Tümer, Abdullah Erdal2025-01-212025-01-2120201308-5514https://dergipark.org.tr/tr/download/article-file/947730https://dergipark.org.tr/tr/pub/umagd/issue/49089/472269https://doi.org/10.29137/umagd.472269https://hdl.handle.net/20.500.12587/19305The artificial neural network-based model wasdeveloped to predict the sorption capacity and removal efficiency of calixarenefor Cr(VI) in aqueous solutions. The input variables were initial concentrationof Cr(VI), adsorbent dosage, contact time, and pH, while the sorption capacityand the removal efficiency were considered as output. They have been used forthe training and simulation of the network in the current work. The trainingresults were tested using the input data (simulated data) that were not shownto the network. According to the indicator, the optimum and most reliable modelwas found based on the test results.eninfo:eu-repo/semantics/openAccessArtificial Neural NetworkModelingSorptionRemoval EfficiencySorption CapacityArticle11-132010.29137/umagd.472269472269