Publications

Klise, Katherine A.; Nicholson, Bethany L.; Laird, Carl D.

Abstract not provided.

Klise, Katherine A.; Bynum, Michael L.

In response to anticipated resource shortfalls related to the treatment and testing of COVID-19, many communities are planning to build additional facilities to increase capacity. These facilities include field hospitals, testing centers, mobile manufacturing units, and distribution centers. In many cases, these facilities are intended to be temporary and are designed to meet an immediate need. When deciding where to place new facilities many factors need to be considered, including the feasibility of potential locations, existing resource availability, anticipated demand, and accessibility between patients and the new facility. In this project, a facility location optimization model was developed to integrate these key pieces of information to help decision makers identify the best place, or places, to build a facility to meet anticipated resource demands. The facility location optimization model uses the location of existing resources and the anticipated resource demand at each location to minimize the distance a patient must travel to get to the resource they need. The optimization formulation is presented below. The model was designed to operate at the county scale, where patients are grouped per county. This assumption can be modified to integrate other scales or include individual patients.

Process Safety and Environmental Protection

Zhen, Todd; Klise, Katherine A.; Cunningham, Sean; Marszal, Edward; Laird, Carl D.

Flame detectors provide an important layer of protection for personnel in petrochemical plants, but effective placement can be challenging. A mixed-integer nonlinear programming formulation is proposed for optimal placement of flame detectors while considering non-uniform probabilities of detection failure. We show that this approach allows for the placement of fire detectors using a fixed sensor budget and outperforms models that do not account for imperfect detection. We develop a linear relaxation to the formulation and an efficient solution algorithm that achieves global optimality with reasonable computational effort. We integrate this problem formulation into the Python package, Chama, and demonstrate the effectiveness of this formulation on a small test case and on two real-world case studies using the fire and gas mapping software, Kenexis Effigy.

Bynum, Michael L.; Klise, Katherine A.; Hart, David B.; Haxton, Terranna H.; Murray, Regan M.; Laird, Carl D.

Abstract not provided.

Haxton, Terranna H.; Laky, Daniel L.; Klise, Katherine A.; Laird, Carl D.; Burkhardt, Jonathan B.; Murray, Regan M.

Abstract not provided.

Computer Aided Chemical Engineering

Seth, Arpan; Hackebeil, Gaberiel A.; Haxton, Terranna; Murray, Regan; Laird, Carl D.; Klise, Katherine A.

Drinking water utilities use booster stations to maintain chlorine residuals throughout water distribution systems. Booster stations could also be used as part of an emergency response plan to minimize health risks in the event of an unintentional or malicious contamination incident. The benefit of booster stations for emergency response depends on several factors, including the reaction between chlorine and an unknown contaminant species, the fate and transport of the contaminant in the water distribution system, and the time delay between detection and initiation of boosted levels of chlorine. This paper takes these aspects into account and proposes a mixed-integer linear program formulation for optimizing the placement of booster stations for emergency response. A case study is used to explore the ability of optimally placed booster stations to reduce the impact of contamination in water distribution systems.

Bynum, Michael L.; Liu, Jianfeng L.; Zhen, Todd Z.; Klise, Katherine A.; Laird, Carl D.

Abstract not provided.

Journal of Water Resources Planning and Management

Hart, David B.; Rodriguez, J.S.; Burkhardt, Jonathan; Borchers, Brian; Laird, Carl D.; Murray, Regan; Klise, Katherine A.; Haxton, Terranna

Sampling of drinking water distribution systems is performed to ensure good water quality and protect public health. Sampling also satisfies regulatory requirements and is done to respond to customer complaints or emergency situations. Water distribution system modeling techniques can be used to plan and inform sampling strategies. However, a high degree of accuracy and confidence in the hydraulic and water quality models is required to support real-time response. One source of error in these models is related to uncertainty in model input parameters. Effective characterization of these uncertainties and their effect on contaminant transport during a contamination incident is critical for providing confidence estimates in model-based design and evaluation of different sampling strategies. In this paper, the effects of uncertainty in customer demand, isolation valve status, bulk reaction rate coefficient, contaminant injection location, start time, duration, and rate on the size and location of the contaminant plume are quantified for two example water distribution systems. Results show that the most important parameter was the injection location. The size of the plume was also affected by the reaction rate coefficient, injection rate, and injection duration, whereas the exact location of the plume was additionally affected by the isolation valve status. Uncertainty quantification provides a more complete picture of how contaminants move within a water distribution system and more information when using modeling results to select sampling locations.

Zhen, Todd Z.; Klise, Katherine A.; Cunningham, Sean C.; Marszal, Edward M.; Laird, Carl D.

Abstract not provided.

Woodruff, David L.; Staid, Andrea S.; Nicholson, Bethany L.; Klise, Katherine A.

Abstract not provided.

