Chemical Engineering / Kimya Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/14
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Conference Object Citation - WoS: 24Effect of pH and Hydration on the Normal and Lateral Interaction Forces Between Alumina Surfaces(2006) Polat, Mehmet; Sato, Kimiyasu; Nagaoka, Takaaki; Watari, KojiNormal and lateral interaction forces between alumina surfaces were measured using Atomic Force Microscopy-Colloid Probe Method at different pH. The normal force curves exhibit a well-defined repulsive barrier and an attractive minimum at acidic pH and the DLVO theory shows excellent agreement with the data. The normal forces are always repulsive at basic pH and the theory fails to represent the measurements. Lateral forces are almost an order of magnitude smaller in the basic solutions. These differences, which have important implications in the study of stability and rheology, are attributed to the hydration of the alumina surface at basic pH. © 2013 Elsevier B.V., All rights reserved.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.Article Citation - WoS: 6Citation - Scopus: 4Regenerable Nickel Catalysts Strengthened Against H2s Poisoning in Dry Reforming of Methane(Elsevier, 2025) Kesan Celik, Nazli; Yasyerli, Sena; Arbag, Huseyin; Tasdemir, H. Mehmet; Yasyerli, NailIn this study, alumina-supported bimetallic Ni-Cu and trimetallic Ni-Cu-Ce catalysts were synthesized to improve catalysts resistant to coke formation and sulfur poisoning for dry reforming of methane (DRM). The effects of parameters such as feed composition, synthesis method, and H2S concentration using the catalyst with the best activity were also investigated. To determine the physical and chemical properties of the synthesized catalysts, XRD, N2 adsorption-desorption, TGA-DTA, ICP-OES, SEM-EDX, XPS, and DRIFTS analyses were performed. XRD analysis showed that the fresh Ni-Cu catalysts have elemental nickel and gamma- alumina phases in their structures. In addition to these structures, the CeO2 crystal structure was determined for the Ni-Cu-Ce catalyst. Type IV isotherm with H1 hysteresis indicating uniform mesoporous structure was obtained with all the catalysts. The activities of the synthesized catalysts in DRM were performed in the presence of different concentrations of H2S (2 ppm, 50 ppm, and 500 ppm) in a fixed bed reactor at 750 degrees C using a gas chromatography-equipped system. The alumina-supported 8Ni-3Cu-8Ce catalyst prepared by the impregnation method exhibited a higher and more stable activity comparing the bimetallic Ni-Cu catalyst in the presence of H2S. Adding copper and cerium to the nickel catalyst has a curative effect on resistance to coke formation and sulfur poisoning. Excess CO2 in the feed stream increased the H2S poisoning resistance of the catalyst. To analyze the reactor exit stream in catalytic activity using different feed stream compositions such as H2S+He, H2S+CO2+He, and H2S+CO2+CH4+He, FTIR with a gas cell was used. The formation of carbonyl sulfide (COS) and H2O, which occurs due to the possible reaction between CO2 and H2S, was observed. Regeneration studies showed that the catalyst could undergo regeneration with a low oxygen concentration (0.3 % O2 in He). 8Ni-3Cu-8Ce@SGA, which gave 71 % CH4 conversion in the first minute of the reaction test in the presence of 50 ppm H2S, was regenerated after completely losing its activity at the end of 5 h. 66 % CH4 conversion was achieved when tested again in the absence of H2S (CH4/CO2/Ar:1/1/1). The 8Ni-3Cu-8Ce@SGA catalyst was deemed worthy of investigation for industrial applications.Book Part A Study on Absorption and Reflection of Infrared Light by the Uncoated and Al Coated Surfaces of Polymer Films Techniques(Apple Academic Press, 2014) Arkış, Esen; Balköse, DevrimPolymer films coated with a thin layer of aluminum or aluminum oxide are extensively used in food packing as heat shields. The infrared rays were not transmitted through the films and were reflected protecting the contents from the harmful effects of infrared light. The quantitative measurement of the film thickness and infrared light reflection and absorption capacities of aluminum coated films used as packing materials were possible using infrared spectroscopy. © 2015 by Apple Academic Press, Inc.Book Part Advances in Nanocomposite Membranes for CO2 Removal(Elsevier, 2024) Marpani,F.; Othman,N.H.; Alias,N.H.; Mat Shayuti,M.S.; Alsoy Altınkaya, SacideNanocomposite membranes have emerged as a promising solution for efficient carbon dioxide (CO2) removal in gas separation processes. These membranes combine polymeric matrices with inorganic nanofillers to synergize the excellent separation performance of inorganic materials with the mechanical stability of polymers. The choice of nanofillers, such as porous and nonporous materials, significantly influences the gas permeability and selectivity of the resulting nanocomposite membranes. Porous fillers with interstitial channels and large surface areas are found to selectively adsorb CO2, enhancing membrane separation performance. On the other hand, nonporous fillers alter the polymer chain orientation, influencing gas separation differently. The 1D, 2D, and 3D morphologies of nanofillers offer unique properties in terms of surface-to-volume ratio, permeability, and selectivity. The fabrication of nanocomposite membranes also plays a crucial role, and advances in materials and manufacturing techniques have enabled the design of high-performing membranes. Asymmetric and symmetric configurations have been explored to optimize separation efficiency. Nevertheless, challenges such as aging, compaction, and swelling need to be addressed to ensure the long-term stability of nanocomposite membranes. Future research should focus on developing advanced theoretical models to better predict gas permeation behaviors in these membranes. Overall, nanocomposite membranes offer a promising avenue for efficient CO2 removal, contributing to sustainable environmental practices and energy production. © 2024 Elsevier Ltd. All rights reserved.