OVERVIEW
A chemical industry player partnered with Mipu to introduce artificial intelligence into its production processes. The goal was to move from reactive process monitoring to predictive control, improving reliability and operational performance through data-driven insights.
INITIAL SITUATION
The client operates within the chemicals industry, characterized by complex production processes, strict quality constraints, and high sensitivity to deviations in operating conditions. Like many industrial organizations, the company had access to large volumes of historical and real-time process data coming from different systems and sources.
Despite this availability, data was fragmented across tools and platforms, limiting its effective use. Process monitoring was mainly based on static thresholds and operator experience, making it difficult to detect early signs of abnormal behavior. Deviations from optimal operating conditions were often identified only after quality or performance issues had already occurred.
Maintenance and operations teams lacked a shared, structured view of process behavior. Energy consumption and inefficiencies were monitored, but without advanced analytics capable of highlighting waste or subtle anomalies. As a result, the organization faced challenges in ensuring consistent process performance, improving reliability, and supporting continuous optimization.
These limitations pushed the client to look for a solution able to integrate existing data, model optimal process behavior, and support operators with actionable, predictive insights rather than reactive alarms.
THE CHALLENGE
The main objective of the project was to represent and monitor the optimal behavior of a chemical process using artificial intelligence, leveraging historical data already available.
The solution needed to work with existing systems and data sources, without requiring major changes to the plant infrastructure. Another key requirement was reliability: the model had to accurately distinguish between normal process variability and real anomalies.
From an organizational perspective, the challenge was also to provide clear and usable outputs for different stakeholders, including operations and maintenance teams, ensuring that insights could be translated into concrete optimization actions.
THE SOLUTION
Mipu designed and implemented a predictive control solution based on its industrial AI roadmap and the Rebecca platform.
The project started with an assessment of the AS-IS situation, focusing on understanding the process, available data, and operational constraints. This phase allowed the team to identify the most relevant variables and define the scope of the predictive model.
A structured data analysis phase followed, including data cleaning and manipulation to ensure quality and consistency. Historical process data was then used to build a Golden Batch model, capable of representing the optimal behavior of the process with high precision.
The artificial intelligence model was trained to recognize normal operating conditions and validated using both compliant and anomalous datasets. Once validated, the model was deployed on Rebecca AI, Mipu’s modular platform for data integration, artificial intelligence, and performance control.
Rebecca connected different data sources and enabled real-time comparison between expected and actual process behavior. The solution provided dedicated dashboards for different users, along with automated alerts and reports highlighting deviations and anomalies.
Throughout the project, Mipu supported governance and change management activities, ensuring alignment between technology, processes, and people.
RESULTS
The implementation of predictive artificial intelligence enabled the client to gain a deeper understanding of its chemical process behavior.
By comparing real-time performance with the Golden Batch model, the organization was able to detect anomalies earlier and more accurately than with traditional threshold-based monitoring. This improved process reliability and supported faster corrective actions by operations and maintenance teams.
The integration of multiple data sources into a single platform simplified access to information and improved collaboration between functions. Optimized dashboards and automated alerts reduced manual analysis time and supported more consistent decision-making.
In addition, the ability to monitor energy consumption and identify waste contributed to improved operational efficiency and sustainability awareness. While specific numerical KPIs were not disclosed, the project demonstrated how artificial intelligence in the chemicals industry can support reliability, productivity optimization, and continuous improvement.
