At present, the synthesis of ammonia through the Haber−Bosch (HB) process accounts for 1.2% of the global carbon emissions, representing roughly one-fourth of the global fossil consumption from the chemical industry, which creates a pressing need for alternative low-carbon synthesis routes. Analyzing seven essential planetary boundaries (PBs) for the safe operation of our planet, we find that the standard HB process is unsustainable as it vastly transgresses the climate change PB. In order to identify more responsible strategies from this integrated perspective, we assess the absolute sustainability level of 34 alternative routes where hydrogen (H 2 ) is supplied by steam methane reforming with carbon capture and storage, biomass gasification, or water electrolysis powered by various energy sources. We found that some of these scenarios could substantially reduce the global impact of fossil HB, yet alleviating the impact on climate change could critically exacerbate the impacts on other Earth-system processes. Furthermore, we identify that reducing the cost of electrolytic H 2 is the main avenue toward the economic appeal of the most sustainable routes. Our work highlights the need to embrace global impacts beyond climate change in the assessment of decarbonization routes of fossil chemicals. This approach enabled us to identify more suitable alternatives and associated challenges toward environmental and economically attractive ammonia synthesis.
Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledge or black-box data-driven models based on patterns observed in data. However, in the past twodecade, hybrid modeling that explores the synergy between the two paradigms has emerged as a pragmatic compromise. The data-driven part of these have been largely based on conventional machine learning algorithm (e.g., artificial neural network, support vector regression), which prevents interpretability of the finally learnt model by the domain-experts.In this work we present a novel hybrid modeling framework, the Functional-Hybrid model, that uses the ranked domain-specific functional beliefs together with symbolic regression to develop dynamic models. We demonstrate the successful implementation of these hybrid models for four benchmark systems and a microbial fermentation reactor, all of which are systems of (bio)chemical relevance. We also demonstrate that compared to a similar implementation with the conventional ANN, the performance of Functional-Hybrid model is at least two times better in interpolation and extrapolation. Additionally, the proposed framework can learn the dynamics in 50% lower number of experiments. This improved performance can be attributed to the structure imposed by the functional transformations introduced in the Functional-Hybrid model.
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