Use modeling to resolve the question that controls the next decision.
A process model should do more than reproduce a flowsheet. It should expose the assumptions, interactions, sensitivities, constraints, and data gaps that determine whether a process can meet its technical and commercial objectives.
ONB develops fit-for-purpose analyses ranging from early feasibility calculations to integrated steady-state flowsheets, reactor models, digital twins, and targeted CFD or CPFD studies. Model complexity is matched to the decision and available evidence.
Can the process meet its target?
Evaluate yield, purity, conversion, throughput, energy use, utility demand, and operating constraints.
What drives performance?
Separate critical variables from secondary variables through sensitivity, scenario, and uncertainty analysis.
What evidence is missing?
Identify the parameters and experiments that most reduce uncertainty before design or scale-up.
Where are the bottlenecks?
Locate capacity, separation, utility, heat-integration, hydraulic, and reactor-performance constraints.
Integrated process simulation
Aspen Plus, Aspen HYSYS, and other fit-for-purpose flowsheets; thermodynamics; recycles; utilities; separation systems; and heat integration.
Mass and energy balances
Traceable steady-state balances, design cases, stream tables, utility loads, and reconciliation of process data.
Reactor and kinetics models
Reaction networks, rate expressions, parameter estimation, residence-time effects, and reactor-performance calculations.
Sensitivity and uncertainty
Operating windows, scenarios, parameter ranges, uncertainty propagation, and decision-relevant tradeoffs.
CFD and CPFD studies
Study definition and analysis for flow, heat and mass transfer, multiphase behavior, solids systems, and reactor hydrodynamics.
Model review and validation
Independent audit of assumptions, inputs, convergence, calibration, validation evidence, and suitability for intended use.
A model is only as useful as its assumptions.
ONB documents where inputs come from, which assumptions control the result, what has been validated, and where the model should not be relied upon. The analysis remains reviewable and usable by engineering, management, laboratories, vendors, investors, and future project teams.