in Wiley Online Library (wileyonlinelibrary.com) Slagging entrained-flow gasifiers operate above the melting temperature of the ash. As slag is highly nonwetting on the surface of char (carbon) particles, it is likely that it will agglomerate into one or several slag droplets and some of these droplets can detach from the char particles. If the slag exists in the form of droplets on the char surface rather than as a solid shell around the unreacted char particle, a shrinking particle model would be more physically realistic representation in comparison to the widely used shrinking core model (SCM). In the early section of the gasifier, the temperature remains below the ash melting temperature and, therefore, the SCM is more appropriate in this region. With this motivation, a novel hybrid shrinking-core shrinking-particle model has been developed. The model provides spatial profile of a number of important variables that are not available from the traditional SCM.

Molten slag flows along the wall of slagging entrained-flow gasifiers. A lower operating temperature of these gasifiers can result in slag buildup that may eventually lead to shut down. Thus, the thickness of the slag layer is an important operating variable. However, it is difficult to monitor the thickness of the slag layer due to the extremely harsh environment inside these gasifiers. To investigate the impact of various operating conditions on the slag layer thickness, a dynamic model has been developed by including models of slag deposition, transport, and a flowing slag layer in a previously developed hybrid shrinking-core, shrinking-particle model of a gasifier. The enhanced model is used to study the effect of particle size distribution, switch in coal type, change in ash composition, and various operating conditions on the slag layer thickness. The model is used to identify important variables that significantly affect the slag layer thickness. The study shows that optimal operating conditions should be decided by evaluating the risk of slag solidification at lower temperature and accelerated degradation of the refractory at higher temperature while maintaining the desired extent of carbon conversion for the given type of coal.

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.

The goal of this work is to help synthesize a sensor network to detect and diagnose faults and to monitor conditions of the key equipment items. Faults or events that lead to loss in productivity occur over time. These faults, if not detected and mitigated at an early stage, can lead to severe loss in productivity, efficiency, and equipment damage, and can be a safety hazard. The desired algorithm for sensor network design would provide information about the number, type and location of sensors that should be deployed for fault diagnosis and condition monitoring of a plant. In this work, the focus was on the integrated gasification combined cycle (IGCC) power plant where the faults at the equipment level and the plant level are considered separately. At the plant level, the objective is to observe whether a fault has occurred or not and identify the specific fault. For component-level faults, the objective is to obtain quantitative information about the extent of a particular fault. For the model-based sensor network design, high-fidelity process model of the IGCC plant is the key requirement.

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