scholarly journals Flexible Power & Biomass-to-Methanol plants: Design optimization and economic viability of the electrolysis integration

Fuel ◽  
2022 ◽  
Vol 310 ◽  
pp. 122113
Author(s):  
Alessandro Poluzzi ◽  
Giulio Guandalini ◽  
Simone Guffanti ◽  
Cristina Elsido ◽  
Stefania Moioli ◽  
...  
Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6797
Author(s):  
Lorafe Lozano ◽  
Edward M. Querikiol ◽  
Evelyn B. Taboada

Techno-economic viability assessments of rural electrification projects, especially those that integrate renewable energy technologies, typically look at system design optimization that would yield the most favorable cost and investment scenarios. However, the true viability of these projects relies more importantly on their impact to the rural communities while ensuring positive financial returns to the project developers. This paper aims to expand the viability assessment of electrification projects in off-grid island communities in order to mainly address the apparently opposing needs of the major stakeholders at play by developing a viability assessment framework considering the techno-economic dimensions as well as the socio-economic impacts to the consumers. The analysis follows a two-phase approach, where system design optimization and financial impact calculations are done in the first phase and the socio-economic viability is accomplished in the second phase. Results suggest that high capital investment for renewable energy has a better pay-off when there is higher demand for electricity. On the other hand, consumers also tend to receive higher economic benefit as they consume more electricity. However, the low income of rural consumers strains their capacity to pay, which necessitates their engagement in more economically-productive uses of electricity. The viability assessment framework can be a useful tool for both investors and consumers as this provides important insights which can be translated into impactful interventions that may include government support through improved policy implementation that can positively sustain electricity access in off-grid communities through renewable energy.


2017 ◽  
Vol 45 (1) ◽  
pp. 71-84 ◽  
Author(s):  
Alexey Mazin ◽  
Alexander Kapustin ◽  
Mikhail Soloviev ◽  
Alexander Karanets

ABSTRACT Numerical simulation based on finite element analysis is now widely used during the design optimization of tires, thereby drastically reducing the time investment in the design process and improving tire performance because it is obtained from the optimized solution. Rubber material models that are used in numerical calculations of stress–strain distributions are nonlinear and may include several parameters. The relations of these parameters with rubber formulations are usually unknown, so the designer has no information on whether the optimal set of parameters is reachable by the rubber technological possibilities. The aim of this work was to develop such relations. The most common approach to derive the equation of the state of rubber is based on the expansion of the strain energy in a series of invariants of the strain tensor. Here, we show that this approach has several drawbacks, one of which is problems that arise when trying to build on its basis the quantitative relations between the rubber composition and its properties. An alternative is to use a series expansion in orthogonal functions, thereby ensuring the linear independence of the coefficients of elasticity in evaluation of the experimental data and the possibility of constructing continuous maps of “the composition to the property.” In the case of orthogonal Legendre polynomials, the technique for constructing such maps is considered, and a set of empirical functions is proposed to adequately describe the dependence of the parameters of nonlinear elastic properties of general-purpose rubbers on the content of the main ingredients. The calculated sets of parameters were used in numerical tire simulations including static loading, footprint analysis, braking/acceleration, and cornering and also in design optimization procedures.


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