Seth, Arpan S.; Hackebeil, Gaberiel H.; Haxton, Terranna H.; Murray, Regan M.; Laird, Carl D.; Klise, Katherine A.

Abstract not provided.

Klise, Katherine A.; Laird, Carl D.; Nicholson, Bethany L.; Staid, Andrea S.; Woodruff, David L.

Abstract not provided.

Klise, Katherine A.; Laky, Daniel L.; Laird, Carl D.; Burkhardt, Jonathan B.; Murray, Regan M.; Haxton, Terra H.

Abstract not provided.

Klise, Katherine A.; Laird, Carl D.; Hart, David B.; Nicholson, Bethany L.; Bynum, Michael L.; Murray, Regan M.

Abstract not provided.

Akula, Paul A.; Bhattacharyya, Debangsu B.; Eslick, John C.; Klise, Katherine A.; Laird, Carl D.; Staid, Andrea S.; Woodruff, David L.

Abstract not provided.

Klise, Katherine A.; Nicholson, Bethany L.; Bynum, Michael L.; Hart, David B.; Laird, Carl D.

Abstract not provided.

Klise, Katherine A.; Nicholson, Bethany L.; Bynum, Michael L.; Hart, David B.; Laird, Carl D.

Abstract not provided.

Klise, Katherine A.; Laird, Carl D.; Nicholson, Bethany L.; Parrott, Lori K.

Abstract not provided.

Klise, Katherine A.; Nicholson, Bethany L.; Laird, Carl D.; Ravikumar, Arvind P.; Brandt, Adam B.

Abstract not provided.

Klise, Katherine A.; Rodriguez, Jose S.; Bynum, Michael B.; Laird, Carl D.; Haxton, Terra H.; Hart, David B.; Murray, Regan M.

Abstract not provided.

Nicholson, Bethany L.; Klise, Katherine A.; Laird, Carl D.

Abstract not provided.

Klise, Katherine A.; Laird, Carl D.; Nicholson, Bethany L.

Continuous or regularly scheduled monitoring has the potential to quickly identify changes in the environment. However, even with low - cost sensors, only a limited number of sensors can be deployed. The physical placement of these sensors, along with the sensor technology and operating conditions, can have a large impact on the performance of a monitoring strategy. Chama is an open source Python package which includes mixed - integer, stochastic programming formulations to determine sensor locations and technology that maximize monitoring effectiveness. The methods in Chama are general and can be applied to a wide range of applications. Chama is currently being used to design sensor networks to monitor airborne pollutants and to monitor water quality in water distribution systems. The following documentation includes installation instructions and examples, description of software features, and software license. The software is intended to be used by regulatory agencies, industry, and the research community. It is assumed that the reader is familiar with the Python Programming Language. References are included for addit ional background on software components. Online documentation, hosted at http://chama.readthedocs.io/, will be updated as new features are added. The online version includes API documentation .

Klise, Katherine A.; Hart, David B.; Moriarty, Dylan; Bynum, Michael L.; Murray, Regan M.; Burkhardt, Jonathan B.; Haxton, Terra H.

Drinking water systems face multiple challenges, including aging infrastructure, water quality concerns, uncertainty in supply and demand, natural disasters, environmental emergencies, and cyber and terrorist attacks. All of these have the potential to disrupt a large portion of a water system causing damage to infrastructure and outages to customers. Increasing resilience to these types of hazards is essential to improving water security. As one of the United States (US) sixteen critical infrastructure sectors, drinking water is a national priority. The National Infrastructure Advisory Council defined infrastructure resilience as “the ability to reduce the magnitude and/or duration of disruptive events. The effectiveness of a resilient infrastructure or enterprise depends upon its ability to anticipate, absorb, adapt to, and/or rapidly recover from a potentially disruptive event”. Being able to predict how drinking water systems will perform during disruptive incidents and understanding how to best absorb, recover from, and more successfully adapt to such incidents can help enhance resilience.

Klise, Katherine A.; Murray, Regan M.; Bynum, Michael L.; Hart, David B.; Moriarty, Dylan

Abstract not provided.

Environmental Modelling and Software

Klise, Katherine A.; Bynum, Michael L.; Moriarty, Dylan; Murray, Regan

Water utilities are vulnerable to a wide variety of human-caused and natural disasters. The Water Network Tool for Resilience (WNTR) is a new open source Python™ package designed to help water utilities investigate resilience of water distribution systems to hazards and evaluate resilience-enhancing actions. In this paper, the WNTR modeling framework is presented and a case study is described that uses WNTR to simulate the effects of an earthquake on a water distribution system. The case study illustrates that the severity of damage is not only a function of system integrity and earthquake magnitude, but also of the available resources and repair strategies used to return the system to normal operating conditions. While earthquakes are particularly concerning since buried water distribution pipelines are highly susceptible to damage, the software framework can be applied to other types of hazards, including power outages and contamination incidents.