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At the next time step, cost function is optimized utilizing the MILP model based on updated input parameters, including real-time price (RTP), newly forecast ambient temperature, updated future wind power output, previous step generated data T r, etc., and generates a new set of parameters for next control window.
The solutions were locally optimized utilizing the Matlab unconstrained minimizer fminsearch based on the Nelder-Mead simplex method.
The effective parameters on the extraction recovery were studied and optimized utilizing two decent optimization methods; factorial design and central composite design (CCD).
The topologies of the truss-structure were optimized first from a given ground structure employing the modified binary particle swarm optimization (BPSO), and subsequently the size and shape of members were optimized utilizing the attractive and repulsive particle swarm optimization (ARPSO).
The geometries of the combination of carbon nanotube with an MTX in different solvent are optimized utilizing the Onsager pattern at HF level of theory.
Factors affecting the extraction process, such as type and volume of extraction and dispersive solvent, extraction time and centrifugation step were investigated thoroughly and optimized utilizing factorial design.
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Resource usage is optimized by utilizing both the recursive even odd decomposition and distributed arithmetic techniques.
Furthermore, by analyzing the effects of material properties, limitations of the common air-filled cavities are pointed out, and an optimized setup utilizing non-standard materials is proposed.
Therefore, the optimized parameters utilized were 308 K, 8.0 MPa, and 0.30 g mL-1.
The condensation and pre-heating process is optimized to utilize the maximum waste heat recovery.
The positive results of the biochemical assay suggest that these compounds may be further optimized and utilized for the treatment of diabetic retinopathy.
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