Project Overview
In an industry where every gallon processed and every hour of uptime counts, managing a chemical regeneration plant isn't just about keeping equipment running — it's about seeing the whole picture clearly. For one of North America's leading specialty chemical recyclers, that clarity has now been achieved through a first-of-its-kind digital asset: a living, breathing model of their core regeneration unit that doesn't just simulate performance — it anticipates it.
Built on advanced simulation technology and grounded in real operational history, this Integrated Decision Support System (iDSS) transforms uncertainty into insight. It reveals not only how much the plant can produce, but why it sometimes falls short — pinpointing hidden bottlenecks, quantifying risk, and exposing untapped capacity. With utilization well below potential, the biggest constraint wasn't machinery — it was visibility.
Now, armed with probabilistic forecasts, dynamic failure logic, and a granular view of system interdependencies, leadership can move beyond reactive fixes and start shaping outcomes. Whether prioritizing maintenance, evaluating upgrades, or aligning production with market demand, decisions are no longer guesses. They're calculations — rooted in data, refined by experience, and optimized for value.
This isn't just modeling. It's a new way to manage industrial assets — where complexity is no longer a burden, but a map to smarter action.
Key Achievements
Revealed True Availability Profile
The model captures how the plant operates across different capacity states — unrestricted, partially restricted, and fully unavailable — providing a richer understanding than traditional single-point availability metrics.
Confirmed Positive Reliability Trend
Analysis indicates improving asset performance over time, reflecting effective maintenance practices and growing system resilience.
Identified Primary Bottlenecks
Two subsystems — heat recovery and drying — were found to be the largest contributors to production loss, offering clear focus areas for targeted improvement initiatives.
Exposed Significant Underutilization
Despite strong technical capability, the plant operates well below its potential, revealing an opportunity linked more to commercial alignment than mechanical limitations.
Enabled Long-term Forecasting with Confidence Ranges
Instead of fixed projections, the model delivers distributions of possible production outcomes, allowing planners to assess risk and set targets accordingly.
Mapped Throughput Potential Under Variable Demand
By incorporating realistic feed rate fluctuations using a Markov-based approach, the model reflects actual operating conditions and supports better planning for logistics and storage.
Introduced Culpability Allocation
Lost production can now be traced back to specific subsystems and failure modes, enabling accountability and guiding capital and engineering efforts where they will have the greatest impact.
Laid Foundation for Cost-informed Decisions
Maintenance cost drivers are mapped across the asset, creating a pathway for optimizing spending and improving return on reliability investments.
Strategic Impact
The iDSS transforms decision-making from reactive to proactive, equipping engineering, operations, and leadership teams with a shared, dynamic understanding of asset performance. It supports scenario testing for:
Beyond operational insights, the project uncovered critical gaps in data quality — particularly in work order completeness and the reporting of slowdown events — prompting actionable recommendations for improved data governance and integration with process historian systems.
Designed for continuous evolution, this model is not a one-time deliverable but a foundational platform. Its structure allows for iterative refinement as new data, rules, and constraints emerge, and its framework is replicable across similar assets, positioning the organization to scale digital decision support enterprise-wide.
This iDSS sets a new standard for how industrial recyclers can leverage advanced simulation to turn uncertainty into clarity, complexity into action, and latent capacity into tangible value — all while maintaining full confidentiality of operational data.