Solution

I used baseline simulation and adsorbent performance data to propose a compact PSA cycle and identify the key design parameters.

The work established technical feasibility and the core design basis for a follow-on development program.

Solution

I built an AI-driven simulator that predicts batch performance from formulation and process data.

The simulator reduced the need for repeated mixing cycles, improving both turnaround time and cost efficiency.

Solution

I led the design of a unified solution that combined sensor-driven prediction, analytics, simulation, optimization, and a plant-focused assistant.

Integrated AI solution development spanning real-time prediction, analytics, simulation, optimization, anomaly detection, and LLM capabilities.

Solution

Using Aspen Plus data, I built an AI surrogate model and designed an optimizer that recommends energy-efficient setpoints.

The result was a faster decision-support layer for a complex wet-scrubbing process, much closer to real-time operation.

Solution

I developed a governing-equation-based simulation from experimental data and coupled it with a DNN surrogate and genetic-algorithm optimization.

The resulting framework made it easier to re-identify optimal conditions even when the equipment design changes.

Solution

I refined the time-series data pipeline, built stronger quality-prediction models, and packaged them into an integrated system with operational interfaces.

The deliverable moved beyond a standalone model into an integrated solution suitable for day-to-day operations.