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We present a heuristic rule-based method for the design of distributed wastewater treatment networks of multiple contaminants with maximum inlet concentration constraints, based on the insight that one of the important essences of distributed wastewater system integration is to reduce the total treatment flow rate by minimizing unnecessary mixing amount of streams.
The problem is first converted into a single objective optimization based on the min-max algorithm, then into the quasi-unconstrained optimization problem with only one dual variable and its non-negative constraints based on the envelope dual theory.
While there have been occasional papers that have shown a large tail, and some arguments that this is stubborn – particular from constraints based on the modern tranisent changes – there has always been substantial evidence to rule these out.
Multiple levels of constraints based on the energy index are established for the island partition model.
So the estimated slip distribution satisfies the constraints based on the ocean-bottom pressure-gauge records.
During query evaluation, GRIN derives a set of inequality constraints based on the query graph structure.
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In the following, we formulate the constraints based on these binary variables to control the completion of a model, that is, to formulate a MILP that performs multiple gap-filling.
The early work presented in [13] stated the need for an extra constraint to compute the optical flow field from an image sequence and proposed one ad hoc constraint based on the assumption of flow smoothness.
The selection of such species is subject to the stoichiometric constraint based on the atomic balance.
Second, a novel constraint based on the vibrato effect will be investigated to extend this method to singing-voice signals.
In this work, we discuss the issue of feature selection under the monotonicity constraint based on the principle of large margin.
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