How is AI Transforming Chemical Industry?

Introduction

 

In a report published at World Economic Forum and Accenture, the total chemical sales in 2014 were worth $3.5 trillion which is expected to touch $6.9 trillion by 2030.

 

The chemical industry is undergoing severity amidst the COVID-19 pandemic, the demand and supply fluctuations have forced the manufacturing production to match short-term volatility, resulting in reducing working capital and operating costs. 

 

While these challenging factors are not new to chemical producers, they are urged to adopt greater speed and agility to cater current market landscape and to prepare for a new market in real-time.

 

In this pandemic, production has been hit hard for synthetic rubber, coatings, fibre plastic, etc serving to feed automotive, building and construction industries (a dark side of the chemical industry in a pandemic). 

 

An exciting side entails chemical manufacturers producing antibacterial products, hand sanitisers, disinfectants, personal protective apparatus and many more.

 

All in all, supply and demand imbalance made a new economic equation of scarcity that question the company’s ability to maintain mass production, introduce new developments or initiate more productive research.

 

To be compelling in this race, artificial intelligence and machine learning support companies to respond with agility, drive effective business decisions, boost performance with novel approaches and ideas.

 

As per the report submitted by IBM, 80% of chemicals industry executives surveyed say AI will be important to the success of their business in the next three years. 

 

It is essential to integrate the chemical industry with more advanced technology. In order to understand where chemical companies can put AI in their business performance, we will discuss the opportunities and ideas in this blog.

 

The Shape of Chemical Industry

 

As per the report provided by Nexocode, 96% of the manufactured goods rely on chemicals as raw materials, and chemical commodities are producing over $4trillion in global revenue of the chemical industry annually. 

 

The above-stated statistics show a highly competitive global industry with the following segmentation;

 

1. Petrochemicals: Products from chemical reactions yielded from petroleum and other fossil fuels or renewable fuels using refinery process.

2. Agrochemicals and Fertilizers: Products used in agriculture and farming for health and growth of plants using fertilizers and pesticides.

3. Commodity Chemicals: Products used like plastic, chemical fibres, detergents, solvents, cleaning agents, etc used for industrial purposes.

4. Speciality Chemicals: Products used for specific functions such as paints, coatings, colours, food additives, perfume fragrances, etc

5. Pharmaceuticals: A particular branch of chemical with final products (raw drugs, compounds, or materials) and restrictions.

 

(Related blog: Quantum Computing in drug discovery) 

 

 

6 Ways AI Can Reshape Chemical Industry

 

1. Optimizing production cycle with AI/ML

 

Some of the most harmful materials and resources are bundled in the early stages of product development/manufacturing in the chemical industry. 

 

Companies can opt for manufacturing processes that are cleaner, greener, and long-term sustainable thanks to excellent AI and ML mechanisms and research. AI has already made considerable strides in international markets. 

 

Companies have already begun their decarbonization journey with the invention of cutting-edge procedures such as sophisticated molecules.

 

2. Reducing chemical wastages and maintaining greater efficiency

 

An accidental incident in the chemical manufacturing process frequently results in massive wastages to be thrown away and scraped off. Avoiding inefficiencies and creating uniform batches of products is a frequent technique among chemical industries.

 

With the advancement of AI and machine learning, the entire production process is automated, and product consistency is maximised. This improves efficiency and reduces chemical waste, reducing the carbon imprint in the environment.

 

3. Employing data analytics to analyze carbon-out

 

Nowadays, companies are responsible for their pollution and carbon impact by more than a dozen environmental and regulatory authorities. Engineers can now track the company's carbon emissions in real-time using data analytics and advanced machine learning tools. 

 

A higher output variance to raises an alert and the entire manufacturing process is stopped until the issue is resolved. 

 

There are also AI-based technologies that forecast carbon flow and out in a production process and recommend alternate resources that can significantly cut carbon generation.

 

4. Evolution and breakthrough in Research and innovation

 

The chemical business is a high-volume, high-mix industry with a lot of room for experimentation. With groundbreaking AI and predictive analysis research, computerized permutations and combinations using AI and ML techniques aid advanced research in recognising molecules, generating formulas, and determining chemical amounts and mixtures. 

 

This, combined with AI and ML's ability to process millions of possibilities, could result in a breakthrough method that is not only efficient but also helps accelerate decarbonization.

 

5. Precautionary Measurements and Predive Forecasting

 

In today's industrial world, competition is high, therefore it's critical for chemical companies to be prepared and proactive. By forecasting future maintenance requirements, AI and machine learning systems can help a lot. 

 

Advanced analogues and mathematical models can also be used to forecast demand for oil, gas, and other raw materials, allowing corporations to optimise their supply chains and minimise any delays and cost increases at the last minute.

 

6. Data analysis for molecular activities studies

 

Years of analytical investigations, laboratory experiments, clinical trials, agricultural trials, and other methods are required frequently in order to fully comprehend chemical products and use them for intended outcomes. Faster outputs, which can eliminate manual errors and reduce labour, are necessary these days. 

 

Machine learning and AI systems can successfully feed and churn massive amounts of data, as well as evaluate it systematically. This data processing and analysis will aid in distinguishing between chemicals that are beneficial with few or no adverse effects from those that are harmful or do not have the desired results. 

 

Apart from the pharmaceutical and pesticide industries, the chemical industrial sector, which is heavily reliant on chemicals, can also benefit from AI and machine learning.

 

In addition, by calculating the damage caused by dangerous pollutants, AI and machine learning can help the chemical sector speed up its efforts to combat climate change. This will assist businesses in making necessary improvements to their machinery and operations in order to reduce pollutants emitted into waterways and the atmosphere.

 

While these AI and machine learning applications span a wide range of applications in the chemical industry, there is still a lot of research and development going on in this subject. 

 

It will be exciting to witness additional transformations brought in by the usage of artificial intelligence in the chemical industry as the globe begins on a new adventure of Industry 4.0.

 

AI, machine learning, data analytics, and other breakthrough technologies are rapidly evolving. The chemical industry, on the other hand, has been resilient to adopt new technology. 

 

Chemical firms will recognise that utilising technology to drive a beneficial balance between production and the environment is the only solution as the focus on climate change, carbon-neutrality, sustainability, going green, and ESG compliances grows.

 

 

Conclusion

 

The fact is the chemical industry is facing challenges from all directions, chemical companies are continuously investing efforts to balance demand and supply alongside maintaining production, permitting novel developments, and exploring new areas.

 

They are willing to plan for huge transformations and innovations to differentiate themselves in a competitive marketplace. 

 

AI is already proving its worth as an increasingly fundamental part of day-to-day living. The industry will soon be similarly affected. AI has the potential to disrupt every aspect of a chemical company’s business model, from customer interaction and operations to sales and R&D. At the same time, it can open the door to new product and service offerings. Accenture.

 

Undoubtedly, in this regard, AI is a significant aspect supporting corporations to respond quickly and decisively while driving business operations with new and innovative ideas.