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Schematic of ammonia plant. 

Schematic of ammonia plant. 

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The aim of this work is to perform a retrofit study of an ammonia plant, in purpose of improving energy efficiency. As a common practice, one can divide an ammonia plant into two parts: the hot-end and the cold-end. In the hot section, two different options are investigated that both lead to a threshold condition and achieve maximum energy saving....

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... The summary of energy targeting at optimum ΔT min is shown in Table 6. As shown, the absence of hot utility confirms that the problem is Threshold [39]. Fig. 7 shows composite curves at ΔT Threshold = 11.9°C, ...
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... Many researchers have used these curves for energy studies [1]. Panjeshahi et al. (2008) divided an ammonia plant into two sections of the hot-end and the cold-end and presented some recommendations to improve energy efficiency. The cold-end section is mainly the ammonia synthesis and refrigeration cycles. ...
... The cold-end section is mainly the ammonia synthesis and refrigeration cycles. They used the EGCC diagram to minimize exergy destruction and losses and compressors' power demands in the cold-section of an industrial ammonia plant by suggesting new refrigeration levels [2]. Ataei (2011) used the EGCC diagram to correct the temperature levels of refrigeration cycles of an olefin plant in order to reduce exergy destruction and losses and compressors' power consumption. ...
... Thus, the thermal exergy destruction and losses within a HEN can readily be calculated and also needed modifications to increase the efficiency of refrigeration systems, can graphically be recommended. To investigate the accuracy of the new method, they studied a PRICO process and an ammonia refrigeration cycle (Panjeshahi et al., 2008) [12]. Safder Tables (ChExCT) as a numerical tool were introduced to achieve maximum waste energy recovery. ...
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... Improved and novel methods of ammonia synthesis have seen great interest, aiming at transitioning away from the massive energetic footprint of the Haber-Bosch process. This can include better energy management of the production process through optimized heat recovery and energy efficiency, which could potentially decrease energy requirements by up to 15% (Panjeshahi et al., 2008), and transitioning towards greener energy sources, and replacing the methane used by the process with renewable sources such as syngas or biogas from biomass gasification or anaerobic digestion, or even hydrogen, further reducing the environmental footprint of the process (Arora, 2017; Tunå et al., 2014). Research has also been investigating alternative ammonia synthesis processes, with notable interest surrounding photocatalytic, electrocatalytic, and plasma-assisted processes (Hao et al., 2020;Peng et al., 2018;Soloveichik, 2019). ...
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... Unreacted nitrogen from the second step is carried through the entire ammonia synthesis plant and needs to be balanced for the stoichiometric requirements of the Haber Bosch reaction, R1. Since its introduction, efforts have been made to improve catalyst selectivity, reduce operating pressure and optimize heat integration of the Haber Bosch process [10][11][12]. Modern optimized Haber Bosch plants still require high pressure (>20.3 MPa) to shift the equilibrium of R1 forward, which accounts for 10-16% of the process energy demand [13]. ...
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... Regarding production of nutrients, the main source of emissions is the high energy requirement for ammonia synthesis and the energy required for P and K production. This could potentially be reduced by a variety of ways, such as through improving heat transfer and energy efficiency (Fló rez-Orrego and de Oliveira Junior, 2017;Panjeshahi et al., 2008;Rafiqul et al., 2005), using alternative, non-fossil, energy sources (Arora, 2017;Tallaksen et al., 2015;Tock et al., 2015;Tunå et al., 2014), or even transitioning towards emerging photocatalytic and electrocatalytic ammonia synthesis processes Michalsky et al., 2012;Montoya et al., 2015;Zhou et al., 2017). ...
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Fertilizers have become an essential part of our global food supply chain and are necessary to sustain our growing population. However, fertilizers can also contribute to greenhouse gas (GHG) emissions, along with other potential nutrient losses in the environment, e.g. through leaching. To reduce this environmental impact, tools such as life cycle assessments and decision support systems are being used to aid in selecting sustainable fertilization scenarios. These scenarios often include organic waste-derived amendments, such as manures, composts and digestates. To produce an accurate assessment and comparison of potential fertilization scenarios, these tools require emission factors (EFs) that are used to estimate GHG emissions and that are an integral part of these analyses. However, such EFs seem to be very variable in nature, thereby often resulting in high uncertainty on the outcomes of the analyses. This review aims to identify ranges and sources of variability in EFs to provide a better understanding of the potential uncertainty on the outcomes, as well as to provide recommendations for selecting EFs for future studies. As such, an extensive review of the literature on GHG emissions from production, storage, transportation and application of synthetic fertilizers (N, P, K), composts, digestates and manures was performed. This paper highlights the high variability that is present in emissions data and confirms the great impact of this uncertainty on the quality and validity of GHG predictions related to fertilizers. Variability in EFs stem from the energy source used for production, operating conditions, storage systems, crop and soil type, soil nutrient content, amount and method of fertilizer application, soil bacterial community, irrigation method, among others. Furthermore, a knowledge gap exists related to EFs for potassium fertilizers and waste valorization (anaerobic digestion/composting) processes. Overall, based on this review, it is recommended to determine EFs on a case by case basis when possible and to use uncertainty analyses as a tool to better understand the impact of EF variability.