Şıldır, Hasan
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Sildir, Hasan
Sıldır, Hasan
Şildir, Hasan
Sıldır, Hasan
Şildir, Hasan
Job Title
Email Address
hasansildir@iyte.edu.tr
Main Affiliation
03.02. Department of Chemical Engineering
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
0
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3GOOD HEALTH AND WELL-BEING
1
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4QUALITY EDUCATION
0
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5GENDER EQUALITY
0
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6CLEAN WATER AND SANITATION
0
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7AFFORDABLE AND CLEAN ENERGY
3
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8DECENT WORK AND ECONOMIC GROWTH
0
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
0
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
1
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13CLIMATE ACTION
2
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14LIFE BELOW WATER
0
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15LIFE ON LAND
0
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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17PARTNERSHIPS FOR THE GOALS
0
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Documents
29
Citations
249
h-index
9
Documents
23
Citations
214
Scholarly Output
11
Articles
10
Views / Downloads
706/656
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
8
Scopus Citation Count
8
Patents
0
Projects
0
WoS Citations per Publication
0.73
Scopus Citations per Publication
0.73
Open Access Source
2
Supervised Theses
0
| Journal | Count |
|---|---|
| Chemical Engineering Research and Design | 3 |
| Chemical Engineering and Processing - Process Intensification | 2 |
| Chemical Engineering Science | 1 |
| Computers & Chemical Engineering | 1 |
| Computers & Chemical Engineering | 1 |
Current Page: 1 / 2
Scopus Quartile Distribution
Competency Cloud
11 results
Scholarly Output Search Results
Now showing 1 - 10 of 11
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 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 Knowledge-Based Training of Learning Architectures Under Input Sensitivity Constraints for Improved Explainability(Pergamon-Elsevier Science Ltd, 2026) Sildir, Hasan; Erturk, Emrullah; Edizer, Deniz Tuna; Deliismail, Ozgun; Durna, Yusuf Muhammed; Hamit, BahtiyarThe traditional machine learning (ML) training problem is unconstrained and lacks an explicit formulation of the underlying driving phenomena. Such a formulation, based solely on experimental data, does not ensure the delivery of qualitative knowledge among variables due to many theoretical issues in the optimization task. This study further tightens Artificial Neural Networks (ANNs) training by including input sensitivities as additional constraints and applies to regression and classification tasks based on literature data. In theory, such sensitivity represents the change direction of the target variable per change in measurements from indicators. The resulting nonlinear optimization problem is solved th rough a rigorous solver and includes the sensitivity expressions through algorithmic differentiation. Compared to traditional methods, with an acceptable decrease in the prediction capability, the proposed model delivers more intuitive, explainable, and experimentally verifiable predictions under input variable variations, under robustness to overfitting, while serving robust identification tasks. A classification case study includes a patient-oriented clinical decision support system development based on the impact of cancer-indicating variables. A competitive test prediction accuracy is obtained compared to commonly used algorithms despite 10 % decrease in the training. The regression case is built upon the energy load estimation to account for prominent considerations to obtain desired sensitivity patterns and proposed methodology delivers significant accuracy drop compared to some formulations to address knowledge patterns. The approach delivers a compatible pattern with practitioner expertise and is compared to widely used machine learning algorithms, whose performances are evaluated through common statistics in addition to multi-variable response graphs.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 Citation - WoS: 1Citation - Scopus: 1Dynamic and Stochastic Optimization of Algae Cultivation Process(Pergamon-elsevier Science Ltd, 2025) Kivanc, Sercan; Beykal, Burcu; Deliismail, Ozgun; Sildir, HasanThis study offers a realistic representation of system dynamics which accounts for light intensity, biomass, substrate, and nitrogen concentration, by employing stochastic programming techniques to account for spatial and temporal variations for algae growth. The optimization task focuses on lipid productivity and selectivity, which are crucial factors in the context of algal biofuel production. Different scenarios from likely and unlikely cases of model parameters were evaluated. Optimal initial conditions for key variables such as nitrogen, substrate, light, biomass, lipid, and surface light intensity are calculated, considering the uncertainty of the parameters as well as other governing equations. The results show that a remarkable 11.18% increase in lipid productivity compared to a reference scenario. Furthermore, in the stochastic case, our results highlight that uncertainty has a disproportionately large effect on biomass in comparison to lipid concentration, providing valuable insights into the behavior of the system under varying conditions. This provides a comprehensive exploration of the parameter uncertainty on lipid productivity and algal growth.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: 5Citation - Scopus: 5Automated 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, HasanCurrent 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.Conference Object Citation - WoS: 1Citation - Scopus: 1A Mixed-Integer Dynamic and Stochastic Algae Process Optimization(Elsevier, 2024) Kivanc, Sercan; Deliismail, Özgün; Şıldır, HasanWith increased energy demand as it gets scarcer, a great deal of research is being carried out into alternatives to non - renewable energy resources. One of the promising studies is the biofuel production from micro algae. Microalgae are photosynthetic organisms and capture carbon dioxide, reducing emissions and providing valuable products (fuel, fertilizer, etc.). Thus, efficiency in the design and optimization of process related units are important. In this study, the optimal experimental conditions for Nannochloropsis Oculata were calculated under the constraints of the model equations and other process related constraints through simultaneous optimization approach. The economic evaluation of the process is also handled by introducing the uncertainty in the economic measures sampled from normal distribution to maximize the average profit. Unlike traditional approaches, the MINLP formulation, which is solved stochastically, dynamically, and simultaneously, provides more robust and reliable results, flexibility, improved decision making, reduced risks to be taken and a better understanding of risk factors. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)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.Article Simultaneous Topology Design and Optimization of Pde Constrained Processes Based on Mixed Integer Formulations(Elsevier B.V., 2024) Erturk,E.; Deliismail,O.; Sildir,H.Simultaneous topological design and optimization of complex processes that are described by partial differential equations is a challenging but promising research area. Widely adopted nested and sequential approaches are mostly applicable based on heuristic solutions, hindering the theoretical improvement potential due to decentralized decision-making in subsequent stages with a significant number of trial-and-error procedures. This study introduces a mixed integer formulation addressing the governing equations and case-dependent topological constraints at each discretization point, enabling solutions through rigorous solvers under process-related constraints and objectives. Nonlinear expressions in the formulations are further tailored using piecewise linear approximations, still representing the major nonlinear trends through a mixed-integer linear nature to favor global optimality and benefit from computational advancements, when needed. Heat and Stokes flow problems are used as case studies to demonstrate the applicability of the methodology. © 2024 Elsevier B.V.