Optimizing Chemical Manufacturing with Artificial Intelligence
Artificial intelligence is a hot topic in today’s business world. Many companies use AI applications to optimize their operations and improve their bottom line. But what about chemical manufacturers? Can AI be used in this industry as well? The answer is a resounding “yes!” In this blog post, we will discuss the benefits of using AI in chemical manufacturing and outline a few specific applications that can help improve your production process.
Artificial Intelligence in Chemical Manufacturing
AI finds applications across various industries, but in the case of manufacturing, its possibilities are immense. Not only can machine learning improve the manufacturing processes, but it may also speed up the discovery and development of new, more optimized solutions.
The ultimate goal for the manufacturing units within the chemical industry is to produce more while improving quality and reducing costs. That wouldn’t be possible without automating the repetitive tasks. But there’s much more to it — AI can also help manufacturers detect downtime and leakages, monitor and optimize the resources and energy consumption, or, what’s even more important, control the quality of the production process with advanced analytics.
In the chemical industry, the usage of AI technologies is actually way less common than in transportation or finance, but in recent years, it has started gaining momentum. Since the chemical manufacturers carry great responsibility and need to stick to strict quality management protocols, automatization may have been approached by them cautiously. However, now that AI has gone mainstream and proven its potential, the chemical manufacturers are becoming much more open to its implementation. The intelligent industry 4.0 approach can significantly impact this sector — not only because of its capability to maximize productivity and profit but also the ability to reduce the chemical companies’ environmental footprint.
Considering that chemical production is responsible for the majority of emissions within the industrial sector, implementing AI could bring a considerable positive environmental impact. It could make the usage of energy and resources more effective (which translates into a reduced carbon footprint), reducing toxic waste, fuelling the discovery of new eco-friendly solutions at the same time. But before delving into these aspects, let’s take a closer look at how machine learning can improve manufacturing processes.
How Can AI Improve a Chemical Manufacturing Process?
Due to their complexity, the chemical manufacturing processes require constant quality control and supervision. Because of their structure and usual production volume, the issues undetected in the early stages can escalate very quickly, generating huge costs and compromising the security of the establishment and the clients. Here’s where AI technologies come to the scene. While traditional algorithms may be efficient in some aspects of manufacturing, they do not learn — contrary to machine learning models. By identifying patterns, predicting future events, and suggesting the most efficient solutions based on the available data, ML provides a range of possibilities to the manufacturers that would be out of reach not that long ago.
AI can solve the most common issues encountered by chemical manufacturers, including:
- downtimes on the production line
- leakages and contamination
- unstable and compromised quality
- low or fluctuating yields
- excessive waste production
- inefficient resources use
- lengthy discovery process
- energy use optimization
Applications of Artificial Intelligence and Machine Learning in a Chemical Plant
Every modern chemical plant operates based on a previously written control program. In recent years, chemical engineers have been incorporating more and more AI and ML into them, noticing the broad spectrum of its benefits. Let’s now break down each aspect the AI has an impact on.
Improving Scientific Research Efforts with Advanced Analytics
Machine learning cannot replace scientists, but it can take over some of their most repetitive and error-prone tasks to speed up the research and increase its effectiveness, in some cases facilitating scientific breakthroughs. In the chemical manufacturing (pharmaceutical sector in particular), ML has a big room for maneuver. Using deep learning models, chemical manufacturers can research substances on the molecular level to find the most efficient solutions and improve the existing formulas.
When talking about applications of AI in scientific research, we have to mention chemical property prediction. How does it work? In a nutshell, the components of the molecules are featured, quantified, and fed to the neural network. The model is trained to recognize particular properties based on the dataset containing the already applied molecules that have them. This way, the manufacturers can detect the compounds that carry desirable features but are less expensive or easier to break down or process. AI and machine learning enable advanced modeling of various parameters of a newly developed substance and help design new production lines (small-scale and large-scale).
That’s only one of the opportunities the deep learning techniques offer. Generative modeling (GANs, RL, AE) seems the most powerful with its ability to identify new molecular structures that fulfill the property requirements like binding affinity, solubility, and synthesizability. Considering the length and costs of the drug discovery and chemical compound design process, the application of these models can be a real game-changer for the chemical industry.
Optimizing Operational Efficiency
Artificial intelligence can help chemical factories optimize operations. Applying AI can help minimize the input required to run a business operation (cut costs of energy, operations, production) while maximizing the output (meaning such variables as business growth, customer satisfaction, revenue, and so on). In this context, analytics is also a secret weapon of manufacturers. Taking advantage of the available data, they can better understand the ongoing processes and predict future scenarios. While for the first purpose, the explanatory models work just fine, for the second, they’ll need to reach out for predictive analytics.
The forecasting model gets fed with the existing data and creates predictions based on detected patterns. Predictive analytics is a powerful tool that allows manufacturing establishments to prevent issues from occurring and evaluate business decisions. That translates into increased financial safety — and increased safety in general.
Of course, the model is not a clairvoyant — it needs data to determine the probability of the events and make accurate calculations. Advanced predictive analysis usually uses deep learning algorithms, so the dataset needs to be quite extensive and of relatively high quality.
Advanced predictive analytics does more than just forecasting — it can suggest how to respond to the predicted events in the safest and most cost-efficient manner. Read our in-depth analysis of this technology to understand better how it can influence your business.
Increasing Yields and Reducing Excessive Waste
Sustainable manufacturing practices are a key goal for most chemical manufacturers, with governments and consumers demanding that companies reduce their environmental impact. AI can help by monitoring various production processes in real-time to spot where changes need to be made. This technology can also help streamline operations to produce more products while using less energy and materials.
