Humberto E. Garcia

A reconfigurable hybrid system and its application to power plant control

This paper presents a reconfigurable approach to implement decision and control systems for complex dynamic processes. The proposed supervisory control system is a reconfigurable hybrid architecture structured into three functional levels of hierarchy, namely, execution, supervision, and coordination. While the bottom execution level is constituted by either reconfigurable continuously varying or discrete-event systems, the top two levels are necessarily governed by reconfigurable sets of discrete-event decision and control systems. Based on the process status, the set of active control and supervisory algorithm is chosen. The underlying concept of the proposed reconfigurable hybrid decision and control is described along with its implementation on the feedwater system at the Experimental Breeder Reactor II of the Argonne National Laboratory. The performance of the hybrid system to accommodate component failures is then evaluated in an in-plant experiment. >

Model-based detection of routing events in discrete flow networks

A theoretical framework and its practical implications for formulating and implementing model-based monitoring of discrete flow networks are discussed. Possible flows of items are described as discrete-event (DE) traces. Each trace defines the DE sequence(s) that are triggered when an entity follows a given flow-path, visiting tracking locations within the monitored system. To deal with alternative routing, creation of items, flow bifurcations and convergences are allowed. Given the set of possible discrete flows, a possible-behavior model-an interacting set of automata-is constructed, where each automation models the item discrete flow at each tracking location. In this model, which assumes total observability, event labels or symbols contain all the information required to unambiguously distinguish each discrete movement. Within the possible behavior, there is a special sub-behavior whose occurrence is required to be detected. The special behavior may be specified by the occurrence of routing events, such as faults or route violations, for example. These intermittent or non-persistent events may occur repeatedly. An observation mask is then defined, characterizing the observation configuration available for collecting item tracking data. The verification task is to determine whether this observation configuration is capable of detecting the specified special behavior. The assessment is accomplished by evaluating several observability notions, such as detectability and diagnosibility. If the corresponding property is satisfied, associated formal observers are constructed to perform the monitoring task at hand. The synthesis of observation masks may also be conducted to suggest optimal observation configurations (specifying number, type, and tracking locations of observation devices) guaranteeing the detection of special events and to construct associated monitoring agents. The developed framework, modeling methodology, and supporting techniques for defining and implementing discrete flow monitoring of entity movements are presented and illustrated with examples.

State-space supervisory control of reconfigurable discrete event systems

The discrete event theory of supervisory and state feedback control offers many advatages for implementing supervisory systems. Algorithmic concepts have been introduced to ensure that the supervising algorithms are correct and meet the specifications. In the current methodology, it is, in general, assumed that the supervisory specifications are invariant during the operation of the system or, at least, until a given supervisory task is completed. However, there are many practical applications where the supervising specifications need to be updated in real time. For example, when dealing with complex processes, the tasks of supervisory systems analysis and synthesis can be facilitated by partitioning the controlled Discrete-Event System (DES) into several subprocesses. This partitioning is based on operational or physical considerations and a unique supervisor is assigned to control each subprocess at a given instant of time. When a decision maker at a higher level of hierarchy decides to change the super...

Operations Optimization of Nuclear Hybrid Energy Systems

Abstract Nuclear hybrid energy systems (NHESs) have been proposed as an effective element to incorporate high penetration of clean energy (e.g., nuclear and renewable). This paper focuses on the operations optimization of two specific NHES configurations to address the variability raised from wholesale electricity markets and renewable generation. Both analytical and numerical approaches are used to obtain the optimal operations schedule. Key economic figures of merit are evaluated under optimized and constant (i.e., time-invariant) operations to demonstrate the benefit of the optimization, which also suggests the economic viability of the considered NHESs under the proposed operations optimizer. Furthermore, sensitivity analysis on commodity prices is conducted for better understanding of the considered NHESs.

