Energy Efficiency in Chemical Industry - How AI in Chemical Manufacturing Can Aid Sustainability

In light of the energy crisis and global warming, it has become imperative for industries to focus on becoming more energy efficient. Energy consumption in the chemical industry is known to be one of the highest among all industrial sectors. Energy efficiency has become a top priority in chemical manufacturing, and many companies are turning to AI technology to help them achieve this goal. AI can help chemical manufacturers reduce energy consumption by optimizing their processes and improving their production methods. In this article, we will discuss how artificial intelligence can be used to bring sustainability into chemical manufacturing.

Global Energy Crisis and The Rising Energy Costs

We are on the verge of a new era in terms of energy use –  and in the following decades, we will most likely see a radical change in consumption patterns, not only on the individual level but also on a broader scale. The dynamically changing geopolitical situation has affected the fossil fuel market, fuelling changes that we have been putting off for years.

For a long time, the European Union has been promoting natural gas as a greener alternative to coal which could support EU countries in the transition to renewable resources. These politics had to be revised with the recent Russian invasion of Ukraine. Since its start, countries worldwide have started questioning this energy policy in terms of both environmental and moral perspectives. At the same time, the prices of fossil fuels skyrocketed, causing losses and affecting local economies.

With these implications, the European and US manufacturers agreed that gaining independence from the Russian gas delivery is a priority. That, combined with increasing pressure on sustainability due to the deepening climate crisis, enforces aiming toward renewable energy production and self-sufficiency through the use of solar panels and other sustainable solutions. Meanwhile, the essential goal for the industries is to reduce their energy consumption as much as possible to reduce their carbon footprint and cut energy-related expenses since, at this point, the majority of energy still comes from fossil fuels.

Energy and CO2 Management for Chemical Manufacturing

The debate on fighting the climate crisis has evolved significantly in recent years, shifting the burden from individual actions to mobilizing the biggest emitters. Even though chemical manufacturing is mainly associated with pollution, it has a significant impact on global carbon dioxide emissions, too.

While it is the largest consumer of oil and gas among industrial subsectors, its carbon print stays far behind the cement, iron, and steel industry. The reason is simple – a big part of its energy input is consumed as feedstock. That said, its contribution to climate change is still not to overlook.

According to 2017 EEA/Roland Berger data, industrial activity was responsible for over 20% of the total greenhouse gas emissions in Europe, and the majority came from energy consumption rather than industrial processes. Over 12% of these originate in the chemical manufacturing industry. As IEA 2020 report lays out, in 2018, direct CO2 emissions from primary chemical production were 880 MtCO2.

No wonder the success of the campaign against climate change strongly depends on the chemical industry. Countries around the world take that into consideration, creating regulations and certification systems that favor environmentally responsible manufacturers. In some, like Germany, they are even obliged to reduce their carbon print every year.

The European Union, recently followed by China, uses Emissions Trading Scheme to regulate the emissions in the industrial sector and allow the companies to evolve at their own pace. But as the number of emission rights is limited, they are becoming increasingly expensive, forcing companies to pursue energy efficiency.

How Much Energy Does the Chemical Industry Use?

The total energy usage in a chemical plant depends on various factors, including lighting, space heating, space cooling, water heating, electronics, ventilation, refrigeration, machinery, and data management. Surprisingly, lighting has an enormous impact on overall consumption. Manufacturers often underestimate its importance, prioritizing the modernization of other systems. As we will prove further in the article, introducing a well-planned lighting system can be a game-changer in terms of electricity use.

Reducing the energy consumption of the manufacturing units is so challenging because it involves various systems (like HVAC) and elements that also impact working conditions. It is not hard to push things too much in one way, losing that healthy balance between energy efficiency, comfort, and work safety. Modern machine learning-based systems can help companies find the middle ground, reducing their environmental impact while keeping processes efficient.

How Can We Save Energy and Reduce Greenhouse Emissions in Chemical Plants?

Since greenhouse emissions are so dependent on electricity use, introducing energy-saving solutions is the most effective method to reduce the carbon print of the chemical plant. Of course, the issue is more complex, as the company’s overall emissions depend on the supply chain and other factors going beyond actual production processes. Nevertheless, energy consumption reduction inside the chemical plant is crucial here. As we’ve already mentioned, machine learning-based systems make it possible without negatively affecting the working conditions. They can test different scenarios and observe which settings are simultaneously saving energy and favoring safe and productive work. Read more about how AI can help with chemical supply chain HERE.

AI for Energy Efficient Chemical Manufacturing Processes – Use Cases

AI is already changing the chemical industry landscape. One of the most common purposes of using artificial intelligence in chemical manufacturing is to increase the efficiency of the processes on the assembly line - also in terms of energy consumption. How exactly can the AI tools streamline them to generate savings? Here are some examples.

Optimizing Operational Efficiency of Chemical Manufacturing with Predictive Analytics

The detailed data extracted during the batch manufacturing process enables AI-based systems to anticipate defects, anomalies, and other issues on the assembly line.

With extensive metrics (pressure, temperature, density, viscosity, etc.), a predictive model trained with historical data can identify insights that could imply upcoming issues. By engaging the sensors and cameras integrated with interpreting devices powered by ML models, the manufacturers can make their AI-fuelled systems detect anomalies in products or machinery. Predictive analytics in chemical manufacturing can help companies boost their efficiency and profitability. At the same time, they monitor the service life of the latter, foreseeing its necessary exchange. This way, they can prevent downtimes which is crucial to the overall operational efficiency.

