ASME/TEMA-standard shell-and-tube units can hide baffle dead zones and pressure-drop blind spots that accelerate scaling. We optimize baffle and pass layout, model erosion and build predictive diagnostics to reduce prototyping cost and speed engineering sign-off.
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Internal flow imbalances and particle recirculation can drive product carryover and excess pressure drop. Through multiphase flow profiling, discrete particle tracking and targeted geometric refinement, we improve separation efficiency and protect downstream assets.
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Poor air-fuel mixing and localized hot spots can cause slagging, emissions and unburned carbon loss. We apply multiphase combustion modelling and Lagrangian particle tracking to optimize air-to-fuel ratios and improve boiler efficiency.
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Burner alignment and flame shape dictate heat transfer to the material bed. Modelling turbulent gas combustion and granular bed flow coupling helps optimize burner air-fuel injection, extend refractory life and reduce fuel consumption and emissions.
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Traditional scale-up often relies on empirical rules and costly prototyping. We simulate impeller selection, mixing kinetics and micro- and macro-dispersion to improve batch uniformity while minimizing dead zones and shear stress.
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Sealed inline mixers rely on internal geometry and velocity to blend streams, leaving operators without direct flow visibility. We tune feeder configuration and element geometry to reduce pumping energy and downstream fouling.
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Air handling unit design often involves airflow maldistribution and unpredictable component interaction. Turbomachinery modelling and velocity and pressure mapping enable virtual iteration, improving thermal efficiency and reducing energy use.
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Laboratories often manage sample information across multiple systems, spreadsheets and manual processes. As sample volumes grow, this can result in limited visibility, slower decision-making and increased administrative effort.
We developed a centralized dashboard for tracking sample submissions, businesses, collection dates and sample types. Visual analytics and recent-submission data provide an immediate view of sample activity and support report generation after completion.
Large laboratory and production datasets can contain numerical measurements, categorical variables, missing values, outliers and several related features. Opening a spreadsheet alone does not provide enough context to understand data quality or identify meaningful patterns.
The solution creates a structured analytical workflow beginning with data sourcing and progressing through preprocessing, exploratory data analysis, control charts and machine-learning models.
Semiconductor manufacturing requires extremely high production quality. Even microscopic defects can affect wafer yield, device reliability and manufacturing costs.
We developed a centralized defect analytics platform that gives engineering and production teams a real-time overview of wafer inspection performance. The dashboard combines computer-vision inspection results, defect classification, performance metrics and spatial heatmap visualization in one interface.
Spreadsheet-based heat exchanger calculations can be difficult to standardize, maintain and review. We developed a reusable engineering interface that organizes sizing and rating calculations into a consistent workflow.
The tool helps design teams perform calculations faster, reduce manual errors and create a repeatable basis for engineering decisions.
Rotating equipment degradation can develop gradually through changes in vibration, operating conditions and process signals. Without centralized monitoring, early warning indicators may be difficult to identify.
We developed an early-warning dashboard that combines equipment signals and process information to highlight degradation trends and support condition-based maintenance decisions.
