Advanced Predictive Analytics in Chemical Manufacturing: Improving Your Chemical Operations

Advanced Predictive Analytics in Chemical Manufacturing: Improving Your Chemical Operations

Dorota Owczarek - December 17, 2021

The predictive power of predictive analytics is a potent weapon in the arsenal of the chemical industry. By using predictive analytics, chemical manufacturers can uncover previously unknown bottlenecks, unprofitable production lines, and other data-driven insights that improve operations. The forecasting power of predictive analytics can benefit the chemical manufacturing process across various stages of production, including quality control, process improvement, failure prediction/prevention, safety monitoring, etc. This article will discuss how predictive analytics can be used to improve chemical production and what you can do to take advantage of it!

From Descriptive to Prescriptive. How Advanced Analytics Techniques Differ?

Descriptive analytics, the primary form of analytics available, uses explanatory models such as statistical regression or clustering algorithms to describe what has happened. These are usually traditional BI (business intelligence) reports on process performance; it’s used when you want to learn more about your systems’ current state of health. These methods can be used to learn about your data (describe it), find hidden insights into processes, and discover new opportunities by looking at past events from different angles.

Predictive analytics is a forecasting modeling process that analyzes current and historical facts to make future predictions based on known patterns. Predictive analytics answers questions like, “What is likely to happen?” and “How can we change what happens next time around? This type of analysis allows users to answer “what if” scenarios. Chemical manufacturers can use predictive models for predictive analytics to uncover problems before they even occur by learning how changing variables affect processes and operational efficiency over time.

However, predictive techniques are still evolving. Chemical producers are now looking forward to using the most advanced data analytics technique - prescriptive analysis, which takes things one step further than just forecasting what might happen next and suggests specific courses of action to respond to certain situations. The prescriptive model suggests how to act for an organization’s goals and objectives to be reached.

Predictive Analytics for Chemical Manufacturing - How Artificial Intelligence Can Optimize Production Performance?

Often, predictive analytics is used in conjunction with artificial intelligence (AI). AI can be used to improve models and make predictions more accurate. In manufacturing processes, predictive analytics can help identify hidden patterns and relationships in data that would not be possible to find through manual analysis.

Predictive analytics in combination with AI allows chemical manufacturers to move from descriptive models that only describe what has happened in the past to prescriptive models that suggest specific courses of action for responding to certain situations. This increased level of understanding can help companies make a significant impact and optimize plant operations.

Chemical companies have already begun using analytics in conjunction with AI for various applications, including the specific use cases described in the following paragraphs.

Developing Novel Chemical Combinations

One of the most promising areas for predictive analytics in chemical manufacturing is the development of new chemical combinations. The process of creating a unique chemical combination is very complex, and it can be challenging to know whether a particular mixture will be successful or not.

Predictive analytics can help identify which chemicals are likely to react well with each other and which ones are not. Using advanced analytics will help chemical manufacturers forecast the solubility of complicated mixtures, plastics, rubbers, and dyes and the aging processes of catalysts, providing significant industrial benefits. This information can then be used to develop novel combinations with a higher chance of success.

Optimizing Complex Production Networks

Predictive analysis is valuable for optimizing complex production processes. Chemical companies are working to identify hidden patterns and relationships in data that would not be possible with traditional descriptive models. These patterns can help highlight the factors driving changes in network dynamics, allowing chemical producers to increase their responsiveness when dealing with disruptions or unexpected issues.

Optimizing Energy Consumption

Predictive analytics can help chemical companies reduce their energy consumption. By analyzing data from past chemical plants’ manufacturing operations, predictive models can be created to identify patterns in energy use. This information can then be used to optimize future production processes and reduce the energy needed for manufacturing operations. Machine learning forecasting algorithms have also been used to improve the thermal efficiency of chemical plants. By predicting how different variables (such as weather conditions or process changes) will affect energy usage, chemical producers make adjustments that result in significant savings.

Waste Reduction

Processes in the chemical industry often produce waste products. It may be possible to recycle these wastes and use them on other lines in some cases. However, in many cases, the waste must be disposed of properly. Advanced analytics can be used to reduce chemical waste in production processes. Predictive analysis can help companies avoid wasting raw materials or products by testing different processing options before committing to a specific method by identifying patterns and relationships that prevent predictive models from making accurate predictions.

