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AI to help industry reduce environmental impact

70%

In the pharmaceutical industry, over 70% of CO2 emissions come from the Scope 3 category, which are those linked to the value chain (production, transport, equipment, and technologies, etc.) 2 

The European Commission estimates that the chemical and oil refining sectors account for over 10% of drinking water consumption in Europe, equivalent to 5-10% of the world’s drinking water resources. And according to the World Report on the Reassessment of Water Resources Development Projections3, industrial water requirements are set to increase by up to 400% by the year 2050.

Water is an essential component in the chemical industry. It is used for heating and/or cooling products and equipment, creating a vacuum, generating steam, preparing solvents and reaction mediums as well as extraction and absorption reagents, rinsing and distilling products, transporting products, washing, and many other processes. Therefore, optimizing water usage is crucial to substantially reducing consumption.

AI solutions can provide the means to identify reduction opportunities throughout the value chain. With the help of sensors that capture data in real time, for example, the cost-productivity-energy consumption relationship can be more accurately assessed. This instantaneous analysis identifies points in the value chain where water savings can be made without compromising the quality of the chemical process in progress.

As with water consumption, using AI to analyze large quantities of data in real time helps detect energy losses throughout the production chain. This makes it a tool for predicting future energy needs, thereby providing valuable information for fine-tuning energy consumption in the most efficient way possible. Examples include adjusting production schedules to align with peaks in solar or wind energy and identifying leaks and resource wastage.

Another area where AI can have a significant impact is in optimizing transportation routes. Transporting raw materials or finished products to the consumer is a major source of CO2 emissions. Thanks to AI, it is now possible to intelligently manage logistics flows, and thus streamline transport timelines. This helps reduce fuel consumption and the corresponding emissions.

Using AI for predictive maintenance is another area where AI can play an essential role in energy savings. By identifying in advance which machines require maintenance, AI can not only help anticipate potential production stoppages and hence energy losses, but also help extend the lifespan of machines. In the long term, this contributes to enhanced profitability.

Testimonial

“At Servier, we believe that AI is an indispensable tool for optimizing our industrial energy consumption, provided the models we use are energy efficient. Currently, we use these technologies to analyze what the most efficient energy matrix would be for some of our industrial sites in France and internationally, factoring in the energy consumption characteristics and resources specific to each one. The aim is to roll out this integrated solution to all our industrial sites by 2030.”

Gwénaëlle Clément Industry Data & Digital Officer, Servier

Using new technologies, such as AI, requires a great deal of energy, particularly in terms of electrical resources. The impact of AI can therefore vary considerably, depending on the energy matrix chosen. At Servier, we are committed to increasing the proportion of renewable sources of electricity in our energy matrix. This proportion rose by 11 points between 2022-2023 and 2023-2024, and now stands at 19%. 


[1] IPCC, Special Report on Global Warming of 1.5 Degrees, 2018, https://www.ipcc.ch/sr15/
[2] THE PHARMACEUTICAL INDUSTRY’S CARBON FOOTPRINT AND CURRENT MITIGATION STRATEGIES: A LITERATURE REVIEW, IPSOR annual 2023
[3] A. Boretti and L. Rosa, ‘Reassessing the projections of the World Water Development Report’, Npj Clean Water, vol. 2, no. 1, Art. no. 1, Jul. 2019, doi: 10.1038/s41545-019-0039-9