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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Hydrothermal liquefaction of chlamydomonas nivalis and nannochloropsis gaditana microalgae under different operating conditions over copper-exchanged zeolites(Elsevier B.V., 2024) Borhan, E.; Haznedaroglu, Berat Z.; Department of Chemical and Biological Engineering; Yousefzadeh, Hamed; Uzun, Alper; Erkey, Can; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); College of EngineeringIn this study, two different green microalgae, Chlamydomonas nivalis (C. nivalis) and Nannochloropsis gaditana (N. gaditana), were cultivated in open ponds and the harvested wet biomass was converted to bio-crude by hydrothermal liquefaction (HTL) with/without catalyst. Catalytic HTL experiments were performed by using copper-exchanged zeolites including Cu-MOR, Cu-ZSM-5, and Cu-SSZ13, synthesized by recently developed supercritical ion exchange method using scCO2. The composition of all bio-crudes was analyzed by elemental analysis and GC/MS. First, the effects of different operating conditions on the yields of the products and the bio-crude composition were determined for non-catalytic process. Temperature, duration, and water/algae biomass ratio in the feed were the process parameters investigated in the ranges of 250–350 ºC, 10–60 min, and 5–20 wt%, respectively. For C. nivalis, 300 ºC, 60 min, and water/algae ratio of 4 were the optimum conditions which led to maximum bio-crude yield of 18.8 wt%, while 300 ºC, 30 min, and water/algae ratio of 9 were the optimum ones for N. gaditana at which the maximum bio-crude yield of 34.0 wt% was observed. Bio-crude yield of N. gaditana was improved using Cu-MOR, while using catalysts for the case of C. nivalis resulted in more gasification with no positive effect on bio-crude yield. Moreover, elemental analysis showed that the fraction of nitrogen and oxygen in biocrude decreased in catalytic HTL runs, in line with the GC/MS results showing that the concentration of hydrocarbons and cyclic compounds increased in the presence of catalysts accompanied by a decrease in concentration of nitrogenous compounds.Publication Metadata only Physics-informed and data-driven modeling of an industrial wastewater treatment plant with actual validation(PERGAMON-ELSEVIER SCIENCE LTD, 2024) Esenboga, Elif Ecem; Cosgun, Ahmet; Kusoglu, Gizem; Department of Chemical and Biological Engineering; Köksal, Ece Serenat; Asrav, Tuse; Aydın, Erdal; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Graduate School of Sciences and Engineering; College of EngineeringData-driven modeling is essential in chemical engineering, especially in complex systems like wastewater treatment plants. Recurrent neural networks are effective for modeling parameters in wastewater treatment process such as dissolved oxygen concentration and chemical oxygen demand due to their nonlinear adaptability. However, traditional models face challenges such as the requirement for larger datasets and more frequent sampling, noisy measurements, and overfitting. To address this, physics-informed neural networks integrate physical knowledge for improved performance. In our study, we apply both approaches to a wastewater treatment plant, enhancing prediction performance. Our results demonstrate that physics-informed models perform successfully in offline and online validation, especially when standard methods fail. They maintain effectiveness without frequent updates. Yet, integrating physics-informed knowledge can introduce noise when standard methods suffice. This result points out the need for careful consideration of model choice in different scenarios.Publication Metadata only Stepwise conversion of methane to methanol over Cu-mordenite prepared by supercritical and aqueous ion exchange routes and quantification of active Cu species by H2-TPR(Elsevier, 2023) Sushkevich, Vitaly; van Bokhoven, Jeroen A.; Department of Chemical and Biological Engineering; Yousefzadeh, Hamed; Bozbağ, Selmi Erim; Erkey, Can; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); College of EngineeringCopper-exchanged mordenite prepared by supercritical ion exchange (SCIE) and aqueous ion exchange (AIE) were investigated in stepwise conversion of methane to methanol. Increasing the oxygen activation temperature and methane reaction time enhances the methanol yield of copper-exchanged mordenite prepared by SCIE (CuMORS). The reducibility of Cu-MORS was compared with those of Cu-MORA prepared by aqueous ion exchange (AIE) using H-2-TPR. It was demonstrated for the first time that deconvoluted H2-TPR profile coupled with effects of Cu loading and oxygen activation temperature on methanol yield data can be used to distinguish the active Cu sites from inactive ones based on their reduction temperature. The copper species responsible for methane activation were found to be reduced below 150 C by H-2 in both Cu-MORS and Cu-MORA. From the stoichiometry of the reaction of H-2 with Cu2+ species, the average number of copper atoms of active sites were calculated as 2.07 and 2.80 for Cu-MORS and Cu-MORA, respectively. Differences in structure of copper species caused by the synthesis routes were also detected by in-situ FTIR upon NO adsorption indicating a higher susceptibility of CuMORS towards autoreduction. The results demonstrated the