Abhijith SivaprasadanM.Sc. candidate at KTH · Stockholm, Sweden
Software / Portfolio
Software with engineering depth.
I bring professional TypeScript/NestJS backend experience together with scientific and engineering software. At QBurst, I implemented API endpoints, reliability fixes and endpoint tests. My public projects apply that software discipline to models, data and engineering analysis.
An open-source two-boiler steam-and-power twin with a carbon-aware Pyomo/HiGHS scheduler, OpenModelica FMI 2.0 simulation and a local Streamlit advisory GUI. Synthetic and uncalibrated; not an ABB product or live control system.
How does energy use change with production? Baseline normalisation and residual diagnostics checked against known-linear data, with explicit missing-meter handling. Alerts are not root-cause diagnoses.
Local electricity-investment GUI and shared Pyomo/HiGHS planner, with audited market coverage and 175 extracted public input references. Synthetic and uncalibrated; normalization, price-target decisions and weather conversion still gate historical fitting.
How do degradation and thermal transients affect a gas turbine? A Make-built educational simulator checked against hand calculations, an analytical transient and repeatable sensor-noise tests. Not commercial-engine validation.
How do radiation approximations affect calculated heat transfer? Published-data fixtures, a discrete-ordinates solver and scientific regression tests. Original work and third-party source retain distinct provenance.
How do fuel activity and allowance-price assumptions affect exposure? Tested Scope 1 calculations with contextual Scope 2 reporting and transparent demonstration factors. Not compliance advice.
Implemented and maintained backend API endpoints in TypeScript/NestJS, delivered reliability fixes, wrote endpoint tests including negative-path validation and used Postman automation in production-oriented agile teams.
Developed and independently validated a quantitative methodology for industrial energy performance mapping, covering KPI/EnPI design, load-driver logic, metering-gap assessment, deviation detection from noisy operational data and regulatory comparison. The work was desk-based method development and decision-support framing rather than hands-on plant execution, with electrical utilities and compressed air treated as relevant electrification pathways. Proprietary site detail is not republished.
Python · ISO 50001 · EU EED · KPI/EnPI Design · Energy Performance Mapping · Industrial Energy Analysis · Structured Reporting
QBurst is professional software experience; the public engineering repositories are separate independent or academic work, not QBurst client code. Streamlit and desktop interfaces run locally unless a case study explicitly links a hosted demo. Passing software checks does not establish physical-model validation.
Source case studies remain authoritative. Private inputs and restricted project details are not published.
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For backend, Python, data-tooling and research-software roles.