Chemical Engineering / Kimya Mühendisliği

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

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Access type: Open accessAuthor: search.filters.author.Yuksel, Asli

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  • Article
    Citation - WoS: 15
    Citation - Scopus: 16
    A Machine Learning Ensemble Approach for Predicting Solar-Sensitive Hybrid Photocatalysts on Hydrogen Evolution
    (IOP Publishing, 2024) Bakır, Rezan; Orak, Ceren; Yuksel, Asli
    Hydrogen, 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.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 5
    Breakthrough Curve Analysis of Phosphorylated Hazelnut Shell Waste in Column Operation for Continuous Harvesting of Lithium From Water
    (Elsevier, 2024) Recepoğlu, Yaşar Kemal; Arar, Ozguer; Yuksel, Asli
    In batch-scale operations, biosorption employing phosphorylated hazelnut shell waste (FHS) revealed excellent lithium removal and recovery efficiency. Scaling up and implementing packed bed column systems necessitates further design and performance optimization. Lithium biosorption via FHS was investigated utilizing a continuous-flow packed-bed column operated under various flow rates and bed heights to remove Li to ultra-low levels and recover it. The Li biosorption capacity of the FHS column was unaffected by the bed height, however, when the flow rate was increased, the capacity of the FHS column decreased. The breakthrough time, exhaustion time, and uptake capacity of the column bed increased with increasing column bed height, whereas they decreased with increasing influent flow rate. At flow rates of 0.25, 0.5, and 1.0 mL/min, bed volumes (BVs, mL solution/mL biosorbent) at the breakthrough point were found to be 477, 369, and 347, respectively, with the required BVs for total saturation point of 941, 911, and 829, while the total capacity was calculated as 22.29, 20.07, and 17.69 mg Li/g sorbent. In the 1.0, 1.5, and 2.0 cm height columns filled with FHS, the breakthrough times were 282, 366, and 433 min, respectively, whereas the periods required for saturation were 781, 897, and 1033 min. The three conventional breakthrough models of the Thomas, Yoon-Nelson, and Modified Dose-Response (MDR) were used to properly estimate the whole breakthrough behavior of the FHS column and the characteristic model parameters. Li's extremely favorable separation utilizing FHS was evidenced by the steep S-shape of the breakthrough curves for both parameters flow rate and bed height. The reusability of FHS was demonstrated by operating the packed bed column in multi-cycle mode, with no appreciable loss in column performance.