Synthetic ammonia produced from fossil fuels is essential for agriculture. However, the emissions-intensive nature of the Haber-Bosch process, as well as a depleting supply of these fossil fuels have motivated the production of ammonia using renewable sources of energy. Small-scale, distributed processes may better enable the use of renewables, but also result in a loss of economies of scale, so the high capital cost of the Haber-Bosch process may inhibit this paradigm shift. A process that operates at lower pressure and uses absorption rather than condensation to remove ammonia from unreacted nitrogen and hydrogen has been proposed as an alternative. In this work, a dynamic model of this absorbent-enhanced process is proposed and implemented in gPROMS ModelBuilder. This dynamic model is used to determine optimal designs of this process that minimize the 20-year net present cost at small scales of 100 kg/h to 10,000 kg/h when powered by wind energy. The capital cost of this process scales with a 0.77 capacity exponent, and at production scales below 6075 kg/h, it is less expensive than the conventional Haber-Bosch process.
The time-varying operation of chemical plants offers economic advantages, particularly in the presence of time-sensitive electricity markets and renewable energy generation. However, the uncertainty and short time scale variability associated with renewable energy production, as well as the nonconvex process and cost models typically associated with chemical processes, make finding the optimal design of such systems challenging. In this work, we present a new approach to finding the optimal design of systems with time-varying operation, called scheduling-informed design, whereby we determine the optimal operation of many designs and embed the resulting cost correlations into the optimal design problem. We apply this method to a case study of wind-powered ammonia generation and show that it greatly improves the computational tractability of the optimal design problem and predicts with greater accuracy operating costs realized due to uncertainty in forecasting.
Small-scale renewable-powered
ammonia production is more sustainable
than the current fossil fuel- and energy-intensive method. The development
of lower capital cost absorbent-enhanced ammonia synthesis as well
as the concept of modular chemical processes may improve the economic
feasibility of such a paradigm. This possibility is investigated through
an ammonia supply chain optimization study wherein modular, wind-powered
ammonia production based on this new technology can be added to the
existing infrastructure. The benefit of modularity is captured via
a mass production exponent which reduces per-module capital cost as
more are constructed. Case studies for Minnesota and Iowa show first
adoption of 8760 t/y modules at conventional ammonia prices of $610/t
and $574/t, respectively, which are considerably lower than those
required for incorporation of scaled-down Haber–Bosch. For
mass production exponents of 0.9 and less, modular production results
in lower supply chain cost and more renewable incorporation than its
continuous counterpart.
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