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The role of optimization in future infrastructure investments
I was amazed when a professor in my undergrad program likened traffic flow to hydrodynamic systems. Specifically, traffic flow was first simulated by applying a kinematic wave function taken from fluid dynamics to describe how changes in flow are propagated through a stream of vehicles.
Another similarity between traffic flow and hydraulic behavior is capacity. A report by consulting firm KPMG highlights how the use of self-driving cars has the potential to reduce traffic jams by increasing traffic flow capacity by 500%. This illustrates the wasted capacity on our roadways to carry traffic. Instead of simply building more roads or adding lanes to existing roads, using self-driving cars will eliminate the inefficiency we bring to the table when trying to drive from here to there.
Our water infrastructure behaves similarly. We have inefficient wastewater collection systems, passive storm water management treatment approaches and drinking water systems that typically lose 30% of potable water in distribution networks. In an age where public funds are limited and there is a need to capture additional capacity for water treatment, collection and distribution systems in a cost-efficient manner, why not first see what we can get out of our existing infrastructure through optimization?
Water technology optimization approaches seek to meet these needs. The word “optimization” is used in many contexts. In operations or capital planning, we are referring to mathematical optimization techniques developed from the field of operations research, such as linear programming, dynamic programming and genetic algorithms. These methods have been used for decades and are now reaching the water sector.
Among the companies using these techniques to solve water infrastructure problems are EmNet, Opti and Optimatics.
EmNet provides real-time intelligence and optimization to sewer networks. The system seeks to reduce and eliminate sewer overflows by monitoring complex systems, analyzing the alternatives and integrating control systems.
While this approach generally optimizes belowground pipe systems, Opti provides real-time control for distributed storm water infrastructure, green infrastructure and rainwater harvesting. It integrates cloud services with sensors and valves to remotely control and optimize operations.
Optimatics offers a suite of solutions, but is known for optimizing capital planning. The software evaluates a pipe network in conjunction with a hydraulic model, GIS, asset condition and costs, and it uses cloud-based processing to provide choices to reduce a utility’s capital improvement program while meeting water quality and quantity constraints.
As implementation of optimization tools expands, one might assume software is taking the place of engineering expertise. Quite the contrary—these methods require valuable input from engineers, scientists, operators, asset managers and utility professionals. Optimization techniques can leverage that knowledge to achieve more efficient, effective and resilient operations and planning.
A dry detention pond in Prince George’s County, Md., provides static water quality and quantity treatment to a dynamically controlled and operated system that provides enhanced treatment capacity. This project added 2 acre-ft of water quality treatment volume with a residence time of 48 hours, independent of the size or scale of the storm. This installation improved treatment by using the full capacity of the infrastructure at a reduced cost. The going rate for storm water retrofits in urban settings is $150,000 per acre, and up to $350,000 per acre in some jurisdictions. The Opti system treats 19 acres of impervious cover at a cost of $26,000, or $1,400 per impervious acre. Getting the most out of existing infrastructure and providing cost-effective solutions is how we start to remake our aging infrastructure.
In another example, the city of South Bend, Ind., eliminated more than 1 billion gal of overflow. From 2006 to 2008, the city averaged 1.87 billion gal of overflows per year. When a real-time monitoring system was put in place, overflows dropped by nearly half, to an average of 964 million gal per year from 2009 to 2011. When real-time control was implemented in 2011, average annual overflows reduced another half, to 523 million gal from 2012 to 2014. The EmNet system saved $1.5 million per year in operations by optimizing 145 sensor nodes, nine valves, three control gates and six rain gauges.
San Diego saved almost $20 million by optimizing the replacement of a 70-year-old transmission main. The estimated cost to replace the existing pipeline with a pipe of the same diameter was $55 million. By combining condition assessment information with failure risk analysis and capital costing, the optimized solution consisted of relining 2 miles of pipe, replacing 16 miles, abandoning 8 miles and constructing 5 miles of new pipe. This program’s estimated cost is $35 million. Optimatics solutions reduced the number of oversized pipes while improving fire flow capacity.
These are a few examples where the full potential of existing infrastructure systems were not being realized. In our current climate of limited budgets and increasing demands on water infrastructure, we cannot waste this extra capacity. Rate- and taxpayers expect this. Optimization in engineering infrastructure may seem to fly in the face of safety factors and other parameters that account for uncertainties. However, our advanced computational power has reduced, but not eliminated these uncertainties, and has unlocked the ability to squeeze every ounce of utility out of our infrastructure while also providing safety.
The basic engineering approach is to gather data, analyze, design, construct, measure performance and redesign for improved performance. This cycle, baked into the engineering method, has provided for continued enhancements in engineered products and systems. Infrastructure is no different here. While using optimization to improve infrastructure performance may seem to break from the status quo, it really is just a further advancement that has led us from the horse-and-buggy to self-driving cars. This same advancement must occur in water infrastructure as well.
This column included input from Barry Liner, P.E., Ph.D., BCEE. To learn more about the basics of optimization, I suggest reading “H2Optimization,” by Liner and Holder Maier.