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Sustainability in the Raw Material Industry With AI

Sustainability in the Raw Material Industry With AI

Raw materials sustainability is the term used to describe the environmental impact of our reliance on raw materials. It considers the depletion of natural resources, pollution, and other environmental damage caused by mining, processing, and transporting these materials.

There are a number of ways to approach raw materials sustainability. One is to focus on reducing our reliance on raw materials by using recycling and other means to close the loop on materials use. Another is to improve the efficiency of our use of raw materials, for example, by using more efficient production processes.

A third approach is looking for alternative sources of raw materials with a lower environmental impact. This might involve using recycled materials or sourcing materials from renewable or sustainable sources.

Each approach has its merits, and there is no one-size-fits-all solution. The best approach will vary depending on the particular circumstances and needs of each case. However, all three approaches are essential in moving towards a more sustainable future.

Types of Sustainability Practices in the Raw Material Industry

There are three types of approaches to sustainability in the raw materials sector: 

  • Primary
  • Secondary
  • Tertiary

Primary approaches focus on preventing environmental impacts at the source, such as by reducing emissions from production processes or using more environmentally friendly inputs. 

Secondary approaches seek to offset environmental impacts through carbon trading or investing in renewable energy. 

Tertiary approaches aim to mitigate environmental impacts after they have occurred, such as through waste management and pollution control. 

Which of these three types of approaches is most effective depends on the specific context and goals of the company or organisation pursuing sustainability. However, combining all three types of approaches is often considered the most effective way to achieve sustainability in the raw materials sector. 

Approaches to Sustainability

Companies are naturally weary of making large-scale changes to the way they operate. Changes divert resources in terms of both money and time that can be invested in other areas, and there is always a risk involved. What many people are not aware of is that increasing sustainability is actually a process. 

For example, a company processing raw materials doesn't need to instantly replace their equipment, something that can necessitate countless hours of extra training and millions of dollars of investment into new equipment. Rather, they can change the way they operate through incremental steps, presented through the examples below.

There are several approaches we can take when speaking of sustainability. These can, roughly, be divided into:

  • Optimising your current processes using data
  • Utilising circular economy principles
  • Utilising alternative materials
  • Implementing renewable energy sources during the production process itself

For the first approach, you don't have to completely change your systems and the machinery you use. First, you gather data, through various sensors. Then you use AI technology and machine learning to analyse and process said information. This helps you find weak points that you can optimise, which leads to better results (more on that below). 

Then, we have the usage of circular economy principles, in which waste products are recycled back into the production process. This reduces the need for new raw materials and helps to conserve resources. 

Another way to make raw materials more sustainable is by using alternative materials that have a lower environmental impact. For example, you could use recycled timber or bamboo instead of virgin timber for construction. These materials are just as strong and durable as traditional materials but have a much smaller impact on the environment.

Finally, using renewable energy sources to power the production process. This can be done by using solar, wind or hydropower. This clean energy can help offset raw material production’s environmental impact.

All these methods can help make raw materials more sustainable and reduce the environmental impact. Using these methods, we can build a more sustainable future for all.

Why Does Any of This Matter?

The importance of sustainability cannot be overstated. Besides the obvious impact on the environment, more and more legal regulations are being put in place to have companies adhere to sustainable practices. As just one example, the European Union struck a provisional agreement with the members of the European Parliament for new reporting rules. From 2024, large companies will need to publicly disclose information relating to how they manage environmental risks.

There are two ways we can look at the importance of sustainability practices. On one level, we can look at the company itself, how it operates, what are the energy needs of its production process, and how much waste does it create. On the other level we can observe the broader impact it has, on the local and global environment..Both of these are, of course, deeply interconnected.

First, many industries are highly dependent on a limited number of raw materials. This concentration of supply makes industries vulnerable to disruptions. By implementing sustainability practices, these materials can be used better, more efficiently, leading to less waste.

Second, the sourcing of raw materials from conflict-affected areas raises serious concerns about the sustainability of supply chains. There is a risk that companies operating in these areas may be complicit in human rights abuses or environmental degradation.

Third, the environmental impact of extracting and processing raw materials can be significant. This is especially true for metals and minerals, which often require energy-intensive processes such as smelting and refining.

Finally, raw materials play a crucial role in the global economy. They are essential inputs into a wide range of products, from automobiles to smartphones. Disruptions in supply can therefore have far-reaching economic consequences.

We come closer to the crux of this article – the introduction of AIs. Artificial intelligence is one solution that, if properly utilised, can help with almost every aspect of the issues and types mentioned above.

