Chemical Engineering / Kimya Mühendisliği

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  • 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, Sacide
    Nanocomposite 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.
  • Book Part
    Citation - Scopus: 2
    Data Driven Leak Detection in a Real Heat Exchanger in an Oil Refinery
    (Elsevier, 2023) Yasmal, Aslı; Kuşoğlu Kaya, Gizem; Oktay, Emirhan; Çölmekci, Ceylan; Uzunlar, Erdal
    This study focuses on implementation of a data-based leak detection method in a heat exchanger in a petroleum refinery. We have studied on the two real leakage cases in a heat exchanger in Izmit TUPRAS Refinery. Leaks are one of the major problems that occur in operations. The autoencoder (AE) method is implemented for leak detection. Reconstruction error is used as the leak indicator. In case of leakage, the reconstruction value is expected to increase. For both cases examined, the reconstruction error is found to be around 1-5 under normal operating conditions. On the other hand, reconstruction error is observed to change between 10 and 60 under the conditions with leakage. Besides, the AE is able to indicate the start of one leakage case before the process engineers noticed it. © 2023 Elsevier B.V.
  • Book Part
    Citation - Scopus: 32
    Language of Response Surface Methodology as an Experimental Strategy for Electrochemical Wastewater Treatment Process Optimization
    (Elsevier, 2022) Gören, Ayşegül Yağmur; Recepoğlu, Yaşar Kemal; Khataee, Alireza
    The availability and accessibility to safe and secure water resources are the key technological and scientific concerns of global significance. As a result of water scarcity worldwide, wastewater treatment and reuse are considered viable options to replace freshwater resources in agricultural irrigation and domestic and industrial purposes. A significant need for clean water has promoted the invention and/or enhancement of several electrochemical wastewater treatment (EWT) processes. Optimization of the process variables plays a crucial role in wastewater treatment to enhance technology performance, considering removal efficiency, operating cost, and environmental impacts. These processes are fundamentally complex multivariable, and the optimization through conventional methods is unreliable, inflexible, and time- and material-consuming. In this perspective, response surface methodology (RSM) appears to be a beneficial statistical experimental strategy for the performance optimization of the EWT process. This model could be utilized for the optimization and analysis of the individual and/or combined effects of operational variables on the treatment process to improve the system performance. Furthermore, this model provides a number of information from a slight number of experimental trials. In this chapter, a summary and a discussion are presented on the RSM model used in the electrochemical wastewater treatment processes to overcome process crucial challenges toward the optimization and modeling of process parameters. It provides a potential model to enhance the various types of wastewater treatment process performance with effective optimization. Overall, it is described that the RSM model can be used in EWT processes to find the optimum conditions.