Case CO₂ capture

Over the years, research on climate change and global environment has led to an increased level of awareness regarding the possible negative consequences of human-made greenhouse gas emissions to the earth atmosphere. Emission of CO₂ originating from large industrial point sources (e.g. energy production from combustion of coal, natural gas or bio-fuels, production of materials such as aluminium or cement, etc.) is one portion of the big picture of particular interest. Due to an increasing global demand for energy, consumables and utilities, and a rather slow global introduction/expanding of renewable/environmental friendly energy sources, these industries are not expected to rapidly decrease in extent, but the possibility for removing CO₂ from these point emissions, thus significantly reducing the carbon footprint, is gaining increased attention.

When it comes to CO₂ capture, CO₂ absorption using amine-based solvents is a promising technology. Adding a capture plant to a power plant will constitute a significant power cost due to the regeneration process for the amine solvents, and this loss will strongly depend on whether the plant operates optimally or not. In a hypothetical (but plausible) future scenario where companies are forced (for instance by governments) to perform CO₂ capture and/or are penalized for emitting CO₂, the entire production plant profitability will depend on the optimal operation of the capture plant.

Cybernetica develops model-based solutions for amine-based CO₂ capture plants. Together with Sintef and Technology Center Mongstad, we are aiming to demonstrate how capture plants can operate more effectively using Nonlinear Model Predictive Control (NMPC). The capture plants are complex, interconnected processes with a variety of unit operations. Understanding the behavior of the plant under various operating conditions is crucial in order to achieve optimal operation. A typical challenge for power plants is that the energy demand will govern the power plant load and consequently the emission rate of CO₂, to which the capture plant must adapt. This will give rise to diurnal variations in plant inputs, making the operation more challenging. In on-line use, the model-based approaches are intended for monitoring (soft sensing) and control purposes. This will strive to aid the plant operators and ensure optimal operation.