The Importance of AI Within the Raw Materials Industry (And Concrete Examples)

There are many different types of approaches that can be taken in order to make the raw material industry more sustainable with the help of AI. Some of these approaches include:

  • Implementing systems that can track and optimise the use of resources
  • Developing software that can help to improve the management of waste
  • Introduction of predictive maintenance software leading to highly improved equipment availability
  • Using energy saving algorithms to help with sustainability and costs across the board

Implementing systems that can track and optimise the use of resources

This first approach involves developing software that can help improve resource management and using data analytics to identify trends and patterns that could lead to more sustainable practices.

As an example, this means a company can maximise the yield from their production process. Using these systems, the company becomes more sustainable by spending less fuel, having less wear and tear on equipment, while having the same, or better, yield..

Developing software that can help to improve the management of waste

Similarly, algorithms dealing with the management of resources in a more optimal fashion are also very useful. They in turn can minimise wasted resources, both in terms of raw materials and money, but also in reserves needed to transport and process them.

These models, gained and created through the usage of machine learning and AI, can in practice find a weak point during production. Gathering data using these systems can highlight aspects of the process that can be modified, leading to far less waste being created.

Introduction of predictive maintenance software leading to highly improved equipment availability

Predictive maintenance is a vital part of sustainability. Algorithms and information that help with the development of these systems lead directly to less downtime and improved equipment lifespans.

Using data analytics, you can schedule equipment maintenance in a much more efficient way, minimising downtime and saving money and resources. Furthermore, by using predictive maintenance you can handle potential issues with your machinery before it becomes a serious problem.

Using energy saving algorithms to help with sustainability and costs across the board

Finally, energy saving algorithms can help you optimise how you use your machinery and fuel. Running heavy equipment in the raw materials industry, maintaining and cleaning it, all of this uses vast amounts of energy. This is not only costly, it's also harmful to the environment.

Using data analytics and applying energy saving systems and algorithms can substantially improve the way you use your resources, and can help you find very concrete ways you can lower energy expenditure, while not lowering yield or the quality of your end products.

In the video bellow you can learn about how you can maximise the efficient use of energy in your company:


Wizata case studies examples

The case study below shows the process of applying AI solutions to a cement producer. Once the entire process has finished, the company saw a 3% reduction in energy consumption, and a 25% increase in asset availability, as well as other significant cost saving and environmental results.


A global Cement Producer recently started an ambitious, multi-phased digital transformation journey to increase revenue by reducing production downtime, running operations at more than 95% efficiency, and reducing CO2 emissions. Key to this was taking the large volume of data points generated each year across its complex worldwide assets and making them available in a contextualised Digital Twin.

With Wizata, the company is experiencing significant throughput increase and cost savings. Project savings add up to about 2M €, using the Wizata Platform and embedded Machine Learning models. The project was also delivered on time and on budget and the company’s local resources maintain the system thanks to the Wizata knowledge transfer and change management techniques.


Priority in the upstream cement industry is to shift towards environmentally friendly production and increasing expectations for running world class operations. Thus, the plants face increasing pressure to reduce downtime, maximise margins and minimise environmental impact.

Bringing a solution to these challenges had the potential to efficiently improve two key business areas in phase 1 of the transformation:

Real Time Anomaly Applications

Data was present in the historian database, but a lack of contextualization and flexibility in data hierarchy and treatment made the leveraging and utilisation of data more difficult. For example, simple monitoring and anomaly detection models need to be applied on real-time telemetry data but the existing infrastructure made that difficult, as well as preventing the execution of new real-time calculations.

 Predictive and prescriptive maintenance on key assets like kilns, Pumps and ID Fans to reduce and predict time to failure. The empirical programmed maintenance shifted to Al based prescriptive maintenance.


Wizata and the local experts worked closely with business leaders to ensure that data collection and management are aimed at producing the biggest business impacts and generating measurable ROI.

The following activities were performed:

  • The Wizata platform hosted within customers Azure tenant
  • Ingest metadata from OSlsoft PI, MES, Historians and engineering data sheets
  • Design and build a process Centric Digital Twin based upon the target equipment
  • Transform and contextualise process and maintenance data
  • Use the embedded ML models of the platform and produce real-time recommendations
  • Build customised dashboards for relevant stakeholders


This case study showcases the power of asset data modelling and replication at scale. With all of this implementation, the company is experiencing a 3% reduction of energy consumption and significant decrease in CO2 emissions. Also, unexpected equipment stoppages decreased by 25%.

