An Automated Continuous Synthesis and Isolation for the Scalable Production of Aryl Sulfonyl Chlorides.

In this work, a continuous system to produce multi-hundred-gram quantities of aryl sulfonyl chlorides is described. The scheme employs multiple continuous stirred-tank reactors (CSTRs) and a continuous filtration system and incorporates an automated process control scheme. The experimental process outlined is intended to safely produce the desired sulfonyl chloride at laboratory scale. Suitable reaction conditions were first determined using a batch-chemistry design of experiments (DOE) and several isolation methods. The hazards and incompatibilities of the heated chlorosulfonic acid reaction mixture were addressed by careful equipment selection, process monitoring, and automation. The approximations of the CSTR fill levels and pumping performance were measured by real-time data from gravimetric balances, ultimately leading to the incorporation of feedback controllers. The introduction of process automation demonstrated in this work resulted in significant improvements in process setpoint consistency, reliability, and spacetime yield, as demonstrated in medium- and large-scale continuous manufacturing runs.

Online fault adaptive control for efficient resource management in Advanced Life Support Systems.

This article presents the design and implementation of a controller scheme for efficient resource management in Advanced Life Support Systems. In the proposed approach, a switching hybrid system model is used to represent the dynamics of the system components and their interactions. The operational specifications for the controller are represented by utility functions, and the corresponding resource management problem is formulated as a safety control problem. The controller is designed as a limited-horizon online supervisory controller that performs a limited forward search on the state-space of the system at each time step, and uses the utility functions to decide on the best action. The feasibility and accuracy of the online algorithm can be assessed at design time. We demonstrate the effectiveness of the scheme by running a set of experiments on the Reverse Osmosis (RO) subsystem of the Water Recovery System (WRS).

Online model-based diagnosis to support autonomous operation of an advanced life support system.

This article describes methods for online model-based diagnosis of subsystems of the advanced life support system (ALS). The diagnosis methodology is tailored to detect, isolate, and identify faults in components of the system quickly so that fault-adaptive control techniques can be applied to maintain system operation without interruption. We describe the components of our hybrid modeling scheme and the diagnosis methodology, and then demonstrate the effectiveness of this methodology by building a detailed model of the reverse osmosis (RO) system of the water recovery system (WRS) of the ALS. This model is validated with real data collected from an experimental testbed at NASA JSC. A number of diagnosis experiments run on simulated faulty data are presented and the results are discussed.