Less than a handful of business process software vendors offer optimization to supplement their simulation capability. However, the need for optimization of simulation models arises when the process analyst wants to find a set of model specifications (i.e., input parameters and/or structural assumptions) that leads to optimal performance. On one hand, the range of parameter values and the numbe...

Wire electrical discharge machining (WEDM) is an unconventional process that being extensively used in the aerospace,medical devices, die, tooling, and automotive industries for high-hardness materials with conductivity. In present work, WEDM of aluminium 6082 alloys was carried out since it influences diversified applications manufacturing industries. The includes extensive number variables in...

The paper is concerned with on-line process steady-state optimization under uncertainty. In such cases a single process model optimization can yield a set-point far away from the one optimal for the true process. The way to improve the set-point is to apply steady-state feedback, i.e., an iterative optimizing algorithm utilizing new measurements available after every subsequent set-point applic...

intelligent wells provide the ability for monitoring and control of downhole environment of the wells. downhole monitoring is achieved through sensors while control is realized with downhole valves. recovery from intelligent wells can be improved by proper selection of candidate wells/fields and optimizing the number, location and performance of the installed interval control valves. design cri...

in this study, hydrus2d/3d software was used to estimate the hydraulic parameters of van genuchten-mualem model via inverse modeling using double-ring infiltrometers data in 3 different soil textures. nine scenarios of inverse modeling (divided in three groups) were considered with different number (5, 4 and 3) of fitted hydraulic parameters for optimization. in the first group, simulation was ...

Hyper-parameter tuning is one of the crucial steps in the successful application of machine learning algorithms to real data. In general, the tuning process is modeled as an optimization problem for which several methods have been proposed. For complex algorithms, the evaluation of a hyper-parameter configuration is expensive and their runtime is speed up through data sampling. In this paper, t...