The Wizata Anomaly Detection system is currently running 20 models onto 35 assets included in the scope of the first phase. The scalability of the whole solution is facilitated by digitising each production process through Digital Twin and applying replication templates to adapt, deploy and maintain intelligence algorithms within the group.

Process engineers can now connect to the contextualised data within Wizata, respond to incidents in real-time and use action-based recommendations to prevent unplanned downtime and improve reliability.

Concrete Examples of Companies Using AI

Artificial intelligence (AI) is revolutionising the raw material industry. By automating processes and tasks, AI can help businesses improve efficiency and productivity and reduce costs. In this article, we'll take a look at some of the ways in which AI is being used in the raw material industry, with some examples from real-world businesses.

One company that is using AI in the raw material industry is US-based C2FO. C2FO provides an online marketplace that connects businesses that need working capital with investors who are willing to provide it. Using machine learning, C2FO's platform analyses a business's financial data and sets an optimal funding rate for each individual business. This means that businesses can get the funding they need at a rate which is fair and beneficial for both parties.

Another company making use of AI in the raw material industry is Australia-based Bluechiip. Bluechiip specialises in developing RFID tags and tracking solutions for the mining industry. Using machine learning, their RFID tags are able to automatically identify and track the location of minerals, ore and other raw materials throughout the mining process. This helps to improve the efficiency of mining operations and can also help to prevent theft and loss of valuable resources.

Finally, AI is also being used in the raw material industry to create new products and materials. US-based start-up Zymergen is using machine learning to design and develop new strains of microorganisms which can be used to produce a variety of chemicals and materials. This includes everything from plastics to pharmaceuticals, and the company is already working with some of the world's leading manufacturers to bring their products to market.

How To Get Started – Conclusion

With the world's population projected to reach 9.7 billion by 2050, the demand for raw materials will only increase, putting even more strain on the planet's resources.

But it's not all doom and gloom. There are a number of ways that the raw materials industry can become more sustainable, and many companies are already taking steps in the right direction.

One of the most promising avenues is the use of artificial intelligence (AI).

AI can help to optimise processes and increase efficiency in the raw materials industry, leading to less waste and fewer emissions.

It can also be used to develop new, more sustainable materials and to recycle existing materials more effectively.

In short, AI has the potential to transform the raw materials industry and make it more sustainable.

So how can you get started with AI in the raw materials industry?

Here are a few things to keep in mind:

Understanding what AI is and how it can be used in your business

AI is a type of technology that enables computers to learn and work on their own, without being explicitly programmed. This makes it a valuable tool for businesses that want to automate tasks or processes that are too complex or time-consuming for humans to do manually.

Hopefully the information and the case study analysed on this page already gave you an idea of what can be accomplished with this technology.

Identify the specific tasks or processes you want to use AI for

Before you can start using AI in your business, you need to identify the specific tasks or processes that you want to automate. This will help you determine what type of AI technology is best suited for your needs and how to go about setting it up.

Some specific tasks that you may want to use AI for include:

  • Automating routine tasks or processes
  • Analysing data to find trends or patterns 
  • Making decisions or recommendations based on data analysis
  • Improving efficiency or accuracy of tasks or processes

Set realistic goals you want to accomplish

Next, you want to have clear goals on what you want to accomplish. So, maybe you want to have an all around more efficient production process. Or, perhaps you want to focus on specific things, like minimising waste, maximising yield, etc…

Talking with a company that specialises in this type of technology can help you set realistic goals for your industry, and will give you a much more clear idea of what can be accomplished.

Collect data that will be used to train the AI system:

In order for an AI system to be effective, it needs to be trained on a large and representative dataset. This data will be used to teach the AI system how to recognize patterns and perform the desired task or process.

  • Some good sources of data for training AI systems include:
  • Historical data collected from your business or other sources
  • Datasets released by public institutions or organisations
  • Data gathered from online platforms or social media

Again, a company specialising in AI solutions can be of great assistance for this step.

Train the AI system and monitor its performance:

Once you've chosen an AI platform or tool, you'll need to train the AI system on your data. This process can take some time, so be patient and monitor the system's performance regularly. Make adjustments as needed to improve its accuracy and efficiency.

 Adjust the AI system as needed to continue improving its performance:

As your AI system continues to learn and grow, you'll need to make occasional adjustments to keep it performing at its best. This may involve changing the data that's used for training, tweaking the parameters of the system, or adding new features.