RESEARCH PLAN AND INTERESTS
John W. Norton, Jr.
Doctoral Candidate, Civil and Environmental Engineering
University of Michigan
OVERVIEW
My research interests concern the technical and financial optimization of various civil
infrastructure systems with my primary research focus being the optimization of drinking
water treatment and delivery networks. I am particularly interested in the financial analysis
and technological implementation of distributed water treatment technologies used to
address network derived water-quality degradation following the approach of Weber (2002;
2004). My current research focus concerns the technical and financial analysis and
implementation of distributed technologies to address different types of water quality
degradation. Current research involves prediction of water age with distribution systems,
optimal implementation of advanced water treatment technologies, minimizing costs
required to maintain a certain water quality and maximizing water quality given a fixed cost.
I expect to continue my primary research agenda in water treatment and delivery since there
are many interesting issues to pursue. However, when I have settled in an academic position
I plan to broaden my research focus to the general field of civil infrastructure technology
selection, optimization, and implementation. There are two good reasons for this. First, I
believe that the approach and general methodology I have utilized in examining potable
water networks is transferable to the broader arena of civil infrastructure systems. Second, I
want to broaden the pool of prospective doctoral students that will be attracted to my
research agenda and approach. I believe a combination of financial and technical analysis is
crucial to the success of civil engineering decision making.
POTENTIAL FUNDING SOURCES AND COLLABORATIONS
I am co-PI on a two-year, $91,000 research effort funded by the National Water Research
Institute to investigate distributed technologies. Although this is an extremely competitive
funding source, I expect to successfully complete this current project and remain viable for
future funding opportunities from this agency. I also intend to pursue funding from
traditional sources such as the EPA and NSF. Although these are typically the most
competitive funding sources, they provide an opportunity for fairly broad research interests
and can be used to leverage other funding sources. Finally, I am strongly interested in
developing industrially funded research collaborations similar to the Geosynthetic Research
Institute at Drexel or the Materials Research Institute at Penn State. I believe it is critical to
establish strong links within the industry to maintain a constructive and relevant research
agenda. I am strongly interested in working in a research and academic environment that
fosters and rewards collaborative, interdisciplinary research efforts.
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STUDENT DEVELOPMENT AND WORK PLAN
I envision significant opportunities for collaboration and learning in pursuit of my research
interests, and have given considerable thought to graduate student development while
achieving these goals. In particular, since many of these research interests are systems-based
analyses instead of focused research investigations, I plan on emphasizing the need for clear
assumptions and mathematical modeling approaches in my students’ efforts to develop
testable research hypotheses. I will involve my students in the entire research process,
including the quest for funding. In my own case, I developed a significant maturity of
expression and capability from developing my own numerous – and ultimately successful –
research proposals. I believe it is a latent disservice to graduate students to isolate them from
the funding source ultimately driving their work.
SHORT-TERM RESEARCH GOALS
Water age model
I have developed a mathematical model to characterize the variation in water age within a
distribution network. My model is a closed-form, lower-bound solution of water age
variation as a function of position within a geometrically simple network. Although I am
finishing a paper describing this approach, there is still a considerable amount of work to
perform in this area. An important extension of this work will be to determine the variation
in water age at any particular point. DiGiano et al. (2005) reported very detailed water age
data from two east coast water utilities that will allow the calibration and validation of a
model describing variation in water age at a point location. In addition, I am working on a
water age model for the general case of water age distribution across a non-circular, convex
distribution network with arbitrary water treatment plant location. A fundamental tenet of
my existing work is the presupposition of the age dependence of water quality degradation.
However, despite clear evidence for this assumption within the water sector, an original
paper clearly articulating this phenomenon does not exist in the literature. I plan on
addressing this deficiency with a significant review paper as soon as feasible.
Technology selection and cost estimation of distributed treatment units
My work thus far has concerned the breakeven amount available to build and operate each
distributed unit over a given design life. The next step will be to identify the functional
requirements needed for proper operation of each distributed unit and then to identify
technologies capable of meeting each functional requirement. Recent literature has described
the dynamic nature of technology implementation in the water utility sector (Means et al.,
2005). I will characterize the cost characteristics of each technology component to predict
the optimal combination of components and estimated cost required for the construction of
each distributed treatment unit to obtain an overall system implementation cost. The
comparison of breakeven cost with estimated unit cost will determine the feasibility of
utilizing distributed units to address network derived water quality degradation using
current technologies and costs. Although this research component has immediate
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consequences in determining the cost implications of the DOT-Net approach, the greater
significance will be in delineating the technical requirements and operational framework of
distributed technology implementation, similar to Fane et al.’s analysis (2002) of urban water
reuse.
LONG-TERM RESEARCH GOALS
Large-scale infrastructure systems data sets
My research focus requires significant data to derive trends and support conclusions. For
instance, my initial efforts required the use of data sets describing water utilities, technology
costs, and urban population characteristics. Publicly available, large scale data sets are crucial
for infrastructure systems study and recent research has discussed how to manage such data
and overcome data gaps by using probability methods (Yao and Natke, 1995; Aktan, 2003). I
would expect that collecting and manipulating such data would be a significant part of my
future students’ research efforts, either as a master’s thesis or part of a doctoral dissertation,
although it is clear that new methods of data collection can be a thesis topic in and of
themselves (e.g. Andersson, 2004; Chen et al. 2005). Potential data sets include raw water
costs in various climates, water storage costs, technology implementation costs, urban
resource consumption and cycling, waste composition, and others. These sorts of
comparative data are crucial for analysis capable of driving urban systems towards greater
efficiency and reduced waste.
