Chemical Engineering / Kimya Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/14

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Department: search.filters.department.İzmir Institute of TechnologyWoS Q: search.filters.wosQuartile.Q2

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  • 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.
  • Erratum
    Citation - WoS: 1
  • Article
    Citation - WoS: 6
    Citation - Scopus: 6
    Synthesis of a Novel Cellulose-Based Adsorbent From Olive Tree Pruning Waste for Removal of Boron From Aqueous Solution
    (Springer Science and Business Media Deutschland GmbH, 2024) Altınbaş, B.F.; Yüksel, A.
    This work investigated the valorization of olive tree pruning debris as a biosorbent for the removal of environmentally hazardous boron from aqueous solution using batch adsorption. For this purpose, a novel, waste-based, boron selective biosorbent from olive tree pruning waste (N-OPW) was synthesized. Alkali pretreatment, followed by glycidyl-methacrylate (GMA) grafting and providing boron selectivity with n-methyl-d-glucamine (NMDG) steps, was applied to the biomass, respectively. N-OPW was characterized using SEM, TGA, and FT-IR analyses. N-OPW showed excellent boron biosorption capacity (21.80 mg/g) in an operation pH range between 2 and 12. The equilibrium was attained in 2 h and the Freundlich isotherm (R2 = 0.997) and pseudo-second-order kinetics (R2 = 0.99) provided the strongest match to experimental data. According to thermodynamic studies, boron adsorption was exothermic (ΔH = −34.14 kJ/mol). The reusability tests with real geothermal water showed that adsorbent had no significant decrease in boron removal capacity while desorbing >99% of the boron adsorbed for three cycles of adsorption/desorption. Results indicated that a promising, reusable, and boron selective biosorbent was successfully synthesized while utilizing olive pruning waste. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.