Abstract:Techno-economic assessments (TEA) of biodiesel production may comply with various economic and technical uncertainties during the lifespan of the project, resulting in the variation of many parameters associated with biodiesel production, including price of biodiesel, feedstock price, and rate of interest. Engineers may only collect very limited information on these uncertain parameters such as their variation intervals with lower and upper bound. This paper proposes a novel non-probabilistic strategy for uncertainty analysis (UA) in the TEA of biodiesel production with interval parameters, and non-probabilistic reliability index (NPRI) is employed to measure the economically feasible extent of biodiesel production. A sensitivity analysis (SA) indicator is proposed to assess the sensitivity of NPRI with regard to an individual uncertain interval parameter. The optimization method is utilized to solve NPRI and SA. Results show that NPRI in the focused biodiesel production of interest is 0.1211, and price of biodiesel, price of feedstock, and cost of operating can considerably affect TEA of biodiesel production.
A numerical model is given to identify equivalent parameters of composite materials, using BP neural network algorithm. Taking Filament-wound composite pressure vessels as the research object, finite element models are first constructed .Getting node displacements as network training samples, the mechanical parameters as output information of network for effective training, the equivalent material parameters can be obtained. The satisfactory numerical validation is given and results show that the proposed method can identify the equivalent modulus and the equivalent Poisson’s ratio of the Filament-wound composite pressure vessels with precision. The computational efficiency is improved with BP neural network.
In order to determine thermal parameters of mass concrete, this paper utilizes homotopy technique to solve an inverse transient heat conduction problem with multi-variables. A finite element model facilitating to sensitivity analysis for non-linear direct and inverse problems is derived, and a precise algorithm of time stepping is used in the transient analysis. Numerical validation has been given with an investigation of effects of noise data and initial guess on the results.
This paper presents a general numerical model to solve non-linear inverse heat conduction problems with multi-variables which include thermal parameters and boundary conditions, and can be identified singly or simultaneously. The direct problems are numerically modeled via FEM, facilitating to sensitivity analysis that is required in solving inverse problems via a least-square based CGM (Conjugate Gradient Method). Inhomogeneous distribution of parameters is considered, and a number of numerical examples are given to illustrate the work proposed.
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