Stochastic event counter for discrete-event systems under unreliable observations

This paper addresses the issues of counting the occurrence of special events in the framework of partially- observed discrete-event dynamical systems (DEDS). First, we develop a noble recursive procedure that updates active counter information state sequentially with available observations. In general, the cardinality of active counter information state is unbounded, which makes the exact recursion infeasible computationally. To overcome this difficulty, we develop an approximated recursive procedure that regulates and bounds the size of active counter information state. Using the approximated active counting information state, we give an approximated minimum mean square error (MMSE) counter. The developed algorithms are then applied to count special routing events in a material flow system.

An identification scheme combining first principle knowledge, neural networks, and the likelihood function

An identification scheme is described for modeling uncertain systems. The method combines a physics-based model with a nonlinear mapping for capturing unmodeled physics and a statistical estimation procedure for quantifying any remaining process uncertainty. The technique has been used in predictive maintenance applications to detect operational changes of mechanical equipment by comparing the model output with the actual process output. Tests conducted on a peristaltic pump to detect incipient failure are described. The inclusion of unmodeled physics and a statistical representation of uncertainties results in lower false alarm and missed detection rates than other methods.

Nuclear Hybrid Energy Systems Regional Studies: West Texas & Northeastern Arizona

The primary objective of this study is to conduct a preliminary dynamic analysis of two realistic hybrid energy systems (HES) including a nuclear reactor as the main baseload heat generator (denoted as nuclear HES or nuclear hybrid energy systems [NHES]) and to assess the local (e.g., HES owners) and system (e.g., the electric grid) benefits attainable by the application of NHES in scenarios with multiple commodity production and high penetration of renewable energy. It is performed for regional cases - not generic examples - based on available resources, existing infrastructure, and markets within the selected regions. This study also briefly addresses the computational capabilities developed to conduct such analyses, reviews technical gaps, and suggests some research paths forward.

Operational analysis and improvement of a spent nuclear fuel handling and treatment facility using discrete event simulation

Spent nuclear fuel handling and treatment often require facilities with a high level of operational complexity. Simulation models can reveal undesirable characteristics and production problems before they become readily apparent during system operations. The value of this approach is illustrated here through an operational study, using discrete event modeling techniques, to analyze the Fuel Conditioning Facility at Argonne National Laboratory and to identify enhanced nuclear waste treatment configurations. The modeling approach and results of what-if studies are discussed. An example on how to improve productivity is presented.

New results on discrete-event counting under reliable and unreliable observation information

We present an approach for addressing the issues of detecting repeated fault events in the framework of model-based monitoring of discrete-event systems (DES) under reliable and unreliable observation information. The analysis task is to determine whether a certain observation configuration is capable of reporting the occurrence of fault events while satisfying the performance requirements. If the reliability of observation information is assured, the assessment is accomplished by evaluating diagnosability notions of interest. To evaluate the notions of diagnosability regarding repeated fault counting, polynomial-time verification algorithms are developed. In order to deal with unreliable observation information, the concept of detection confidence is introduced, which measures the quality of fault counting. An algorithm computing detection confidence is conjectured. For online fault counting, we develop a new online fault counting algorithm assuming observation reliability. The developed algorithm has lower time and space complexities than an online diagnosis algorithm reported in literature for counting the occurrence of repeated faults. This online algorithm is naturally extended to handle the unreliable observation information.

Discrete event simulation of fuel transfer strategies for defueling a nuclear reactor

Fuel handling and condition ing activities for the decommissioning of the Experimental Breeder Reactor Il are being performed at Argonne National Laboratory. Discrete event simulation and optimization techniques are being investigated to plan, supervise and perform these activities in such a way that productivity can be improved. The idea is to characterize this operation as a collec tion of interconnected serving cells, and then apply operational research tech niques to identify appropriate planning schedules for the given scenarios. This paper describes an application of discrete event simulation to evaluate fuel transfer strategies and identify optimal solutions among distributed resources.