Rate constants for DOC oxidation and carbonate mineral precipitation were determined, and other model parameters were optimized using the nonlinear optimization program PEST by means of matching hydrochemical observations closely (pH, DIC, DOC, Na, K, Ca, Mg, NH4, Fe(II), SO4, Cl, CH4, saturation index of calcite and siderite).
Reads were assembled using Velvet (Zerbino and Birney 2008), parameters were optimized using the Velvet-optimizer script [64].
In the optimization process, the controlling parameters are optimized using the linear least square method that is embedded into the optimization algorithm for geometric parameter design variables.
To place the learning techniques on a more even playing field, the parameters were optimized using the PSBC feature set, but it is likely that other optimal parameters could be found in the scenarios of additional or other features.
Five design parameters are optimized using the software GENOPT®.
Engine-operating parameters were optimized using the Desirability approach of RSM.
Certain important parameters are optimized using the coupling mode theory and Beam Propagation Method.
After this, the MEPS experimental parameters were optimized using the Si-G sorbent.
Experimental parameters were optimized using the one-factor-at-a-time approach and were followed using an orthogonal array design.
A series of extraction parameters were optimized using the response surface method based on central composite design.
The significant parameters of the extraction have been found using chemometric procedures and these parameters were optimized using the central composite design (CCD) for two solvents.
