Industrial decarbonization / electrification R&D
This track is strongest where energy engineering, modelling, optimisation and prototype tools meet. The evidence covers quantitative industrial energy-performance mapping, heat-source dispatch optimisation and reproducible Python tools that turn operational assumptions into technical outputs. It should be read as method-development evidence for industrial decision support under messy data, mixed technologies and decarbonisation constraints.
R&D signal
Industrial energy balance, load-driver mapping, KPI/EnPI design, baseline normalization, scenario modelling and technology comparison across electrical utilities, compressed air, heating, cooling and process-support systems.
Python/PuLP linear and mixed-integer dispatch models, objective-function design, constraint handling and cost-emissions trade-off interpretation. Current growth area: automatic control and MATLAB/Simulink.
Reproducible engineering tools with CLI interfaces, Streamlit front ends, automated tests, PDF report generation and CI. Designed for decision-support and technical handover, not one-off data exploration.
Formal technical reports, regulatory comparison, documented assumptions, implementation roadmaps and clear translation from model outputs to industrial actions.
Project evidence
Developed and independently validated a quantitative methodology for industrial energy performance mapping, including load-driver logic, metering-gap assessment and deviation detection from noisy operational data. The work covered electrical utilities and compressed air systems, both directly relevant to industrial electrification pathways.
Designed and implemented a stepwise optimization model from first principles, coupling electricity market price signals to heat-source dispatch decisions and comparing cost and emissions objectives across present and future scenarios.
Built a reproducible prototype tool for baseline normalization, KPI reporting, anomaly flags, action tracking and PDF reporting. This is the strongest software evidence for turning research logic into a working proof-of-concept.
Development direction
The next growth step is deeper work in industrial electrification beyond system-level energy modelling: electrothermal process heating, thermal storage, cooling integration, electrochemical process routes, control-oriented modelling and experimental validation. The current strength is structured modelling and engineering tool-building; the strongest fit is an environment where that discipline can be applied in advanced simulation and experimental facilities as concepts move from model to prototype to implementation pathway.