Integrated gasification combined cycle (IGCC) plants with CO 2 capture have strong potential in the future carbon-constrained world. In these plants, the CO 2 and COS content of the syngas at the inlet of the acid gas removal process should be within certain limits in order to satisfy the environmental limits on sulfur and CO 2 emissions. To satisfy these constraints, the syngas from the gasification process is passed through water gas shift reactor(s). A premium is placed on sulfurtolerant catalysts since the syngas may contain COS and H 2 S. In comparison to the sweet-shift process, the sour-shift process results in higher overall efficiency because of the higher temperature of the feed syngas and requirement of less additional steam for the shift reactions. The optimal operating conditions and the dimensions of the sour shift reactors can be obtained by considering the effect of a number of key variables. With this motivation, a 1-D mathematical model of a sour water gas shift (WGS) reactor has been developed by considering mass, momentum, and energy conservation equations. The experimental data available in the open literature are reconciled for measurement errors after gross errors are removed and then used to obtain the rate parameters. Subsequently, the developed model is used to study the performance of the WGS reactor as part of an IGCC plant with CO 2 capture. The results presented in this paper are very useful in designing, analyzing, and operating the sour water gas shift reactors.

State and parameter estimation plays an important role in many different engineering fields. Estimation of systems described by linear and nonlinear differential equations has been very well studied in the literature. Work in the past decade has been geared toward efficiently extending these algorithms to constrained systems. Of recent interest is the evaluation of state estimation techniques for differential-algebraic equation (DAE) systems. The algebraic equations in these studies are exact, an example being the mole fractions adding to unity. However, there are situations where algebraic equations can be of both certain and uncertain types. In this paper, we propose a modified extended Kalman filter (EKF) approach that can handle uncertainties in both differential and algebraic equations, and equality constraints. We also show the importance of this work in estimation of mole fraction, temperature, and pressure profiles in a water gas shift reactor. The impact of location and type of measurements on the estimation accuracy is also studied.

Droplet microfluidics is likely to play a central role in the development of lab-on-a-chip technologies and as a result, significant research is directed towards this field. Understanding the spatio-temporal dynamics of discrete droplets inside microfluidic devices and the design of microfluidic devices for specific tasks are some of the dominant research topics. These works have since resulted in the development of microfluidic devices with functionalities such as sorting, merging, synchronization, storing etc. However, the anticipated application of microfluidic devices to more complex problems will require more integrated devices that can incorporate the above functionalities on a single chip. In the current work, we present a genetic algorithm optimization based design tool for discovering very large scale integration of discrete microfluidic networks for a given objective function. The application of the algorithm is demonstrated through a combinatorial sequencing problem, where the objective is to achieve three different droplet combinatorial sequences for three different droplet types. Multiple fascinating, but non-obvious designs were discovered for this application. It is difficult to imagine such devices being designed using trial and error experimental procedure, which has been the main route for obtaining microfluidic device designs. With advances in technologies for fabrication of microfluidic devices, the current tool can be a significant step towards drastically cutting down on the laborious trial-and-error design process and help in developing droplet microfluidics based lab-on-a-chip platforms cheaper and faster.

Growing complexity of processes necessitates the use of information from sensors along with first-principles mathematical models to ensure safe and optimal operations. Use of sensors in complex processes requires identifying optimal location of sensors that can maximize information from a process. Classical sensor placement approaches for nonlinear systems that use state estimation schemes usually incorporate linearized models around the steady-state operating point. However, such approaches face difficulties when abnormalities or disturbances drift the system away from the normal operating point. Therefore, use of models that can appropriately track the behavior of the system in the sensor placement framework are of interest. However, the computational complexity of the detailed models makes such approaches intractable. In this work, we develop a sensor placement framework that combines genetic algorithms and the extended Kalman filter to obtain optimal sensor locations. Within this framework, we have investigated the applicability of simplified models by comparing the results of sensor placement for simplified and detailed models. The effect of the simplified models on the estimation accuracy and the optimal sensor network is further evaluated by analyzing the sensitivity to different parameters. Results show that an appropriate simplified model can not only significantly reduce the computational time of the sensor placement algorithm, but also yield a senor network with similar characteristics as the sensor network obtained using the detailed model. Further, information loss in using simplified models in sensor placement may be partially compensated through tuning of the filter parameters, resulting in acceptable, optimal sensor placement solutions.

Major performance losses occur in process industries due to failures that are not identified at the incipient stage.Early detection of such faults is also critical for the safety of the equipment, operating personnel, and other resources. When a fault occurs in a system, it can propagate and affect several process variables. Variables that need to be measured in order to detect and diagnose the faults have to be identified and chosen economically. An algorithmic approach for identifying the optimal number, type, and location of the sensors for fault detection and diagnosis is useful, particularly for large-scale, chemical process plants. In this work, previous algorithms for sensor placement that use signed directed graph (SDG) models for the process are enhanced to include magnitude ratio (MR) information to identify more promising sensor locations. Further, we also study the combination of fault evolution sequences (FES) already introduced in the literature and the MR information for effective fault diagnosis. This is achieved by including the idea of artificial sensors that represent pairwise sensors from the original list of possible sensors. Based on the MR and FES, the artificial sensors can assume discrete values, much like the SDG approach. A symmetric difference operator is used on both the original sensors (whose behaviors are modeled as before using SDG) and the artificial sensors to identify sensor placements. This approach elegantly incorporates the new MR and FES information in the original well-accepted SDG based sensor placement algorithms. Several case studies are presented to demonstrate the usefulness of the proposed approach.