ENG. ANDREW OMWENGA
Chemical & Process Simulation Engineer • Thermodynamic Specialist
Steam Boiler Energy Predictive Maintenance & Efficiency Model
Section 1.0 — Simulation & Facility Metadata
Section 2.0 — Executive Summary
Industrial predictive maintenance and thermal efficiency model developed for Proton Bakers, Zimbabwe, analyzing boiler stack losses, excess O2, and tube scaling.
Section 3.0 — Problem Statement & Operating Bottlenecks
Unscheduled boiler shutdowns caused by thermal stress and water-side tube scaling inflated emergency maintenance costs and disrupted bakery production schedules.
Section 4.0 — Objectives & Rigorous Simulation Methodology
Python Win32 COM API connecting Aspen HYSYS boiler simulation with plant sensor data streams. Calculated direct and indirect boiler thermal efficiency according to ASME PTC 4.
Section 5.0 — Simulation Results & Thermodynamic Findings
Maintaining excess flue gas O2 at 3.2% (vs 5.8% previously) reduced stack heat losses by 4.2%, while automated blowdown control prevented silica scale accumulation.
Section 6.0 — Core Engineering Takeaways
Section 7.0 — Model Assumptions & Future Recommendations
Sensor signal noise required moving average smoothing filters before feeding thermodynamic calculations.
Machine learning neural network integration for real-time burner combustion tuning.
Section 8.0 — Consultant Conclusion & Verification Sign-off
Python-automated Aspen HYSYS models provide continuous predictive diagnostic power for industrial steam assets.