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/)Book Part Calcium Soap Lubricants(CRC Press, 2015) İzer, Alaz; Kahyaoğlu, Tuğçe Nefise; Balköse, DevrimThe reparation and characterization of calcium stearate (CaSt2) and a lubricant by using calcium stearate were aimed at in this study. Calcium stearate powder was prepared from sodium stearate and calcium chloride by precipitation from aqueous solutions. CaSt2 and the Light Neutral Base oil were mixed together to obtain lubricating oil. It was found that CaSt2 had a melting temperature of 142.8 °C and in base oil it had a lower melting point, above 128 °C. It was dispersed as lamellar micelles as the optical micrographs had shown. From rate of settling the size of dispersed particles were found to be 1.88 µm and 0.11 µm for lubricants having 1% and 2% CaSt2, respectively. The friction coefficient and wear scar diameter of base oil 0.099 and 1402 nm were reduced to 0.0730 and 627.61 nm respectively for the lubricant having 1% CaSt2. Lower wear scar diameter (540 nm) was obtained for lubricant with 2% CaSt2. CaSt2 improved the lubricating property of the base oil but did not improve its oxidative and thermal stability. © 2015 by Apple Academic Press, Inc.Article Citation - WoS: 1Citation - Scopus: 1Optimized 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.Article Citation - WoS: 3Citation - Scopus: 3Shallow Shell Ssta63 Resin: a Rapid Approach To Remediation of Hazardous Nitrate(Royal Society of Chemistry, 2024) Cendik, Elif; Saygi, Mugenur; Recepoğlu, Yaşar Kemal; Arar, OzgurThis study examines the potential of Purolite Shallow Shell (TM) SSTA63 anion exchange resin for mitigating nitrate ion (NO3-) contamination in aqueous environments. Through systematic experimentation, including dosage optimization, pH dependency, kinetic and desorption studies, we investigate the sorption behavior and practical applications of the resin. Results indicate that the resin effectively removes NO3- ions, with maximum efficiency achieved within 10 minutes. When 0.025 g of resin was used, 75% of NO3- was removed, whereas with 0.05 g, 89% was removed, and with 0.1 g of resin, 95% was removed. At pH 1, approximately 50% of NO3- ions were removed, with removal efficiency reaching 97% between pH 4 and 10. Sorption isotherms affirm the suitability of the Langmuir model for the current investigation. The monolayer maximum sorption capacity (qmax) value was found to be 53.65 mg g-1. The resin demonstrates robust desorption capabilities using 0.1 M hydrochloric acid (HCl), effectively desorbing NO3- above 99%, indicating easy NO3- desorption and resin regeneration. The presence of coexisting ions such as chloride (Cl-), sulfate (SO42-), and phosphate (PO43-) showed a minimal impact on NO3- removal in individual binary mixtures, with efficiencies exceeding 93%, suggesting a strong selectivity of the resin towards NO3-. Purolite SSTA63 anion exchange resin exhibited a high affinity for NO3- ions, even over other competing ions, despite the general trend of ion exchange resins to favor ions with a higher atomic number and valence. Overall, this resin presents a promising solution for NO3- removal, with implications for water treatment and environmental remediation. This study explores the potential of Purolite Shallow Shell (TM) SSTA63 anion exchange resin for mitigating nitrate ion (NO3-) contamination in aqueous environments.Article Citation - WoS: 15Citation - Scopus: 16A Machine Learning Ensemble Approach for Predicting Solar-Sensitive Hybrid Photocatalysts on Hydrogen Evolution(IOP Publishing, 2024) Bakır, Rezan; Orak, Ceren; Yuksel, AsliHydrogen, as the lightest and most abundant element in the universe, has emerged as a pivotal player in the quest for sustainable energy solutions. Its remarkable properties, such as high energy density and zero emissions upon combustion, make it a promising candidate for addressing the pressing challenges of climate change and transitioning towards a clean and renewable energy future. In an effort to improve efficiency and reduce experimental costs, we adopted machine learning techniques in this study. Our focus turned to predictive analyses of hydrogen evolution values using three photocatalysts, namely, graphene-supported LaFeO3 (GLFO), graphene-supported LaRuO3 (GLRO), and graphene-supported BiFeO3 (GBFO), examining their correlation with varying levels of pH, catalyst amount, and H2O2 concentration. To achieve this, a diverse range of machine learning models are used, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), XGBoost, Gradient Boosting, and AdaBoost-each bringing its strengths to the predictive modeling arena. An important step involved combining the most effective models-Random Forests, Gradient Boosting, and XGBoost-into an ensemble model. This collaborative approach aimed to leverage their collective strengths and improve overall predictability. The ensemble model emerged as a powerful tool for understanding photocatalytic hydrogen evolution. Standard metrics were employed to assess the performance of our ensemble prediction model, encompassing R squared, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The yielded results showcase exceptional accuracy, with R squared values of 96.9%, 99.3%, and 98% for GLFO, GBFO, and GLRO, respectively. Moreover, our model demonstrates minimal error rates across all metrics, underscoring its robust predictive capabilities and highlighting its efficacy in accurately forecasting the intricate relationships between GLFO, GBFO, and GLRO values and their influencing factors.