Energy Systems Engineering

Abstract Meeting current and future demands for energy is often quoted as the most pressing problem facing humanity in the 21st century. This problem has to be addressed in a global environment where resources are constrained and diminishing, while at the same time raising standards of living for a growing population and preventing catastrophic climate change. Recent experience has shown that energy production is increasingly competing with other demands on the same resources, such as commodity chemicals production and food production, and all compete for water as a primary resource. In moving to a sustainable future, radical changes will have to take place in how we produce, transport and utilize energy and commodity chemicals. The solutions will be many and diverse, and highly dependent on geographic location and patterns of consumption. In many respects, chemical engineers, and process systems engineers in particular, are uniquely qualified to address this grand challenge. The design, optimization and operation of a diverse range of novel, highly integrated processes and supply chains is our core competence. Furthermore, process systems engineering tools, techniques and formulations are highly developed and ready to meet this challenge, so the creative application of the best of these will have a tremendous impact. On the other hand, new challenges and applications also spawn new formulations and tools. The purpose of this talk is to use several examples from ongoing projects in the Process Systems Engineering Laboratory at MIT to illustrate how the energy systems engineering challenge is fostering both the creative application of the state-of-the-art and new formulations and tools. Gasification of low-value carbonaceous feedstocks (e.g., coal, lignocellulosic biomass, oil shale, etc.) is a promising route to the polygeneration of electrical power, transportation fuels and chemicals. In order to reduce or even reverse carbon dioxide emissions to the atmosphere, novel processes have to be invented with integrated carbon capture and sequestration. We have recently used PSE tools to invent a novel high-efficiency process for power production from coal that almost eliminates the efficiency penalty of carbon capture and sequestration, and dramatically reduces the water consumption of the process. This process should be particularly attractive in water-stressed regions with large coal reserves, such as Northern China and the American West. The notion of polygeneration enables a plant to exploit fluctuations in prices and demands for several products (e.g., electrical power, transportation fuels) on several time scales. However, in this setting it is no longer optimal to design for a single steady state. Therefore, we are developing novel optimization formulations and algorithms for integrated design and operation that yield optimal profitability over a range of price and demand scenarios. The productivity of transportation fuels and chemicals from the aforementioned processes can be significantly enhanced by the introduction of a carbon-free hydrogen source, specifically water splitting using heat and/or electrical power from nuclear and solar sources. Moreover, large amounts of carbon dioxide are produced from current and future hydrogen demands. One of the most promising processes for the production of hydrogen using heat from a nuclear reactor is the sulfur-iodine (SI) thermo-chemical cycle. At the heart of this process is the Bunsen reactor, in which water, sulfur dioxide and iodine are combined to form sulfuric acid and hydrogen iodide. Due to a fortuitous liquid-liquid phase split, the sulfuric acid and hydrogen iodide can be separated and thermally decomposed to generate oxygen and hydrogen, respectively. The key to designing and optimizing this process is accurate thermophysical property models that can predict phase and chemical equilibrium of multi-electrolyte, multi-solvent solutions. This challenge has prompted us to develop a new version of the electrolyte-NRTL model suitable for such solutions, and large-scale optimization techniques to develop self-consistent thermodynamic databases for parameter estimation. In addition, the challenge of fitting binary parameters to such complex phase diagrams required us to propose a new formulation for the parameter estimation problem as a bilevel program; the inner programs determine the stable phase splits by Gibbs free energy minimization, and the outer program minimizes the least squares error. Solution of this formulation requires our recent developments in algorithms for the solution of bilevel programs with nonconvex inner programs. Moreover, this advance has very broad implications for how binary parameters should be fitted to any phase equilibrium data. The emergence of a global liquefied natural gas (LNG) market is facilitating the development of novel supply chains that enable the exploitation of remote natural gas combined with carbon capture, sequestration and enhanced oil recovery. The implementation of these supply chains require novel process designs at several stages in the overall supply chain, and a common feature of these processes is the need for heat and power integration between streams at varying temperatures and pressures. This problem is considerably more complex than conventional heat integration, and leads to novel nonconvex optimization formulations that vary temperature and pressure levels simultaneously to find the optimal process design. These formulations have prompted us to develop the novel notion of global optimization of algorithms, in which the process model is evaluated as an algorithm, and the algorithm is relaxed directly in the global optimization procedure. Such advances are making these complex problems tractable within a deterministic global optimization framework. The quest for new sources of oil to meet ever-increasing demand is motivating the development of technologies for oil exploration and production in ultra-deep water (depths of one mile or more). At such depths of water, production facilities have to be located on the seabed and operated remotely. Due to the extreme costs associated with maintenance and repair of subsea production processes, an autonomous process must be designed that is guaranteed to be robust to input disturbances. These guarantees must be provided a priori at the design stage, which is motivating the development new formulations and optimization algorithms for robust process design. Finally, the design and operation of processes for the production of transportation fuels from biomass via fermentation is motivating new dynamic optimization formulations and algorithms. The bioreactor can be modeled using conventional differential equation formulations, but this must be coupled with predictive models of the response of the microorganism(s) to their environment. A common approach to this is a flux balance model that represents the behavior of a microorganism as a linear program (LP); increasingly, large-scale flux balance models are being constructed from genomic data. The overall model is a system of differential equations with LPs embedded, which can be reformulated as an equivalent complementarity system, a type of hybrid (discrete/continuous) system. This motivates the development of novel simulation and optimization algorithms for such complementarity systems, in particular drawing on ideas and techniques from nonsmooth analysis.