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The optimal topological structure, position of central processing facility, diameter and route of each pipeline are obtained integrally by solving this model with GUROBI solver.
Figure 8 has been obtained by solving this model, and shows the range of values of deposition velocity that can be expected under different flow conditions and for different diameters.
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The electrical field, the flow field, and the concentration field during dispensing processes were obtained by solving this theoretical model numerically.
By solving this non-linear programming (NLP) model, whose objective is to minimize the annual cost, an optimal cogeneration system can be obtained.
This model is treated as an extensive maximal flow problem, and an efficient step-by-step algorithm to solve this model is developed.
We will define the following noise models The free-energy expression for our specific model is given by In order to solve this model, we will parameterize the covariance models such that In the following methods section, we will describe the details of these covariance components.
It also establishes a mathematical model of the consistency coordination control between the VPP and STATCOM agents, and solve this model by the bilinear matrix inequality (BMI) constraints feasible solution.
A detailed scheduling of oil depots along product pipeline can be obtained by solving the model.
By solving the model, a 175 h detailed scheduling plan is given.
Dynamic simulations of the integrated econometric emission model were conducted by solving the model forward over the period from 2001 to 2050.
By solving the model for various values of ε, the approximated Pareto frontier is constructed.
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