Optimizing Diammonium Phosphate Sizing in Micro-Nutrients Production

Executive Summary

Process Point successfully implemented an advanced data-driven solution for a micro-nutrients production facility struggling with diammonium phosphate (DAP) sizing issues. By developing a predictive model and simulator, we significantly improved product size consistency and operational efficiency.

Client Profile

  • Industry: Micro-nutrients Production
  • Product: Diammonium Phosphate (DAP)
  • Key Challenge: Meeting targeted average size and uniformity specifications

Business Challenges

  • Persistent difficulty in meeting product size specifications
  • Multiple unsuccessful attempts to improve size characteristics
  • Temporary improvements followed by size drops
  • Unexplained oscillations in product size

Project Goals

  • Develop a model to predict SGN (Size Guide Number)
  • Create a simulator to test actionable changes for size improvement
  • Investigate and understand the root causes of poor size and unexplained oscillations

Process Point's Innovative Solution

Process Point delivers bespoke artificial intelligence and machine learning solutions to revolutionize operations in complex process industries. We specialize in tailored AI applications that drive efficiency, safety, and innovation in Mining, Petrochemicals, and related sectors.

  • Extensive Data Collection: Gathered data for 50 attributes over the past 2 years, aggregated to 10-minute intervals
  • Advanced Data Cleaning: Performed data cleaning and feature generation in sync with chemical principles, based on Subject Matter Expert (SME) feedback
  • Predictive Modeling: Developed two LightGBM models with a voted aggregate approachProcess variable model based on PI and LIMS tagsHistorical trend model focused on temporal aspects of the time series
  • Process variable model based on PI and LIMS tags
  • Historical trend model focused on temporal aspects of the time series
  • Interactive Simulator: Created a simulator with a GUI for operations and operators to estimate the impact of changes on size

Key Features of Our AI/ML Solutions

Comprehensive Data Analysis
In-depth analysis of two years of production data to uncover valuable insights and trends.
Integrated Chemical Principles
Combining chemical principles and SME knowledge for effective data preprocessing.
Dual Modeling Approach

Captures both process variables and historical trends for accurate predictions.
Real-Time Decision Support

User-friendly simulator designed for real-time decision-making and support.

Results and Business Impact

  • Improved Predictive Accuracy: Achieved a Root Mean Square Error (RMSE) of 11.69 and Mean Absolute Error (MAE) of 7.44 in size prediction
  • Enhanced Process Understanding: Identified key factors influencing product size and uniformity
  • Operational Efficiency: Provided operators with a tool to estimate the impact of process changes on product size
  • Data-Driven Decision Making: Empowered operators with real-time insights for proactive size management
  • Quality Improvement: Enabled more consistent production of DAP meeting size specifications

Conclusion

By addressing the complex challenge of DAP sizing with a tailored, data-driven approach, Process Point demonstrated its expertise in applying advanced analytics to solve critical production problems. This success story exemplifies our commitment to delivering high-impact solutions that drive product quality improvements and operational excellence for our clients in the specialty chemicals and micro-nutrients industry.
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