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Theses Doctoral

Repurposing mass-produced internal combustion engines: Quantifying the value and use of low-cost internal combustion piston engines for modular applications in energy and chemical engineering industries

L'Heureux, Zara Elisabeth

This thesis proposes that internal combustion piston engines can help clear the way for a transformation in the energy, chemical, and refining industries that is akin to the transition computer technology experienced with the shift from large mainframes to small personal computers and large farms of individually small, modular processing units. This thesis provides a mathematical foundation, multi-dimensional optimizations, experimental results, an engine model, and a techno-economic assessment, all working towards quantifying the value of repurposing internal combustion piston engines for new applications in modular, small-scale technologies, particularly for energy and chemical engineering systems.
Many chemical engineering and power generation industries have focused on increasing individual unit sizes and centralizing production. This "bigger is better" concept makes it difficult to evolve and incorporate change. Large systems are often designed with long lifetimes, incorporate innovation slowly, and necessitate high upfront investment costs. Breaking away from this cycle is essential for promoting change, especially change happening quickly in the energy and chemical engineering industries. The ability to evolve during a system's lifetime provides a competitive advantage in a field dominated by large and often very old equipment that cannot respond to technology change.
This thesis specifically highlights the value of small, mass-manufactured internal combustion piston engines retrofitted to participate in non-automotive system designs. The applications are unconventional and stem first from the observation that, when normalized by power output, internal combustion engines are one hundred times less expensive than conventional, large power plants. This cost disparity motivated a look at scaling laws to determine if scaling across both individual unit size and number of units produced would predict the two order of magnitude difference seen here. For the first time, this thesis provides a mathematical analysis of scaling with a combination of both changing individual unit size and varying the total number of units produced. Different paths to meet a particular cumulative capacity are analyzed and show that total costs are path dependent and vary as a function of the unit size and number of units produced. The path dependence identified is fairly weak, however, and for all practical applications, the underlying scaling laws seem unaffected. This analysis continues to support the interest in pursuing designs built around small, modular infrastructure.
Building on the observation that internal combustion engines are an inexpensive power-producing unit, the first optimization in this thesis focuses on quantifying the value of engine capacity committing to deliver power in the day-ahead electricity and reserve markets, specifically based on pricing from the New York Independent System Operator (NYISO). An optimization was written in Python to determine, based on engine cost, fuel cost, engine wear, engine lifetime, and electricity prices, when and how much of an engine's power should be committed to a particular energy market. The optimization aimed to maximize profit for the engine and generator (engine genset) system acting as a price-taker. The result is an annual profit on the order of \$30 per kilowatt. The most value in the engine genset is in its commitments to the spinning reserve market, where power is often committed but not always called on to deliver. This analysis highlights the benefits of modularity in energy generation and provides one example where the system is so inexpensive and short-lived, that the optimization views the engine replacement cost as a consumable operating expense rather than a capital cost.
Having the opportunity to incorporate incremental technological improvements in a system's infrastructure throughout its lifetime allows introduction of new technology with higher efficiencies and better designs. An alternative to traditionally large infrastructure that locks in a design and today's state-of-the-art technology for the next 50 - 70 years, is a system designed to incorporate new technology in a modular fashion. The modular engine genset system used for power generation is one example of how this works in practice.
The largest single component of this thesis is modeling, designing, retrofitting, and testing a reciprocating piston engine used as a compressor. Motivated again by the low cost of an internal combustion engine, this work looks at how an engine (which is, in its conventional form, essentially a reciprocating compressor) can be cost-effectively retrofitted to perform as a small-scale gas compressor. In the laboratory, an engine compressor was built by retrofitting a one-cylinder, 79 cc engine. Various retrofitting techniques were incorporated into the system design, and the engine compressor performance was quantified in each iteration. Because the retrofitted engine is now a power consumer rather than a power-producing unit, the engine compressor is driven in the laboratory with an electric motor. Experimentally, compressed air engine exhaust (starting at elevated inlet pressures) surpassed 650 psia (about 45 bar), which makes this system very attractive for many applications in chemical engineering and refining industries. A model of the engine compressor system was written in Python and incorporates experimentally-derived parameters to quantify gas leakage, engine friction, and flow (including backflow) through valves. The model as a whole was calibrated and verified with experimental data and is used to explore engine retrofits beyond what was tested in the laboratory. Along with the experimental and modeling work, a techno-economic assessment is included to compare the engine compressor system with state-of-the-art, commercially-available compressors. Included in the financial analysis is a case study where an engine compressor system is modeled to achieve specific compression needs. The result of the assessment is that, indeed, the low engine cost, even with the necessary retrofits, provides a cost advantage over incumbent compression technologies.
Lastly, this thesis provides an algorithm and case study for another application of small-scale units in energy infrastructure, specifically in energy storage. This study focuses on quantifying the value of small-scale, onsite energy storage in shaving peak power demands. This case study focuses on university-level power demands. The analysis finds that, because peak power is so costly, even small amounts of energy storage, when dispatched optimally, can provide significant cost reductions. This provides another example of the value of small-scale implementations, particularly in energy infrastructure. While the study focuses on flywheels and batteries as the energy storage medium, engine gensets could also be used to deliver power and shave peak power demands.
The overarching goal of this thesis is to introduce small-scale, modular infrastructure, with a particular focus on the opportunity to retrofit and repurpose inexpensive, mass-manufactured internal combustion engines in new and unconventional applications. The modeling and experimental work presented in this dissertation show very compelling results for engines incorporated into both energy generation infrastructure and chemical engineering industries via compression technologies. The low engine cost provides an opportunity to add retrofits whilst remaining cost competitive with the incumbent technology. This work supports the claim that modular infrastructure, built on the indivisible unit of an internal combustion engine, can revolutionize many industries by providing a low-cost mechanism for rapid change and promoting small-scale designs.


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More About This Work

Academic Units
Earth and Environmental Engineering
Thesis Advisors
Lackner, Klaus S.
Ph.D., Columbia University
Published Here
October 24, 2017
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