Throwing away a kilo of material that could have been used in the production process is a waste of resources. It also hampers sustainability goals and can increase the cost of a product.
Every chemical manufacturing company deals with some level of waste, whether it’s a result of overproduction, misjudgment during recipe formulation, or contamination. The good news is that artificial intelligence can help reduce or even eliminate this type of waste.
There are two ways AI can be leveraged to achieve this goal: data-driven decision-making and machine learning-based predictions. In both cases, the focus is on understanding past behaviors to improve future actions. Data-driven decision-making relies on analyzing past data to understand and identify the underlying causes of waste and recommend corrective actions.
By understanding what has led to excessive waste in the past, AI can provide recommendations for avoiding these situations in the future. In many cases, a slight change in process or a tweak to a recipe can result in a significant waste reduction.
Manufacturers who adopt can also use a machine learning-based approach to reduce waste in their operations. In this case, a predictive model is built using a data set that includes a variety of factors such as temperature, level of stirring, the timing for switching between processes, and more. The model is then used to predict the amount of material wasted when a particular recipe is followed.
Increased Quality Assurance
Quality assurance aims to prevent defects in manufactured products, and AI-based tools are perfect for that. In the chemical industry, it’s essential to act immediately — once the undesirable substance gets to the production line, it may take minutes or even seconds to contaminate the entire batch. AI can identify such events at the very early stage and trigger actions that prevent them from progressing. In addition, it can learn with this event, using it to identify similar issues faster in the future or even prevent them from happening.
To maximize the efficiency of quality control, manufacturing companies may use computer vision — a rapidly developing AI-based technology with great potential in the production sector. In its case, the cameras fuelled with deep learning algorithms carry out visual inspections, verifying whether the product or its components fulfill all the requirements. The pixels in the image get scanned and evaluated by the algorithm to separate the good elements from the defective ones. Even though computer vision is mainly used in other production sectors, it can serve chemical manufacturers for material recognition or classification based on physical properties.
Minimizing Downtime Through Predictive Maintenance
Downtimes are costly, and in the worst cases, they can sabotage a chemical company’s financial safety. Once the equipment failure occurs, the production slows down or stops, so naturally, it generates loss — but that’s just the tip of the iceberg. Restarting the production process may cause additional costs for the chemical plant since it takes some time to recreate the pre-existing conditions. As a result, the quality of the product may remain lower for a few days.
The algorithms for predictive maintenance can monitor all the machinery elements in real-time and detect any defects or upcoming failures that could lead to downtimes. It can be done both ways — through regression or classification. The second approach makes it possible to determine when the subsequent failure may happen instead of whether it will happen — thus, it’s usually preferential for the manufacturers. However, it comes with a much higher demand for data.
Providing early warnings, these algorithms can prevent downtimes while maximizing efficiency and extending the useful life of the equipment. While regular maintenance is good prevention, it’s not very efficient in terms of resource management. With predictive maintenance, you can bring it to the next level, maximizing the life of the machinery without risking downtimes.
To optimize the chemical manufacturing processes, it’s also worth including the AI’s predictions in the planning. What will be the demand for a particular substance or drug in a specific year? What quantities of substrates do we need to stock up on? Which ingredients should be replaced and with which to reduce the chemical production costs without compromising quality? Machine learning algorithms can provide the manufacturers with an approximate answer based on the available data. That facilitates data management for scheduling and maintaining maximum cost-efficiency and long-term planning. It allows each chemical company to prepare for increased and decreased demand periods.
What Are the Benefits of Using AI for Chemical Companies?
After looking through all the applications listed above, you’ll probably agree with us that the potential of AI for chemical manufacturing is impressive. Aside from optimizing the manufacturing processes, reducing downtimes and waste, and increasing yield, the chemical industry can use it for research purposes to develop new, better chemical combinations that are cheaper, safer, and friendlier for the environment.
At the same time, AI can also become a tool to meet the dynamically changing regulations regarding environmental protection and CO2 emissions. The algorithms can identify areas for improvement in terms of electricity and resources consumption to bring the chemical company closer to climate neutrality and reduce its expenses at the same time. Since the rights to CO2 emissions are becoming more and more expensive, it’s another way to ensure the chemical plant’s financial safety. Plus, a reduced carbon footprint is an asset in business negotiations since the contractors along the chemical supply chain also aim at keeping their emissions low.
The Future of Chemicals Production With AI Technology
It’s hard to say what the future will look like — we could ask the algorithms! Without a doubt, some current tendencies already point out the direction of AI’s development in this sector. As we’ve already mentioned, generative modeling may turn out a game-changer for chemists searching for new molecules with healing properties or alternatives for substances in wide use that have a negative impact on the environment. Machine learning tools can provide scientists with efficient ways to screen numerous chemical combinations or reactions and their outcomes.
With such screening/generative abilities, the ML algorithms can also bring us closer to a greener future, making it easier for the chemical companies to produce substances that have matching properties to plastic or petroleum products but break down easier and without waste or pollution. While already using AI for manufacturing process optimization, these companies can take advantage of unused funds and resources to research environmentally-friendly alternatives.
If you would like to discover the potential of machine learning for the chemical manufacturing industry in practice and see in which areas your company could benefit from it, reach out to us — we’ll search for them together with our Data Scientists and AI Engineers!
Originally published at https://nexocode.com on February 20, 2022.