Predictive data analytics has also been applied to track batches of products as they go through the entire manufacturing process. This allows for real-time adjustments when issues arise during the course of the operation so that corrective actions taken will minimize disruption while maximizing efficiency.

Predict Deteriorations in Quality and Act On It in Real-Time

Quality deterioration can significantly impact the overall yield of a chemical batch. It may be necessary to discard an entire batch due to quality issues in some cases. This can result in lost time and money.

To prevent this from happening, predictive analytics can be used to monitor product quality as it is being manufactured. By identifying early signs of quality deterioration, process engineers can take corrective action or preventative measures before the issue becomes severe enough to cause problems with the final product.

This ability to detect and correct quality issues in real-time can save companies significant amounts of money and improve their bottom line. Additionally, it minimizes the part of defective products to be discarded, resulting in less waste and environmental damage.

Production process based on predictive modeling for feedback control and process monitoring
Production process based on predictive modeling for feedback control and process monitoring

Related case study: APIs Production Process Predictive Monitoring

To improve the current repeatable batch production processes, a producer of active pharmaceutical ingredients approached us to implement AI models and utilize predictive modeling.

Our challenge? Building a model that analyzes real-time data streams from the production process and identifies potential outliers that may lead to deterioration of quality, based on historical data. The benefits are improved effectiveness, predictability, and efficiency of manufacturing operations and yields.

Predictive Maintenance

Predictive maintenance is another area where predictive analytics can help chemical manufacturers. The idea of predictive maintenance stems from the observation that many breakdowns and malfunctions occur due to buildup or deterioration over time, which implies that these failures could be predicted if they were monitored closely enough to detect early warning signs.

By analyzing large amounts of data, predictive models can be created that highlight the factors influencing asset failures and equipment malfunctions. This information allows companies to prioritize their inspection schedule for critical assets based on their likelihood of failure or malfunctioning in the near future, which can significantly cut maintenance costs.

Predictive analytics could reveal abnormal deviations from expected behavior over time due to one particular machine out of hundreds (or thousands) across a large geographic area; these insights might otherwise remain obscured by more general statistics about the entire plant’s performance. This information allows operators at multiple locations to take preventative measures before an issue becomes critical.

Predictive analysis also provides additional advantages over traditional maintenance programs because it offers real-time sources of insight rather than retrospective reports generated after an event occurs. It helps identify the “what,” “when,” and “why” of equipment failures so that targeted interventions and plant maintenance actions can be applied.

Planning Production Based on Demand

Predictive analytics information can also help a company plan production based on expected demand.

Chemical producers can determine how much material they need to produce to meet customer orders by analyzing predictive models. This will reduce the amount of excess inventory held at any given time and improve the overall efficiency of supply chains by producing only what is needed instead of larger quantities that may not be sold or consumed before their expiration date.

Predictive analytics allows companies to take advantage of previously unavailable levels of insight into product usage to know precisely what predictive analytics tool can help chemical companies better assess their production needs by allowing them to schedule batches of products that are more in line with demand. This allows for higher utilization rates and reduced idle time, lowering overhead costs associated with wrong production scheduling.

The Benefits of Introducing Machine Learning to Chemical Production

With the fourth industrial revolution, the era of artificial intelligence and machine learning is upon us. And while some may be hesitant to adopt these new technologies, chemical producers would do well to consider their potential benefits.

Predictive analytics can also be used to monitor product quality in real-time. Predictive models can alert operators by identifying early signs of quality issues and either stop batch production or alter operating modes for real-time quality control for batch manufacturing processes. By taking advantage of predictive analytics and machine learning tools, chemical plants can gain new insights into their production operations and optimize energy consumption and materials usage.

Moreover, machine learning can help improve production by predictive analytics tools that can help chemical enterprises better assess their production needs by scheduling batches of products that are more in line with demand. This allows for higher utilization rates and reduced idle time, lowering overhead costs associated with wrong production scheduling. Many manufacturers are already using advanced analytics and machine learning algorithms to predict product demand and optimize production schedules accordingly.

Predictive analytics can impact maintenance operations, a particularly exciting development for chemical companies benefiting from lower maintenance costs, zero-accidents culture, and a continuous production process without breaking due to equipment failure.

Benefits of AI in chemical manufacturing

How to Apply Predictive Analytics in the Chemical Industry?

