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ChemeNova AI Node — Smart Centrifuge Dashboard

AI-Integrated Centrifuge Monitoring & Optimization
ChemeNova LLC × Chemrich Global

Streamlit App


What This Is

A real-time process analytics dashboard that transforms Chemrich centrifuge hardware into intelligent, self-optimizing assets. Built on Physics-Informed Neural Networks (PINNs) and FFT vibration analysis.

Core capabilities:

  • Inflection Point Detection — identifies the exact moment diminishing dewatering returns begin, triggering optimal discharge (replaces fixed timers)
  • FFT Bearing Analysis — resolves BPFO, BPFI, and BSF fault frequencies weeks before audible symptoms appear
  • Predictive Moisture — predicts cake moisture at discharge with ±0.6% variance
  • ROI Dashboard — quantifies cycle time savings, energy reduction, and rejection rate improvement

Results (90-Day Pilot — Specialty Resin Facility)

Metric Baseline AI + Chemrich
Average Spin Time 22 min 16.5 min (−25%)
Moisture Variance ±4.2% ±0.6%
Energy per Batch 14.5 kWh 11.2 kWh
Annual Capacity Baseline +18%
Downstream Rejection 12% <1%

Run Locally

git clone https://github.com/chemenova/ai-node
cd ai-node
pip install -r requirements.txt
streamlit run app.py

Deploy on Streamlit Cloud

  1. Fork this repo to chemenova/ai-node
  2. Go to share.streamlit.io
  3. Connect repo → set app.py as main file
  4. Deploy

Tech Stack

  • Streamlit — dashboard framework
  • SciPy FFT — vibration frequency analysis
  • NumPy/Pandas — physics simulation engine
  • Plotly — interactive charts

About

ChemeNova LLC is a U.S.-based AI chemical intelligence company.
Chemrich Global Chemrich Global is an international chemical manufacturing and distribution company that provides specialty chemicals, high-purity compounds, and advanced formulation solutions along with specialized AI intergrated pharmaceutical equipments.

Contact: sm3835@njit.edu

About

A real-time process analytics dashboard that transforms Chemrich centrifuge hardware into intelligent, self-optimizing assets. Built on Physics-Informed Neural Networks (PINNs) and FFT vibration analysis.

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