Heavy Fuel Oil (HFO)

Fritt-Rasmussen, Janne; Wegeberg, Susse; Gustavson, Kim; Sørheim, Kristin Rist; Daling, Per S.; Jørgensen, Kirsten S.; Tonteri, Ossi; Holst-Andersen, Jens Peter

doi:10.6027/tn2018-549

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…The chemical and physical properties of HFO depend not only on its origin, quality, and properties but also on the distillate variations added during the viscosity adjustments in the refinery process [4] . Based on the concentration of sulfur (S), HFO is classified as high‐, medium‐ or low‐S fuel oil with typical chemical composition shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Characterization, Processing, and Application of Heavy Fuel Oil Ash, an Industrial Waste Material – A Review

Basha

Aziz

Maslehuddin

et al. 2020

The Chemical Record

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Heavy fuel oil ash (HFOA) is generated as an industrial waste material during the combustion of heavy fuel oil in power/desalination plants. With increasing energy demands, a significant volume of HFOA is generated. It is generally disposed of in landfills, causing environmental pollution, as it contains several toxic elements. Recently, efforts were made towards developing strategies for reusing industrial waste materials and creating value-added products from the waste materials. Despite significant information available in the literature on the utilization of HFOA, there is still a need for a thorough and systematic review on the characterization and utilization of HFOA in various applications. Consequently, this paper aims to present a critical review of the literature on HFOA generation, its chemical composition, physical properties, morphology, and applications. It is encouraging to note that HFOA has been used in several potential applications, such as the preparation of activated carbon and carbon nanotubes, metal recovery, environmental pollutant removal, polymer composites and construction materials, etc. However, the development of several value-added materials utilizing HFOA and its applications in other areas such as coatings, cathodic protection systems, and phase change materialswould emerge as a new topic of research. It is expected that this review will act as a precursor for further research on the use of HFOA in industrial applications. Since the use of HFOA will lead to environmental, economic, and technical benefits, research in the utilization of this industrial waste material is highly recommended.

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Section: Introductionmentioning

confidence: 99%

Characterization, Processing, and Application of Heavy Fuel Oil Ash, an Industrial Waste Material – A Review

Basha

Aziz

Maslehuddin

et al. 2020

The Chemical Record

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“…Algorithms 2023, 15, x FOR PEER REVIEW 14 of 17 units with a high ramp capacity contribute less in terms of GHG emissions than they do in terms of energy consumption. They offer cleaner conversion with poor combustion indices [38]. Table 4 lists the CO2 emission coefficients of the assessed generators in relation to their fuel type [29].…”

Section: Prospects For Real-world Conditionsmentioning

confidence: 99%

Variational Bayes to Accelerate the Lagrange Multipliers towards the Dual Optimization of Reliability and Cost in Renewable Energy Systems

Nikolaidis

2022

Algorithms

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Renewable energy sources are constantly increasing in the modern power systems. Due to their intermittent and uncertain potential, increased spinning reserve requirements are needed to conserve the reliability. On the other hand, each action towards efficiency improvement and cost reduction contradicts the participation of variable resources in the energy mix, requiring more accurate tools for optimal unit commitment. By increasing the renewable contribution, not only does the overall system inertia decrease with the decreasing conventional generation, but more generators that are expensive are also introduced. This work provides a radically different approach towards a tractable optimization task based on the framework of Lagrange relaxation and variational Bayes. Following a dual formulation of reliability and cost, the Lagrange multipliers are accelerated via a machine learning mechanism, namely, variational Bayesian inference. The novelty in the proposed approach stems from the employed acquisition function and the effect of the Gaussian process. The obtained results show great improvements compared with the Lagrange relaxation alternative, which can reach over USD 1 M in production cost credits at the least number of function evaluations. The proposed hybrid method promises global solutions relying on a proper acquisition function that is able to move towards regions with minimum objective value and maximum uncertainty.

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“…These features also cause HFO to persist longer in the marine environment and travel further. The thickness of HFO slicks range from 0.5 to 1.0 cm, in contrast to crude oil, which can vary from 1 to 5 mm (Fritt-Rasmussen et al 2018). (The lighterweight and more volatile components of crude oil have slick thicknesses of <1 mm).…”

Section: Differences Among Crude Oil Hfo and Alternative Fuelsmentioning

confidence: 99%

“…Studies examining the effectiveness of spill recovery technologies and methods have shown that mechanical recovery mechanisms (booms) are the most effective means of containment (Fritt-Rasmussen et al 2018). In situ burning is not effective because of the lack of light (volatile and flammable) hydrocarbon compounds and the suspension of much of the HFO mass in the water column.…”

Section: Differences Among Crude Oil Hfo and Alternative Fuelsmentioning

confidence: 99%

Spill Behavior, Detection, and Mitigation for Emerging Nontraditional Marine Fuels

Kass,

Sluder,

Kaul

2021

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Heavy Fuel Oil (HFO)

Cited by 12 publications

References 27 publications

Characterization, Processing, and Application of Heavy Fuel Oil Ash, an Industrial Waste Material – A Review

Characterization, Processing, and Application of Heavy Fuel Oil Ash, an Industrial Waste Material – A Review

Variational Bayes to Accelerate the Lagrange Multipliers towards the Dual Optimization of Reliability and Cost in Renewable Energy Systems

Spill Behavior, Detection, and Mitigation for Emerging Nontraditional Marine Fuels

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