potential of TPR based methods to identify copper active sites and suggested the importance of site selective ion exchange in order to controllably synthesize active Cu species in zeolites.Publication Metadata only Boosting methylene blue adsorption capacity of an industrial waste-based geopolymer by depositing graphitic carbon nitride onto its surface: towards sustainable materials for wastewater treatment(Pergamon-Elsevier Science Ltd, 2024) Kaya-Ozkiper, Kardelen; Soyer-Uzun, Sezen; Department of Chemical and Biological Engineering; Uzun, Alper; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); College of EngineeringSurface characteristics of a geopolymer (GP) from an industrial waste, red mud (RM), and metakaolin (MK), were tuned by depositing urea-derived graphitic carbon nitride (g-C3N4) onto its surface. Methylene blue (MB) adsorption measurements demonstrated that the resulting g-C3N4/RM-MK-GP offers an excellent MB uptake capacity of 170.9 mg g-1, much higher than those of either the GP or the g-C3N4. Kinetics measurements revealed that chemisorption has an important effect on adsorption. The regenerability of g-C3N4/RM-MK-GP was studied for up to four consecutive cycles. Differences between the adsorption capacities of g-C3N4 and g-C3N4/RM-MKGP were investigated by combining the power of various characterization tools. Results pointed out that surface functional groups associated with g-C3N4, surface hydroxyl and silanol groups of RM-MK-GP, together with exchangeable charge balancing cations of geopolymeric framework provide a unique structure for g-C3N4/RMMK-GP. This study presents a versatile route to produce a sustainable, efficient, and cheap adsorbent for wastewater treatment.Publication Metadata only Theoretical insight into selectivity and catalytic activity of γ-(Al2O3) supported Ir(CO)2 complex for 1,3-butadiene partial hydrogenation(Elsevier, 2024) Akgul, Deniz; Ince, Deniz; Kozuch, Sebastian; Aviyente, Viktorya; Department of Chemical and Biological Engineering; Uzun, Alper; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); College of EngineeringSelective hydrogenation reactions of unsaturated hydrocarbons over metal oxide-supported transition metals like palladium, platinum, rhodium, or iridium complexes are common processes, but understanding the reaction mechanisms is still challenging, since the interactions between the reactants and the active center remain unclear. Herein, we have modeled the 1,3-butadiene hydrogenation mechanism in the presence of gamma-(Al2O3)2 supported iridium catalyst, initially present as [Ir(I)(CO)2]+. The origins of the selectivity and reactivity of the reduction reactions of butadiene to butenes has been elucidated by using density functional theory (DFT) within the framework of the energetic span model.Publication Metadata only Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins(John Wiley and Sons Inc, 2024) Erkip Albert; Department of Chemical and Biological Engineering; Erman, Burak; Department of Chemical and Biological Engineering; College of EngineeringAn explicit analytic solution is given for the Langevin equation applied to the Gauss-ian Network Model of a protein subjected to both a random and a deterministic peri-odic force. Synchronous and asynchronous components of time correlation functionsare derived and an expression for phase differences in the time correlations of resi-due pairs is obtained. The synchronous component enables the determination ofdynamic communities within the protein structure. The asynchronous componentreveals causality, where the time correlation function between residues i and j differsdepending on whether i is observed before j or vice versa, resulting in directionalinformation flow. Driver and driven residues in the allosteric process of cyclophilin Aand human NAD-dependent isocitrate dehydrogenase are determined by a perturba-tion-scanning technique. Factors affecting phase differences between fluctuations ofresidues, such as network topology, connectivity, and residue centrality, are identi-fied. Within the constraints of the isotropic Gaussian Network Model, our resultsshow that asynchronicity increases with viscosity and distance between residues,decreases with increasing connectivity, and decreases with increasing levels of eigen-vector centrality.Publication Metadata only Supercritical ion exchange: a new player in Cu-zeolite catalyst synthesis towards NH3-SCR of nox(Elsevier B.V., 2024) Department of Chemical and Biological Engineering; Yousefzadeh, Hamed; Sarı, Tarık Bercan; Bozbağ, Selmi Erim; Erkey, Can; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); College of Engineering; Graduate School of Sciences and EngineeringIn this study, the effect of zeolite type, copper loading, and synthesis method/conditions on the performance of a range of Cu/Zeolites in the Standard Selective Catalytic Reduction (SCR) reaction was investigated via utilizing synthesis methods of Supercritical and Aqueous Ion Exchange (SCIE and AIE). Cu/MORS synthesized via SCIE outperformed the conventionally prepared catalyst, Cu-MORA. Cu/MORS and Cu/ZSM-5 exhibited higher NO conversion than Cu/SSZ-13 at low temperatures due to higher Cu loading. However, Cu/SSZ-13 ones surpassed the other catalysts in terms of NO conversion at temperatures above 450 °C. Selective nature of SCIE under different