Every downtime equals energy waste since restarting the assembly line is a very energy-consuming process. Plus, it has an additional cost that aggravates the environmental footprint of the chemical plant. The first days after the restart of the assembly line, the chemical products are more likely to be defective, particularly if their recipe is quite complex. So, downtime prevention enabled by predictive analytics contributes not only to productivity but also to the overall energy efficiency of the factory.

Increasing Yields with Reduced Energy Consumption

How to produce more while using less energy? AI helps chemical manufacturers solve this puzzle. Using predictive analytics, the chemical companies can maximize their output (which, depending on their priorities, could be production volume, revenue, customer satisfaction, etc.) while minimizing their energy input. Deep learning models learn from big data to identify variables tied to both high productivity and low energy consumption and suggest settings that include the most optimized combinations of these.

Minimizing Excessive Waste

AI can help chemical manufacturers solve the pressing issue of wastewater by optimizing its management and reducing its production volume. Trained deep neural networks can set optimal wastewater treatment recommendations based on real-time data, facilitate the control of disinfection and sludge treatment, and optimize the removal processes that enable reusing wastewater for manufacturing. Less wastewater equals less energy wasted on its recycling.

As for now, waste of material is inevitable in chemical manufacturing, particularly when it comes to the production of complex chemicals with precisely defined property requirements. However, AI tools can reduce it to an absolute minimum. The predictive models trained with assembly line data are able to identify the dependencies between the calibrations of particular processes (temperature, stirring velocity and frequency, etc.) and the amounts of waste produced. After recognizing these patterns, they estimate the wasted material in different scenarios.

Another step can be using generative models to come up with optimized recipes that lead to less energy waste while maintaining optimal properties of the final product. Artificial intelligence can also serve for property prediction. It can reduce the environmental impact of the produced chemicals by lowering their toxicity but also their energy consumption. For example, with the help of deep learning, the manufacturers can find combinations of ingredients and raw materials that fulfill their requirements without requiring high temperatures, vast amounts of water, and time-consuming treatment.

Energy Efficiency Improvements in Chemical Plants

Aside from optimizing the manufacturing processes themselves, the chemical manufacturers can polish supporting systems in the chemical plant in order to maximize energy savings. Here’s how.

Turning to Renewable Energy

As the most dependent on gas among all the industrial subsectors, chemical manufacturing is turning to renewables now that this energy source is no longer a safe option. Many manufacturers introduce photovoltaics to cover their electricity demand, at least partially. These solutions are integrated into the AI-fuelled energy management systems so that the chemical plant can make the most out of solar energy in real-time. Storing it is not that efficient or easy due to the current limitations of battery production (and its environmental implications).

Applying Energy-Efficient Technologies

As we’ve mentioned before, lighting has a significant impact on a chemical plant’s energy consumption, and the artificial intelligence capabilities of its optimization go far beyond using sensors. The systems powered by machine learning can identify energy-efficient lighting settings that favor productive work and reduce the frequency of errors. In addition, they may adjust the intensity and distribution of the light to the current conditions inside the chemical plant using real-time data and combining it with detected productivity patterns.

Another interesting way to reduce energy usage and carbon print is by producing energy from waste heat. As a side product of practically any manufacturing process, it may serve as an energy source instead of being released or emitted. In the case of chemical plants, these processes include ventilation and heating, wastewater treatment, and much more. The AI can collect the data from the respective systems (for example, the HVAC system) to identify the waste heat sources that can later serve the purpose of energy production.

What Are the Benefits of Using AI for Energy Efficiency in Chemical Production?

Prioritizing energy efficiency doesn’t have to come at the cost of productivity and financial safety. Actually, it’s quite the opposite - most of the AI-based technologies described above have a positive impact on their profit, either in the short-term or long-term. While saving energy, the chemical manufacturers achieve their primary goal - producing more at a lower cost. Plus, they secure their financial stability, not depending on the fluctuating energy prices.

Reduced energy consumption and carbon footprint also unlock new business opportunities for chemical manufacturers. Fulfilling the criteria of the local regulatory bodies, they can get certificates that grant them priority in various bids.

Let’s not forget about the image-building aspect of energy efficiency. Having lowered their CO2 emissions, the manufacturers are also more likely to find growth-stimulating business cooperations. Their carbon print also matters for environmentally-conscious customers.

Green Chemistry in Chemical Manufacturing - The Future of Sustainable Chemical Industry

Considering the recent development of recycling technologies, the future of the chemical industry seems bright, at least in terms of material sustainability.

The innovative startups keep coming up with technologies that enable recycling PP plastics and PET into virgin-quality plastics ready to be brought back to the market. With hydrogen and CCUS (carbon capture, utilization, and storage) technologies gaining momentum, energy management efficiency is more accessible than ever. It no longer requires investments that pay themselves only in a few decades. The photovoltaic installations are getting increasingly affordable, empowering companies to become partially or even fully independent of energy deliveries.

In the following years, the pressure on sustainability will likely get stronger and stronger, forcing the chemical manufacturers to optimize their processes and systems in terms of energy use. Having worked with the chemical industry for a long time, we are here to make this transition smoother with artificial intelligence solutions. If you could use our support in this field, reach out to us!