Ugur Yüzgeç

Learn More
Amodel predictive control (MPC) strategy is presented to determine the optimal control decisions for the short-term refinery scheduling problem. For cases where process disturbances occur or new plans need to be implemented during the scheduling period, the moving horizon strategy allows control decisions to be updated effectively to maintain an optimal(More)
This paper presents two genetic algorithms based on optimization methods to maximize biomass concentration, and to minimize ethanol formation. The objective function is maximized according to the values of feed flow rate, using genetic search approaches. Five case studies were carried out for different initial conditions, which strongly influence the(More)
In this study, previously developed five different state estimation methods are examined and compared for estimation of biomass concentrations at a production scale fed-batch bioprocess. These methods are i. estimation based on kinetic model of overflow metabolism; ii. estimation based on metabolic black-box model; iii. estimation based on observer; iv.(More)
Differential evolution (DE) is one of the novel evolutionary optimization methods used for solving the problems that consist of nondifferentiable, nonlinear and multi-objective functions. In this presented work, the classical DE technique and its various versions, such as opposition based on differential evolution (ODE), adaptive differential evolution(More)
A model predictive control (MPC) strategy is developed to determine the optimal solution of the short-term refinery production planning problem. The main objective of the proposed algorithm is to maximize the total profit and to minimize the costs regarding the refinery process over a planning horizon. The refinery planning problem is solved in discrete(More)
  • 1