Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Author: search.filters.author.Sildir, HasanHas files: Yes

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  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Storage Tank Farming Planning Under Equipment and Port Operational Costs Through Mixed Integer Quadratically Constrained Programming
    (Elsevier, 2025) Yalcin, Damla; Deliismail, Ozgun; Tuncer, Basak; Sildir, Hasan
    The study contributes a new method for managing crude oil tank farms, focusing on scheduling and optimizing storage tanks using mathematical modeling. The short-term continuous-time scheduling model reduces tank requirements and performs selection with convenient capacities. The nonconvex mixed-integer quadratically constrained programming (MIQCP) model is used to account for tank farm scheduling dynamics. It focuses on the integration of ships, storage tanks, charging tanks, and crude oil distillation units. The study examines 8 cases focusing on oil supply, arrival times, prices, and maximum flow rate constraints to show the impact of real-world volatility. By incorporating process intensification principles, the mathematical model emphasizes the importance of optimizing storage tank usage to minimize port operational costs of crude oils.
  • Article
    Synthesis of Nannochloropsis Oculata Cultivation Process Based on Mixed-Integer Formulations
    (Elsevier, 2025) Kivanc, Sercan; Tuncer, Basak; Deliismail, Ozgun; Sildir, Hasan
    Sophisticated mathematical formulations and related optimization tasks are important to favor microalgae processing. This study focuses on the development of a mixed integer nonlinear programming approach to calculate design and operational decisions through simultaneous and rigorous approach under set of complex constraints and objective functions. Through a set of differential algebraic equations, whose model parameters are obtained through fitting a dataset available in the literature, three case studies are demonstrated for the calculation of optimum cultivation conditions based on economic considerations and biomass production. The case studies show the impact of the approach for the sustainability of the process as different conditions are primary defined by light color, reactor size, dilution rate, feed stream composition, and growing medium are required for desired tasks. The approach is flexible and further modifiable to various considerations for more complex decision-making problems.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 5
    Automated Deep Learning Model Development Based on Weight Sensitivity and Model Selection Statistics
    (Pergamon-elsevier Science Ltd, 2025) Yalcin, Damla; Deliismail, Ozgun; Tuncer, Basak; Boy, Onur Can; Bayar, Ibrahim; Kayar, Gizem; Sildir, Hasan
    Current sustainable production and consumption processes call for technological integration with the realm of computational modeling especially in the form of sophisticated data-driven architectures. Advanced mathematical formulations are essential for deep learning approach to account for revealing patterns under nonlinear and complex interactions to enable better prediction capabilities for subsequent optimization and control tasks. Bayesian Information Criterion and Akaike Information Criterion are introduced as additional constraints to a mixed-integer training problem which employs a parameter sensitivity related objective function, unlike traditional methods which minimize the training error under fixed architecture. The resulting comprehensive optimization formulation is flexible as a simultaneous approach is introduced through algorithmic differentiation to benefit from advanced solvers to handle computational challenges and theoretical issues. Proposed formulation delivers 40% reduction, in architecture with high accuracy. The performance of the approach is compared to fully connected traditional methods on two different case studies from large scale chemical plants.