The dataset represents dynamic simulation and estimation data for a CO2 scrubbing process in a bio‑CO2 purification plant, used to design and test a real‑time optimization and Kalman filter–based control framework for solvent flow.
Purpose: To estimate unmeasured mass flows (CO2 inlet gas flow and rich solvent flow) in a CO2 scrubber using first‑ and second‑order Kalman filters.
To compute optimal lean solvent flow setpoints that minimize solvent usage while maintaining food‑grade CO2 purity and stable operation.
To quantify potential performance and economic gains of an RTO strategy compared to conventional LG‑ratio control.
Nature: Synthetic/virtual dataset generated from a detailed dynamic process model of a CO2 purification plant implemented in K‑Spice, with Kalman filters and optimizers implemented in MATLAB and coupled via OPC.
Time‑series data capturing process variables (flows, temperatures, pressures, compositions), estimated states, and controller/optimizer setpoints under ramps, load changes, and disturbances.
Contains noisy measurements reflecting realistic sensor variance and process noise, configured via covariance matrices and transmitter noise settings.
Scope: Focused on a single-unit operation (CO2 scrubber) within a larger biogas upgrading and CO2 purification train, with gas composition >99.6% CO2 at the scrubber inlet.
Covers operation from turndown to full capacity, including capacity ramps, disturbance tests (inlet temperature, pump discharge pressure, condenser pressure), and comparison of baseline LG‑ratio control vs fmincon and genetic algorithm optimization.
Supports evaluation of key performance indicators: CO2 purity (>99.8% at outlet), lean solvent reduction (~24–25% at design conditions), production increase (~6.3%), and implied annual revenue impact for a 55,000 t/y bio‑CO2 plant.
