Waste Management in Chemical Industry. How to Reduce Chemical Manufacturing Waste with AI

The chemical industry is one of the most polluting industries in the world. Every year, millions of tons of hazardous waste are produced by chemical plants. This waste includes toxic chemicals, heavy metals, and other harmful substances that can contaminate the air, water, and soil. Waste management is a critical issue for the chemical industry. If not properly managed, waste can cause environmental pollution and health problems locally for people living near chemical plants and globally by affecting the climate and ecosystems.

The good news is that AI-based solutions can help reduce waste in the chemical industry. AI can be used to track and predict the release of hazardous chemicals, optimize production processes to reduce waste, and provide decision support for waste management. In this article, we will explore how artificial intelligence can be used to reduce the amount of waste produced by chemical manufacturers. We will look at how AI can help identify and prevent waste production, as well as help manage and recycle waste products.

Industrial Waste – The Biggest Factor in Global Waste Generation

As the environmental consciousness is rising, the topic of waste is treated more and more seriously. In recent years, the weight of the public debate has shifted from personal to corporate responsibility. That’s a reasonable trend, considering that industrial waste makes up the majority of the global waste generation.

On a more local scale, things are no different. Based on the European Commission data ( Eurostat), it is clearly visible that the households’ and services’ contribution to waste generation is minor compared to industrial activity. In 2016, the percentage share of these sectors was below 14%. Construction was in the first place, but industrial activities were just behind (manufacturing (11.1%), mining and quarrying (27.6%).

With its 9.5 % share, wastewater contributes to the problem. While urban wastewater has its fair share in the overall waste generation, industrial wastewater is the real dealbreaker since it often contains much more problematic substances in higher concentrations. As a result, its treatment is quite costly and contingent on specific requirements.

Chemical Waste Management

This issue manifests itself in chemical manufacturing, which is a subject of today’s article. While cooling water can be treated and brought back to the consumption circle, the one used for chemical manufacturing may not be suitable for such treatment. The same applies to solids. That’s why the crucial part of chemical waste management is reducing waste. It is also a core principle of lean manufacturing, a production process management methodology that the majority of today’s chemical plants follow. Originating from Toyota production system, lean management puts waste elimination at its core while employing a continuous improvement principle.

In recent decades, scientific efforts and technological development have allowed chemical companies to reduce manufacturing waste significantly. There is still a lot to be done, however. As general global consumption continues to rise, we need more radical and efficient ways to prevent excessive industrial waste production. Through the course of this article, we will investigate how machine learning can contribute to it.

Chemical waste can be divided into liquid, solid, and gaseous, and may include pure chemicals, often unused or expired, acids, solvents, used oil, nitrogen, bleach, metals, etc. Any manufacturing equipment and containers contaminated with chemical substances also classify as chemical waste. Read more about chemical supply chain here.

The chemical industry uses enormous amounts of water, too – for cooling but also for manufacturing purposes (in processes such as distillation, refining, product rising, etc.). Once used, this water contains substances that are often highly toxic and resistant even to a few-degree sophisticated water treatment, like pesticides or so-called “forever chemicals” (PFAS) - a class of synthetic substances that do not break down.

So far, these most persistent chemicals continue to be produced and applied in products. Machine learning has contributed to their detection and classification in recent years - we’re still getting to know them even though they were invented in the 40s. Other resistant contaminants, with the main culprit being the pharmaceutical manufacturers, include estrogen and antibiotics.

Chemical waste requires a particular approach to storage (for instance, many substances cannot be stored in standard containers made with plastic or glass). Recycling is also more complex than the case of communal waste due to safety measures. Nevertheless, the chemical industry is getting better at transforming waste - according to the EPA (US Environmental Protection Agency), in 2020, only 3% of it was released into the environment. The remaining part was handled with treatment, energy recovery, and recycling.

Waste Reduction Methods – Machine Learning Use Cases

Whereas communal waste generation cannot be prevented or controlled at the source, the industrial one can. Considering the impact the chemical substances can have on the environment, and how resistant they can be, the reduction-oriented production planning seems the best path toward the industry’s greener future. The post-treatment of the waste is, of course, important, but preventive measures allow the companies to avoid pumping money into costly processes and focus on optimization instead.

The following use cases display the role machine learning can play in reducing chemical waste. We approach the topic from different angles to show you the whole spectrum of its capabilities.

Increasing Yields and Reducing Excessive Waste with Predictive Analytics

Increased efficiency is often pursued in regard to savings, but in the case of chemical manufacturers (or any other manufacturers, actually), it may also reduce the environmental impact. Waste reduction simply pays off for the chemical plants. The less they lose in the process, the more they produce and – likely – sell. Artificial intelligence can help them identify the most efficient ways to use particular products while generating the least waste possible.

AI-Based Quality Inspection and Predicting Quality Deteriorations

Faulty batches constitute a big part of waste produced by chemical plants. Thorough Quality Assurance can thus be a great way to reduce the quantities of defect waste. With artificial intelligence, the manufacturers can identify the issues on the assembly line before the whole batch turns defective. Intelligent systems can, for instance, detect cross-contamination right away with real-time data provided by sensors. AI models also have the capability to prevent it from happening by constantly evaluating the processes and detecting any deviation from the norm.

Defect detection can be computer-vision powered. Contrary to common conviction, visual inspection is also applicable to chemical substances. By analyzing different variables – color, stratification, density, physical state, etc., the machine learning can detect defects in chemical products and prevent excessive waste generation.

