Browsing by Author "Sarac, Tugba"

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    Article
    Citation - WoS: 6
    Citation - Scopus: 6
    Estimation of Biosurfactant Production Parameters and Yields Without Conducting Additional Experiments on a Larger Production Scale
    (Elsevier, 2022) Sarac, Tugba; Anagun, Ahmet Sermet; Ozcelik, Feristah; Celik, Pinar Aytar; Toptas, Yagmur; Kizilkaya, Busra; Cabuk, Ahmet
    In this study, a Plackett-Burman design was applied to investigate critical factors for surface tension. After adding a new factor called production scale, a central composite design (CCD) was constructed to examine nonlinear relations among factors and surface tension. An artificial neural network (ANN) was trained using data from CCD experiments. The ANN with the best structure of 5-6-1 was then tested with different unseen data sets. The predictions from ANN were within the 95% confidence interval (CI), even for a larger production scale, deter-mined by using the replicates. A genetic algorithm (GA) was developed to check how the values of the factors vary if the production scale was set to a user-defined value. Based on the validation experiments, it was observed that the 95% confidence interval of surface tension was 36.83 +/- 1.00 mN m-1 while pH 8, temperature 35 degrees C, incubation time 12 h, and amount of inoculum 2.30%, respectively, for the production scale of 600 mL. The proposed methodological approach with the integration of ANN and GA is considered to make a serious eco-nomic contribution as it allows predicting a proper setup for larger production scales without conducting additional experiments.
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    Citation - WoS: 14
    Citation - Scopus: 17
    A Two-Objective Mathematical Model Without Cutting Patterns for One-Dimensional Assortment Problems
    (Elsevier Science Bv, 2011) Kasimbeyli, Nergiz; Sarac, Tugba; Kasimbeyli̇, Refail
    This paper considers a one-dimensional cutting stock and assortment problem. One of the main difficulties in formulating and solving these kinds of problems is the use of the set of cutting patterns as a parameter set in the mathematical model. Since the total number of cutting patterns to be generated may be very huge, both the generation and the use of such a set lead to computational difficulties in solution process. The purpose of this paper is therefore to develop a mathematical model without the use of cutting patterns as model parameters. We propose a new, two-objective linear integer programming model in the form of simultaneous minimization of two contradicting objectives related to the total trim loss amount and the total number of different lengths of stock rolls to be maintained as inventory, in order to fulfill a given set of cutting orders. The model does not require pre-specification of cutting patterns. We suggest a special heuristic algorithm for solving the presented model. The superiority of both the mathematical model and the solution approach is demonstrated on test problems. (C) 2010 Elsevier B.V. All rights reserved.