Abstract: Methods and apparatus for capturing carbon dioxide from ambient air and delivering said carbon dioxide to an enclosed environment are described. In general, the methods and apparatus comprise contacting a packed bed or fluidized bed device with a stream of ambient air, wherein the packed bed or fluidized bed device comprises a humidity-sensitive sorbent material that adsorbs carbon dioxide from the ambient air; contacting the packed bed or fluidized bed device with a stream of humid air to release the adsorbed carbon dioxide; delivering the released carbon dioxide to an enclosed environment; and optionally, repeating the steps of contacting the packed bed or fluidized bed device with ambient air and humid air in an alternating fashion.

Abstract: Methods and apparatus for capturing carbon dioxide from ambient air and delivering said carbon dioxide to an enclosed environment are described. In general, the methods and apparatus comprise contacting a packed bed or fluidized bed device with a stream of ambient air, wherein the packed bed or fluidized bed device comprises a humidity-sensitive sorbent material that adsorbs carbon dioxide from the ambient air; contacting the packed bed or fluidized bed device with a stream of humid air to release the adsorbed carbon dioxide; delivering the released carbon dioxide to an enclosed environment; and optionally, repeating the steps of contacting the packed bed or fluidized bed device with ambient air and humid air in an alternating fashion.

Abstract: A model predictive controller for a process control system which includes a real-time executive sequencer and an interactive modeler. The interactive modeler includes both a process model and an independent disturbance model. The process model represents the dynamic behavior of the physical process, while the disturbance model represents current and future deviations from the process model. The interactive modeler estimates current process states from the process model and input data received from the executive sequencer. The executive sequencer then projects a set of future process parameter values, which are sought to be controlled, over a predetermined control horizon. The interactive modeler then solves a set of equations as to how the physical process will react to control changes in order to determine an optimized set of control changes. As a result, the process control system will be able to accurately track a predetermined set-point profile in the most effective and cost efficient manner.

Abstract: A model predictive controller for a process control system which includes a real-time executive sequencer and an interactive modeler. The interactive modeler includes both a process model and an independent disturbance model. The process model represents the dynamic behavior of the physical process, while the disturbance model represents current and future deviations from the process model. The interactive modeler estimates current process states from the process model and input data received from the executive sequencer. The executive sequencer then projects a set of future process parameter values, which are sought to be controlled, over a predetermined control horizon. The interactive modeler then solves a set of equations as to how the physical process will react to control changes in order to determine an optimized set of control changes. As a result, the process control system will be able to accurately track a predetermined set-point profile in the most effective and cost efficient manner.