However, predictive analytics is not a silver bullet and should be used with other maintenance practices. As predictive models become more accurate, thanks to deep learning algorithms, chemical producers will need to continually update their data sets and retrain the models so that predictions remain relevant. Additionally, operators should not rely solely on predictive analytics; instead, they should use it as one tool in their arsenal of maintenance practices.

Nevertheless, the benefits of predictive analytics are transparent, and chemical companies would do well to consider implementing these technologies into their production operations. With the right tools in place, predictive analytics can help improve efficiency, reduce waste, and ensure product quality across all stages of the manufacturing process.

Applying AI and Data Science in Chemicals

To successfully adopt advanced analytics and machine learning, companies should understand novel technologies. They should also have a clear view of the tools and practices already applied within a given company. A good practice is always to look at your entire portfolio together as one extensive system – this way, you will see how different assets work in conjunction with others machines. Moreover, using visualization software can make it easier for chemical factories to gather insights into their operations and quickly identify areas where predictive analytics could bring added value across all aspects of manufacturing.

To use predictive maintenance successfully, you need a strong foundation of historical data. Without enough data points from the past, predictive models will not detect any trends or patterns that can help predict future events with accuracy and reliability.

Predictive Analytics Step by Step

  • Define project - What is it you are trying to achieve? Set clear project goals.
  • Collect data - What information will be needed for predictive analysis, and where can the company get this data? Collect historical *data, possibly in a normalized and structured manner. How to conduct data collection and what data sources to use(sensors, IoT, existing software, real-time)?
  • Clean and prepare data - Apply initial data audit. Remove any irrelevant data, merge similar data sets, conduct data preprocessing.
  • Build and test model - Select a machine learning algorithm for predictive analysis and use it. Build a predictive model based on the data set. Test the model iteratively as you build to measure it against established benchmarks. Identify constraints, measure predictive accuracy and other benchmarks.
  • Deploy model - Deploy predictive model and machine learning tools to the production environment. Utilize the results to make decisions, generate reports and monitor outcomes.
  • Monitor and refine the model - Monitor the predictive model in production. Track the performance of your advanced analytics over time, and adjust them when necessary to increase predictive power or reduce errors.
Predictive analytics process

The Future of the Chemical Manufacturing Industry. Will AI Change How Chemical Companies Operate?

The use of chemical software based on artificial intelligence and predictive analytics market and is poised to grow by $248.94 million during 2021-2025.

This increase can be attributed to an increased demand for novel technologies and predictive insights due to rising concerns about compliance and environmental impact and the advancements of predictive modeling techniques that allow the chemical industry to grow sustainably.

It’s estimated that these factors will help drive growth throughout this decade and beyond, providing a significant return on investment for every chemical manufacturer that takes advantage of artificial intelligence applications and use them in production systems.

With the ever-growing amount of data generated by industrial processes, chemical manufacturers need to find ways to use big data analytics if they want to stay competitive in today’s market. The benefits of predictive analytics are clear, and more and more chemical companies are beginning to adopt this technology. By using predictive models, companies from the chemical sector can detect problems before they arise and act on them.

Data science, IoT and sensors, and automation

Vision, speed, and agility are essential in today’s chemical manufacturing industry. Predictive analytics can provide the insights needed to achieve these goals by helping identify opportunities for improvement and solving complex problems that have remained hidden in the past. Chemical companies can optimize their production processes, improve product quality, and reduce waste with predictive analytics. By doing so, they can remain competitive in an ever-changing market while protecting the environment at the same time.

Are you looking for custom predictive analytics software and ways on how to adopt AI? Reach out to our team and see how your idea could be implemented.

About the author

Dorota Owczarek

Dorota Owczarek

AI Product Lead & Design Thinking Facilitator

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With over ten years of professional experience in designing and developing software, Dorota is quick to recognize the best ways to serve users and stakeholders by shaping strategies and ensuring their execution by working closely with engineering and design teams. She acts as a Product Leader, covering the ongoing AI agile development processes and operationalizing AI throughout the business.

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This article is a part of

AI in Chemicals
5 articles

AI in Chemicals

Artificial Intelligence is a powerful tool that can help chemical companies work smarter and faster. The technology allows for more productive processes by automating tasks, providing insights into how chemicals react or improving manufacturing environments.

Follow our article series to find out the applications of AI in Chemicals and how this tech benefits companies from the whole sector that operate in petrochemicals, agrochemicals, commodity, and specialty chemicals.

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