conditions resulted in an obvious difference between the catalyst's performance while the SCIE temperature varied from 40 to 80 °C. The results of this study showed that synthesis method/condition affects directly the Cu speciation and catalyst performance in NH3-SCR, calling for developing new synthesis methods of Cu/Zeolites to modify the catalysts performance in after-treatment systems.Publication Metadata only Attenuation of Type IV pili activity by natural products(Taylor & Francis Inc, 2024) Yalkut, Kerem; Hassine, Soumaya Ben Ali; Kula, Ceyda; Ozcan, Aslihan; Avci, Fatma Gizem; Akbulut, Berna Sariyar; Ozbek, Pemra; Department of Chemical and Biological Engineering; Başaran, Esra; Keskin, Özlem; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of EngineeringThe virulence factor Type IV pili (T4P) are surface appendages used by the opportunistic pathogen Pseudomonas aeruginosa for twitching motility and adhesion in the environment and during infection. Additionally, the use of these appendages by P. aeruginosa for biofilm formation increases its virulence and drug resistance. Therefore, attenuation of the activity of T4P would be desirable to control P. aeruginosa infections. Here, a computational approach has been pursued to screen natural products that can be used for this purpose. PilB, the elongation ATPase of the T4P machinery in P. aeruginosa, has been selected as the target subunit and virtual screening of FDA-approved drugs has been conducted. Screening identified two natural compounds, ergoloid and irinotecan, as potential candidates for inhibiting this T4P-associated ATPase in P. aeruginosa. These candidate compounds underwent further rigorous evaluation through molecular dynamics (MD) simulations and then through in vitro twitching motility and biofilm inhibition assays. Notably, ergoloid emerged as a particularly promising candidate for weakening the T4P activity by inhibiting the elongation ATPases associated with T4P. This repurposing study paves the way for the timely discovery of antivirulence drugs as an alternative to classical antibiotic treatments to help combat infections caused by P. aeruginosa and related pathogens.Publication Metadata only Equipment selection for coupling a microgrid with a power-to-gas system in the context of optimal design and operation(Elsevier Ltd, 2024) Akülker, Handan; Department of Chemical and Biological Engineering; Aydın, Erdal; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); College of EngineeringThis study proposes a one-layer deterministic Mixed-Integer Nonlinear Programming to design and schedule a PTG-integrated microgrid. The key contribution is that optimal equipment selection, design, and scheduling, considering the PTG system at the core of the problem, are determined just in a single formulation. Scenarios based on different carbon dioxide taxes and natural gas prices are investigated. Only one wind turbine farm is chosen when the carbon dioxide tax is increased from 50 $/ton to 100 $/ton. On the other hand, when the natural gas price is increased from 1.548 $/m3 to 1.72 $/m3, two wind turbine farms are selected. Solar panel arrays are not chosen in all the scenarios. Generated power by solar panels is not enough for installation despite their much lower carbon dioxide emissions and negligible operational costs. Consequently, the optimal equipment selections may change linked to the natural gas price and carbon dioxide tax.Publication Metadata only Combining computational screening and machine learning to explore MOFs and COFs for methane purification(AIP Publishing, 2024) Department of Chemical and Biological Engineering; Gülbalkan, Hasan Can; Uzun, Alper; Keskin, Seda; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of EngineeringMetal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have great potential to be used as porous adsorbents and membranes to achieve high-performance methane purification. Although the continuous increase in the number and diversity of MOFs and COFs is a great opportunity for the discovery of novel adsorbents and membranes with superior performances, evaluating such a vast number of materials in the quickest and most effective manner requires the development of computational approaches. High-throughput computational screening based on molecular simulations has been extensively used to identify the most promising MOFs and COFs for methane purification. However, the enormous and ever-growing material space necessitates more efficient approaches in terms of time and effort. Combining data science with molecular simulations has recently accelerated the discovery of optimal MOF and COF materials for methane purification and revealed the hidden structure-performance relationships. In this perspective, we highlighted the recent developments in combining high-throughput molecular simulations and machine learning to accurately identify the most promising MOF and COF adsorbents and membranes among thousands of candidates for separating methane from other gases including acetylene, carbon dioxide, helium, hydrogen, and nitrogen. After providing a brief overview of the topic, we reviewed the pioneering contributions in the field and discussed the current opportunities and challenges that we need to direct our efforts for the design and discovery of adsorbent and membrane materials.