Case Studies / Chemical Company

Chemical Manufacturing Company - Sulfuric Acid Regeneration Unit

ChemicalsiDSSAspenTech Fidelis

Challenge

A chemical manufacturing company operates a sulfuric acid regeneration unit where catalyst degradation, feed variability, and heat exchanger fouling create uncertain production capacity over a 5-year planning horizon. Financial models assumed constant throughput; operational reality was far more complex.

Outcome

Executive team gained ability to evaluate strategic decisions—turnaround schedules, catalyst replacement strategies, contract negotiations—with full visibility to technical risk and financial consequence. The model is now maintained internally and updated quarterly.

Technical Complexity

The sulfuric acid regeneration unit faced three interlinked uncertainties:

  • Catalyst decay kinetics varied with feed sulfur content and operating temperature
  • Heat exchanger fouling rates depended on feed composition and cleaning schedules
  • Production capacity was constrained by both catalyst activity and heat transfer efficiency
  • Sulfuric acid pricing and contract delivery obligations added financial variability

Solution: Integrated Decision Support System

Catalyst Degradation Model

Modeled decay kinetics as function of feed sulfur content, temperature, and operating hours using Fidelis probabilistic framework

Heat Exchanger Fouling Integration

Linked fouling rates to capacity forecasts and maintenance cost scenarios

Economic Integration

Connected production variability to sulfuric acid pricing and contract delivery obligations

Turnaround Optimization

Optimized turnaround timing against NPV of lost production vs. maintenance cost

Business Impact

The iDSS enabled company leadership to make strategic decisions with full understanding of technical-financial trade-offs. They could answer questions like "What happens to NPV if we defer turnaround by 6 months?" with probabilistic confidence intervals. The model revealed that feed sulfur variability—a procurement decision—had quantifiable EBITDA impact, enabling cross-functional optimization between procurement and operations.