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The objective function is set to a manufacturing cost, and the constraints are set to requirements for product performance and yield.
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The constraints were set as described above and linear programming was employed to solve the optimization problem (maximizing growth).
The objective of the model (in silico cell) was growth and the constraints were set to represent the environmental conditions and the flux through the ammonium exchange reaction was calculated using linear optimization at steady state.
The penalty to the individuals that are violating the constraint is set to a minimum fitness in this paper.
The constraint is set as w ̂ H ( n ) AF m HF m H c ( 0 ) = 1 to ensure that the filter can converge to the information of desired user.
For computability, the constraint is set at 1+ERR ≥ 0.001.
As a result, the entire flue gas stream could not be utilized in the flue gas condenser in the base cases and Y+ cases, as this would result in a temperature crossover (the constraint was set at a minimum temperature difference anywhere within the flue gas condenser of 1°C).
In this paper, the required sum SE ({{bar C}_{text {sum}}}) and the power constraints are set to make the EE maximization problem feasible.
The harmonised migration tables that used the additional constraints are set out in Tables 6 and 7.
Otherwise, the capacity constraints are set to maximal steady state value (SSU j ) calculated for the unmodified reference state.
To investigate the largest achievable rate region, the individual rate constraints are set to 0 in all strategies (R_{k}^{text {th}}=0,forall kin {1,2}).
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