Current versus future optimal technology selection
There is good reason to believe that the financial attractiveness of the distributed treatment
units will be controlled by the technology costs of a few components, most likely those
associated with the electronic aspects of remote monitoring and control. Previous research
efforts describing water and wastewater technology costs have identified scale economies
sensitive to specific technologies (e.g. Deininger and Su, 1973; Fraquelli and Giandrone,
2003) or focused on technologies relevant to small systems due to economies of scale (Clark,
1980; Clark et al., 1991). Each of the costs will have scale economies depending on the extent
of production and implementation and more significantly, it is expected that these scale
economies will fluctuate most dramatically for those technology components that currently
dominate the cost structure. Analysis of the underlying cost structure and the economies of
scale of the distributed unit technology components can identify break-even points that will
allow cost effective implementation of the distributed treatment approach.
Optimal technology selection over multi-year scenarios
It is very likely that the cost of the various components and treatment technologies will vary
over time. For example, electronic components such as the sensors and remote monitoring
equipment are likely to increase in capability and decrease in cost (Flamm, 2003). As a result,
from year to year there are likely to be considerable differences in the optimal selection of
technologies and components used within the distributed unit. Poor selection could result in
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sub-optimal technology lock-in with substantial switching costs (Katz and Shapiro, 1994).
This research goal would focus on optimal technology selection with changing capability and
cost structures and investigate solutions such as modular components to reduce switching
costs.
RESEARCH AND FACILITY NEEDS
My research is primarily dependent on robust data sets and computer analysis and I do not
plan on any significant laboratory investigation. As such, my primary research needs are
computer-related (for instance, analytical software and proprietary data sets along with
robust hardware) and workspace-driven (e.g. a collaborative, open workspace environment
for my research students and associates). A good description of my preferred workspace
design is given by Finholt and Olson in their paper on scientific collaboratories (1997).
REFERENCES
Aktan, E. A. (2003). “Distinctions between intelligent manufactured and constructed systems and a new
discipline for intelligent infrastructure hyper-systems.” Proceedings of SPIE - The International Society
for Optical Engineering, 5057, 259-266.
Andersson, M. (2004). “Swedish data for railway infrastructure maintenance and renewal cost modeling.”
Advances in Transport, Vol. 15, Computers in Railways IX, 283-292.
Chen, P.; Buchheit, Rebecca, B.; Garrett, Jr., James, H.; and McNeil, Sue. (2005). “Web-Vacuum: Web-based
environment for automated assessment of civil infrastructure data.” Journal of Computing in Civil
Engineering, 19(2), 137-147.
Clark, Robert, M. (1980). “Small water systems: Role of technology.” ASCE, Journal of the Environmental
Engineering Division, 106(1), 19-35.
Clark, Robert, M.; Goodrich, James, A.; and Lykins, Jr., Benjamin, W.. (1991). “Package plants for small water
supplies. Their role in systems expansion.” Proceedings - AWWA Annual Conference, Resources,
Engineering and Operations for the New Decade, 853-882.
Deininger, Rolf A.; and Su, Shiaw, Y. (1973). “Modelling regional waste water treatment systems.” Water
Research. 7(4), 633-646.
DiGiano, F. A.; Zhang, W.; and Travaglia, A. (2005). “Calculation of the mean residence time in distribution
systems from tracer studies and models.” Journal of Water Supply: Research and Technology—AQUA,
51(1), 1-14.
Fane, S. A.; Ashbolt, N. J.; and White, S.B. (2002). “Decentralised urban water reuse: The implications of system
scale for cost and pathogen risk.” Water Science and Technology, 46(6-7), 281-288.
Finholt, T. A., and Olson, G. M. (1997). “From laboratories to collaboratories: A new organizational form for
scientific collaboration.” Psychological Science, 8(1), 28-36.
Flamm, Kenneth. (2003). “Moore's law and the economics of semiconductor price trends.” International Journal
of Technology, Policy and Management, 3(2), 127-141.
Fraquelli, G.; and Giandrone, R. (2003). “Reforming the wastewater treatment sector in Italy: Implications of
plant size, structure, and scale economies.” Water Resources Research, 39(10), 1293[1-7].
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Katz, M. L.; and Shapiro, C. (1994). “Systems competition and network effects.” Journal of Economic
Perspectives, 8(2), 93-115.
Means III, Edward G.; Ospina, Lorena; and Patrick, Roger. (2005). “Ten primary trends and their implications
for water utilities.” Journal/American Water Works Association, 97(7), 64-77.
Weber, Walter, J., Jr. (2002). “Distributed optimal technology networks: a concept and strategy for potable
water sustainability.” Water Science and Technology. 46(6-7), 241-246.
Weber, Walter, J., Jr. (2004). “Optimal uses of advanced technologies for water and wastewater treatment in
urban environments.” Water Science and Technology: Water Supply. 4(1), 7-12.
Yao, James T. P.; and Natke, H. Guenther. (1995). “Fuzzy logic and civil infrastructure systems research.” ASCE
Structures Congress - Proceedings, Vol. 2, Restructuring: America and Beyond, 1643-1646.
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