Chemical Engineering / Kimya Mühendisliği

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

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Start date: 2020Department: search.filters.department.İzmir Institute of Technology

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Now showing 1 - 5 of 5
  • 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.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Optimized Lithium(I) Recovery From Geothermal Brine of Germencik, Türkiye, Utilizing an Aminomethyl Phosphonic Acid Chelating Resin
    (Taylor and Francis Ltd., 2025) Recepoğlu, Y.K.
    This study investigates the performance of Lewatit TP 260 ion exchange resin for the efficient recovery of lithium (Li(I)) from geothermal water sourced from the Germencik Geothermal Power Plant in Türkiye. A series of batch sorption experiments were performed to evaluate the influence of key parameters, including resin dosage, solution pH, temperature, initial Li(I) concentration, and contact time, on the Li(I) recovery process. The optimal conditions were determined to be a resin dose of 0.5 g per 25 mL of geothermal water, pH in the range of 6–8, and a temperature of 25°C. Under these conditions, the resin achieved a maximum Li(I) recovery rate of 71% from the geothermal water. Sorption isotherms were further analyzed using the Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) models. Among these, the Langmuir model provided the best fit (R² = 0.9841), suggesting a maximum sorption capacity (qm) of 4.31 mg/g. Continuous recovery experiments conducted in column mode confirmed the practical applicability of Lewatit TP 260, achieving a total sorption capacity of 0.41 mg Li(I)/mL resin. The findings exhibit the potential of this resin as a viable sorbent for sustainable Li(I) extraction from geothermal brines, supporting the development of green energy technologies and contributing to the circular economy. © 2024 Taylor & Francis Group, LLC.
  • Correction
    Citation - WoS: 1
    Erratum: Bioactive Snail Mucus-Slime Extract Loaded Chitosan Scaffolds for Hard Tissue Regeneration: the Effect of Mucoadhesive and Antibacterial Extracts on Physical Characteristics and Bioactivity of Chitosan Matrix (Biomedical Materials (Bristol) (2021) 16 (065008) Doi: 10.1088/1748-605x
    (IOP Publishing, 2023) Perpelek, M.; Tamburaci, S.; Aydemi̇r, S.; Tıhmınlıoğlu, F.; Baykara, B.; Karakaşli, A.; Havitçioǧlu, H.
    The authors regret that some errors were identified in 'figures 12 and 13' on pages 14 and 15, in the published manuscript concerning fluorescence microscopy images of Saos-2 and SW1353 cells on scaffolds for 1 and 3 d of incubation. The fluorescence images in figures 12 and 13 were mistakenly used as duplicated due to the inadvertently mislabeling during the processing of files and integrating them into the final figures. Intensity data regarding corrected fluorescence images were also measured and corrected. The revised figures (figures 12 and 13) and their captions appear below. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. (Figure Presented). © 2023 IOP Publishing Ltd.
  • 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.