Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Robust Scheduling of Crude Oil Farming and Processing Under Uncertainty(Elsevier, 2026) Yalcin, Damla; Sildir, HasanThe sulphur content in crude oil has a significant impact on refinery operations, influencing the feasibility of crude blending, the distribution of product yields, and overall economic performance. Variations in sulphur content introduce uncertainty in the short-term scheduling of crude oil loading, blending, and distillation processes. This study introduces a scenario-based stochastic optimization framework in which sulphur uncertainty is treated as a central modeling element, represented through a regression-based relationship with specific gravity (SG). The approach systematically propagates uncertainty through blending decisions, crude distillation unit (CDU) feed composition, and product yields. The problem is modeled as a mixed-integer quadratically constrained programming (MIQCP) formulation within a continuous-time scheduling framework, enabling the simultaneous optimization of timing, blending, and processing strategies. The results indicate that increased sulphur uncertainty adversely affects the distribution of yields for nine end-products, resulting in profit losses. These findings underscore the importance of explicitly managing compositional uncertainty and provide insights into cost-performance trade-offs in refinery scheduling.Article Machine Learning Integrated Solvothermal Liquefaction of Lignocellulosic Biomass to Maximize Bio-Oil Yield(Elsevier Sci Ltd, 2025) Ocal, Bulutcem; Sildir, Hasan; Yuksel, AsliAccelerating consumption of limited fossil-based for economic growth and simultaneously mitigating greenhouse gas emissions create a dilemma that is waiting to be solved by researchers. In this context, solvothermal liquefaction of lignocellulosic biomass to produce bio-oil is a promising way to obtain green energy. However, maximizing bio-oil is challenging to optimize the operating parameters employing conventional techniques due to the complexity and non-linearity of the process. Lately, machine learning approaches have become powerful tools for addressing complex nonlinear problems by predicting process behavior and regulating operating parameters for optimization by learning from datasets. The current research demonstrates integrating experimental and a developed artificial neural network model to optimize solvothermal liquefaction of pinus brutia, based on temperature, water fraction, and biomass amount in maximizing bio-oil generation for the first time. The highest bio-oil yields were obtained at 31.40 %, 18.68 %, and 39.69 %, respectively, with 4 and 8 g biomass in the presence of water, ethanol, and water/ethanol mixture at 240 degrees C. Under the model conditions, the maximum biooil yield was experimentally verified at 46.20%, which was predicted at 48.8 %. Beyond providing accurate yield predictions, the approach highlights the potential of date-driven modeling to reduce experimental workload and cost while aiding parameter selection to improve efficiency. These outcomes emphasize the importance of machine learning integration into liquefaction process, providing remarkable results for future process design, optimization, and scalability. On the other hand, the study also includes characterization results (ultimate, proximate, FTIR, and GC-MS) of selected products and pinus brutia.Article Towards Facile Deep Learning Architectures for Chemical Processes: Simultaneous Pseudo-Global Training and Economic Synthesis(Institution of Chemical Engineers, 2025) Sildir, Hasan; Yalcin, Damla; Tuncer, Basak; Deliismail, Ozgun; Leblebici, Mumin EnisChemical process data is usually not directly valorized in pure machine learning predictive models due to limited data availability. This limitation often caused from high sensor costs, data variety, and veracity issues. In response, this study proposes a novel formulation based on mixed-integer linear programming (MILP), called Approximated Deep Learning (ADL), to overcome these limitations and enable accurate modeling under data scarcity. The ADL simultaneously performs input selection, outlier filtering, and training of deep learning architectures within a single-level optimization problem. The method approximates the nonlinear and nonconvex components of traditional deep learning models in the mixed-integer domain through sophisticated reformulations, achieving a pseudo-global solution. A key feature of ADL is the integration of sensor pricing as a regularization mechanism, which promotes cost-efficient soft sensor design without compromising predictive performance. The proposed framework is validated on a publicly available bubble column dataset and benchmarked against four conventional deep learning methods. Results show that ADL achieves superior test accuracy with more than 50% reduction in input space, drastically reducing sensor cost. Furthermore, the optimized architecture is a high-quality initial guess for transfer learning on larger datasets. Overall, the method offers a practical and economically viable solution for data-driven chemical process modeling.Article Citation - WoS: 1Citation - Scopus: 1Storage Tank Farming Planning Under Equipment and Port Operational Costs Through Mixed Integer Quadratically Constrained Programming(Elsevier, 2025) Yalcin, Damla; Deliismail, Ozgun; Tuncer, Basak; Sildir, HasanThe 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, HasanSophisticated 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.