## 7 Economic Assessment and Future Perspectives of Hydrogen and
### Ammonia Production
* An economic assessment of hydrogen technologies participating in California electricity markets found that hydrogen production can be cost-competitive with other energy sources, such as natural gas, in certain markets and applications [126]. The IEA's Ammonia Technology Roadmap, published in 2021, explores three possible futures for ammonia production and aims to reduce emissions from the industry. The report highlights that the world will need more ammonia with fewer emissions in the future, as an increasingly larger and affluent global population will lead to growth in ammonia demand. The report also notes that ammonia production accounts for around 2% of total final energy consumption and 1.3% of CO2 emissions from the energy system [127; 128]. Recent studies have evaluated the feasibility of producing green ammonia, which is produced using renewable energy sources, and blue ammonia, which is produced using fossil fuels with carbon capture and storage [129; 130] A techno-economic evaluation of ammonia production via integrated biomass gasification in an existing pulp and paper mill was also conducted [131]. These assessments have shown that green ammonia production can be cost-competitive with fossil-fuel-based ammonia production under certain conditions. In a roadmap to the ammonia economy published in 2020, the authors envisage renewable ammonia being produced in the future at a scale that is significant in terms of global fossil fuel use [132]. One of the challenges in producing green hydrogen and ammonia is optimizing the production process to maximize efficiency and reduce costs. This is where AI and AM are used. Artificial intelligence (AI), a subset of computer science combining advanced computing and statistics, is the ability of a machine to receive inputs and produce a behavior or reaction similar to that of an intelligent person. While humans have natural cognitive skills and learn through experience, artificial intelligence (AI) is based on data and algorithms [133]. Figure 25 shows the input parameters needed to produce hydrogen using artificial intelligence [134]. AI solutions are designed to enhance rather than replace human decision-making. AI may increase the quantity and quality of information accessible to human decision-makers by analyzing massive volumes of data and delivering insights and recommendations, ultimately leading to better and more informed decisions. Artificial intelligence (AI) tools are increasingly being recognized as a promising approach for modeling and optimizing bioprocesses. These tools have the potential to significantly improve the design and operation of production equipment, as well as predict and prevent equipment failures [135]. Figure 26 presents several AI technologies that have been used in these systems [136; 137]. Response surface methodology (RSM) and artificial neural networks (ANNs) have demonstrated their superior predictive accuracy in modeling complex nonlinear bioprocesses, making them more efficient tools for optimizing the design and operation of production equipment. Depending on the manufacturing system, AM may signify a variety of things. can be used to manufacture complex components with higher precision and less waste, reducing material costs and increasing production efficiency [138]. Recent progress in artificial intelligence (AI) and additive manufacturing (AM) has the potential to completely transform the way green hydrogen and ammonia are produced. Figure 25: Water splitting via artificial intelligence technology [134].