Establishing a Preventive Maintenance Schedule

Aside from the financial loss, any downtime in a chemical plant can lead to excessive waste production. That’s because some substances cannot be subjected to the same treatment or process twice. They may also lose their properties during the downtime period. That’s why it is crucial for companies to keep the assembly line going. And there is no better way to prevent failure in equipment than machine-learning-powered maintenance.

AI can optimize the company’s preventive maintenance schedule to detect possible failures before they actually occur. It can be approached in different ways, depending on the variety of the data sources or the equipment the company uses. The strategy choice determines which model will be applied in the process. For instance, the regression models serve well for predicting the remaining useful lifetime (RUL) of a particular asset.

The efficiency of predictions obviously depends on the volume of data and its quality. For high accuracy, the model should be fed with both historical, real-time, and static data from different sources, including sensors, ERPs, and other systems that provide the context.

Reducing Water Waste

Chemical plants use water extensively to support various ongoing processes. Its big part goes for cooling purposes. Since the chemical reactions that are a part of manufacturing processes may require using heat or emitting it, the cooling system is essential for the plant to function effectively and safely. Whereas in other industries, the measures regarding the quality of cooling water are not that strict, the chemical manufacturers need to ensure its purity is the highest. Any contamination could compromise safety and affect the product’s composition.

Thus, their water waste reduction strategy usually focuses on maximizing the cooling tower efficiency. Machine learning allows them to keep the cooling processes going with less water. Provided with the data, the model can find leakages and estimate the amount of water lost in a particular part of the process. Based on that information, the plant manager or other person responsible for the efficiency of the manufacturing processes can decide which measures could reduce the water waste.

Eliminate Waste with Closed Loop of Manufacturing System

Waste water recycling is another crucial part of the waste reduction strategy. It is becoming more and more common to turn the parts of the chemical plants into closed-circuit units in which the water is continuously reused for different purposes.

Manufacturers can use different types of treatment to purify the wastewater, depending on the impurities it may contain. While particulate solids can be handled with filtration, other contaminants require more elaborate methods. Machine learning often supports the decision-making processes regarding treatment planning.

The classification model can quickly analyze the sensor data of the water sample to detect the contaminants and categorize them as biological or synthetic, solid, liquid, etc. Trained with the historical data containing the treatment-related parameters, it is able to recommend the most efficient combination in terms of water usage, energy usage, etc.

AI Applications in Recycling

Another way for machine learning to support the waste reduction in chemical manufacturing is intelligent recycling. A well-trained model can streamline the recycling of the waste water but also the products and equipment used in the manufacturing process, such as containers, pipelines, etc. The companies usually combine machine learning systems with computer vision for sorting purposes, enabling the automated elements to recognize the type of waste and evaluate its suitability for recycling.

In such a case, the image from the capturing device gets sent to the interpreting one. The classification model trained with the images of different waste types evaluates the input data and attributes a category to it in order to define waste. On this basis, the system makes an automated decision on where to place a particular piece. An additional step is to verify whether the waste can be recycled and categorize it based on the type of treatment it requires.

Planning Production Based on Demand

Chemicals are subjected to equally strict norms in regard to their useful life as food products. That can fuel waste generation, particularly in the current economic landscape that strengthens fluctuations in demand.

While in the past, companies would rely on statistical methods to estimate demand, now they can reach out for machine learning to obtain accurate estimations. Using deep learning techniques, they are able to identify patterns in the provided historical data and apply this knowledge to predict future demand.

With such support, manufacturers can create production plans that reflect the probable demand instead of relying just on seasonality factors. With that comes a more flexible approach to ordering ingredients. And that means fewer expired materials and finished products that are problematic to utilize and recycle.

The Benefits of AI in Smart Manufacturing and Smart Waste Management

With smart AI-driven systems, chemical plants can make better-informed decisions based on accurate outputs. In big units, the number of ongoing processes can be hard to keep track of and analyze, which favors excessive waste generation. The machine learning models perform these mundane, error-prone analytic tasks, finding areas for improvement in terms of waste generation and waste management without any human engagement.

With predictive analytics, chemical manufacturers are able to adopt a much more flexible approach to manufacturing and ordering. Instead of stockpiling materials that could expire, they adjust their orders to the demand estimated with relevant data and reduce inventory waste. Their production reflects the market needs, and most of their products fulfill quality standards thanks to AI-driven quality inspection and predictive maintenance. That means less waste and more income.

With machine learning-powered classification tasks, the chemical manufacturers can identify the contaminants faster, streamline their wastewater treatment, and recycling processes.

The Green Future of the Chemical Industry

As you can see, artificial intelligence impacts waste generation in chemical plants in various different ways.

Basically, every newly built chemical plant nowadays is planned with the closed-loop principle so that no waste water leaves the circle. As we have mentioned, only 3% of chemical waste water in the US is released to the environment, and hopefully, it will soon reduce to zero.

In the most optimistic scenario, machine learning will turn the manufacturing units into fully circular, partially sustainable systems that do not depend on external water sources for cooling and manufacturing purposes. Considering that the water resources are shrinking, and the climate prognoses are not very optimistic, it is urgent for the chemical companies to make this transition in the nearest future. Artificial intelligence makes it smoother and more accessible. Simultaneously, we are coming up with new, water-efficient and more effective treatment plans streamlined with ML classification.

Do you have an idea for applying ML technology to your business? Or maybe you would like to hear more about its applications in chemical industry from our side? Reach out to us so that we can talk!