• Written by Sonali Subhadarsini, Product Manager

The dashboard for the polyethylene plant's production line, once hailed as a breakthrough in transparency, now gathers dust in the corner of the control room, its screens dimly displaying a tangle of numbers and charts that nobody has the time to decipher, a testament to the challenges of automating decision-making in the chemicals industry, where yield drift, feedstock variability, and plant-to-plant recipe differences make every production run a unique balancing act, and the tight yield and safety envelopes leave little room for error, making it essential to focus on automating the steps around a decision rather than the decision itself, a strategy that has been successfully implemented using AI process automation in chemicals.

What makes chemicals harder

The chemicals industry is notorious for its complex processes, where small changes in feedstock quality or recipe parameters can have significant effects on yield and product quality, and the continuous nature of many chemical processes means that these effects can be cumulative, making it difficult to identify and correct problems before they impact production, and the variability in feedstock, which can be affected by factors such as weather, transportation, and storage conditions, adds another layer of complexity, as does the fact that different plants, even within the same company, may have different recipes and processes, making it challenging to develop a one-size-fits-all automation solution, and requiring a deep understanding of the specific process and its constraints, which is where AI process automation in chemicals can provide valuable insights and support.

For instance, a shift supervisor at a polyethylene plant may need to adjust the recipe on the fly to account for changes in feedstock quality, which can affect the yield and quality of the final product, and this requires a deep understanding of the process and its constraints, as well as the ability to analyze data from various sources, such as sensors and laboratory tests, to make informed decisions, and AI process automation in chemicals can help by providing real-time data analysis and recommendations, allowing the shift supervisor to make more informed decisions and optimize the production process.

Mapping the current process

Before automating any part of the decision-making process, it's essential to map out the current process in detail, including all the steps involved, the data used, and the handoffs between different teams and systems, this mapping process can be time-consuming, but it's crucial to understanding where automation can add the most value, and where it may not be suitable, and it requires input from all stakeholders, including production staff, maintenance personnel, and quality control specialists, to ensure that the automation solution meets the needs of all users, and is integrated with existing systems and processes, such as the production planning system, which provides the foundation for the automation solution.

By mapping out the current process, organizations can identify areas where automation can help reduce delays, improve quality, and increase efficiency, such as automating data collection and analysis, or streamlining handoffs between teams, and this can help to build a business case for automation, and ensure that the solution is tailored to the specific needs of the organization, and is aligned with its overall strategy and goals, which is critical to the success of the automation project, and requires careful planning and execution, as well as ongoing monitoring and evaluation to ensure that the solution is meeting its intended objectives.

The handoffs that create most of the delay

In many chemical plants, the handoffs between different teams and systems are where the most significant delays occur, for example, when the production team finishes a batch, they may need to wait for the quality control team to complete their tests before they can start the next batch, and this wait time can be significant, especially if the quality control team is busy with other tasks, or if the tests require specialized equipment or expertise, and automating these handoffs can help reduce delays and improve overall efficiency, by providing real-time updates and notifications, and streamlining the flow of information between teams, which can help to reduce errors and improve collaboration, and is a key aspect of AI process automation in chemicals.

For instance, a category manager at a chemical company may need to coordinate with the production team, the quality control team, and the logistics team to ensure that the right materials are delivered to the right place at the right time, and this requires careful planning and coordination, as well as effective communication and collaboration, which can be facilitated by automation solutions, such as automated workflows and notifications, which can help to reduce delays and improve overall efficiency, and is a key benefit of AI process automation in chemicals, which can help to improve the overall performance of the organization, and provide a competitive advantage in the market.

Exception handling as the majority of the real work

In chemical plants, exception handling is often the majority of the real work, as production staff and maintenance personnel need to respond to unexpected events, such as equipment failures or changes in feedstock quality, and these events can be complex and require specialized knowledge and expertise to resolve, and automating exception handling can help reduce downtime and improve overall efficiency, by providing real-time alerts and notifications, and streamlining the flow of information between teams, which can help to reduce errors and improve collaboration, and is a key aspect of AI process automation in chemicals, which can help to improve the overall performance of the organization, and provide a competitive advantage in the market, and is an area where AI can provide significant value, by analyzing data from various sources, and providing recommendations and insights to support decision-making.

For example, a maintenance technician at a chemical plant may need to respond to an unexpected equipment failure, which requires specialized knowledge and expertise to resolve, and AI process automation in chemicals can help by providing real-time data analysis and recommendations, allowing the technician to make more informed decisions and optimize the maintenance process, and this can help to reduce downtime and improve overall efficiency, and is a key benefit of AI process automation in chemicals, which can help to improve the overall performance of the organization, and provide a competitive advantage in the market, and is an area where AI can provide significant value, by analyzing data from various sources, and providing recommendations and insights to support decision-making, and for more information on how to implement AI process automation in chemicals, please contact us.

What this does not do

AI process automation in chemicals is not a replacement for human judgment and expertise, but rather a tool to support and augment decision-making, and it's not a solution for all problems, but rather a targeted approach to specific challenges, such as automating the steps around a decision, or streamlining handoffs between teams, and it requires careful planning and execution, as well as ongoing monitoring and evaluation to ensure that the solution is meeting its intended objectives, and is aligned with the overall strategy and goals of the organization, and is not a one-size-fits-all solution, but rather a tailored approach to the specific needs of the organization, and its implementation requires a deep understanding of the process and its constraints, as well as the ability to analyze data from various sources, and provide recommendations and insights to support decision-making.