.
For example, in fairly simple processes like mixing and blending solutions, an AI system can continually monitor the concentrations, detect impurities and proportions, and make precise adjustments based on predetermined standards. A beneficial byproduct of this is that material and energy efficiency can also significantly be improved.
Current applications of AI in chemical manufacturing
AI will have (in fact, is already having) a profound impact on the chemical manufacturing sector.
It will change many of the fundamental aspects of the industry, from research to pollution control. There are four key areas of the chemical manufacturing sector where AI is currently being implemented:
- R&D
- Manufacturing products
- Forecasting and planning for contingencies
- Managing/assessing risks
Let’s look at each of these in turn.
Research & development
AI can significantly help in discovering new compounds, possible molecular structures, complexes, and mixtures. This is especially true of the pharma side of chemical manufacturing.
Hypothetically, there should be billions of possible permutations and combinations of chemicals. An AI can be fed with a large amount of information and can come up with millions of possible variations.
What AI has to play with are 118 known chemical elements (only 92 of them are naturally occurring), at least millions of carbon-based compounds, and about 500,000 known inorganic compounds. Where could this take us?
Manufacturing chemical products
It’s not enough to simply automate manufacturing based on pre-programmed commands. It’s equally crucial that the processes must be capable of adapting and improving.
AI integration can collect and analyse large amounts of data in real-time. Improvements can be suggested or immediately applied equally quickly.
Risk management
AI can create various models of certain scenarios that could negatively impact manufacturing and even the logistical aspects of the business. For example, it can assess the potential injuries of employees based on their job descriptions and locations at any given time in a chemical factory. Based on these assessments and models, work policies and safety protocols can be effectively designed.
How is machine learning used in the chemical industry?
Machine learning is an application of AI technology. Instead of just programming machines to simply perform certain tasks, they are given some level of autonomy to adapt to different situations based on collected data.
For example, additional catalysts can be automatically added into a mixture of reactants the reaction has slowed down given certain conditions. By doing this, production efficiency can be maintained despite sudden and unexpected changes or variations in certain parameters.
4 benefits of AI in chemical manufacturing
Integrating AI in chemical manufacturing has a wide range of benefits, which include optimising productivity, achieving better efficiency, and maintaining high levels of quality control.
Enhancing process optimisation
Chemicals can only react in certain proportions and under certain conditions. Some reactants may need the presence of catalysts, while others may require high temperatures and pressures to occur.
The process of chemical manufacturing also has intermediate byproducts that require further processing to derive the final desired product.
AI can design various processes that lead to the same desired results with minimal waste of materials and energy. It can also minimise the production of unwanted intermediate byproducts, which may become pollutants. Different chemical pathways can be explored by models created through artificial intelligence.
Efficiency
In the most simple and general terms, efficiency is the ratio between input and output multiplied by 100%. The higher the percentage, the higher is the efficiency rating. This means that optimal outputs must be achieved at the smallest possible amounts of inputs. This applies to labour inputs, energy inputs, and material inputs.
An AI system can analyse the productivity of a factory by including various input factors and the resulting final products. It can design labour-saving routines, better energy sources and distribution, and optimal proportions of materials used. This can be done in various combinations and under different scenarios.
Predictive maintenance
Many internal and external factors can affect the production efficiency of a chemical manufacturing company.
One example of an internal factor is the inventory of raw materials or chemical feedstocks. AI can help predict how much raw material is left at any given time so that the purchase of supplies can be efficiently managed. Market forces, such as price fluctuations, can also be included in creating models.
Quality control
Quality control is more reactive after the fact, seeking to correct errors rather than preventing them.
An AI system can not only help in finding defective products in a batch, but also prevent similar mistakes or at least minimise those errors at a very minimum statistical level.
Constant monitoring and assessment of the various steps of manufacturing a product can easily be handled by an AI system.
Challenges of implementing AI in chemical manufacturing
The primary challenge of implementing AI in chemical manufacturing is the retrofitting of the system in existing, older technology systems. A manufacturer may need to totally replace equipment and/or electronics. There will also be a need to hire experts to install, operate, and maintain the system, which could translate into a significant investment.
Another significant challenge to AI implementation in chemical manufacturing is data modelling. The system needs a large amount of data to be accurate and precise. It has a learning curve, which can be a bit messy at first.
Of course, existing data gathered over years of chemical manufacturing operations can serve as a basis – but if this isn’t internally available, bear in mind that external sources won’t be as reliable because every factory design, operational routine, and system is slightly or significantly different. It’s crucial that AI should be trained using the available data and operational data of your specific factory.
Conclusion
The applications of implementing AI in chemical manufacturing are largely positive and beneficial in many respects. It can help a chemical manufacturing company improve its production efficiency, safety, and environmental impact. However, it’s not without risk or difficulty, and it will probably entail added costs and significant retrofitting.