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    An Artificial Neural Network (ANN) Modelling Approach for Evaluating Turbidity Properties of Paper Recycling Wastewater
    (North Carolina State Univ Dept Wood & Paper Sci, 2024) Kardes, Serkan; Ozkan, Ugur; Bayram, Okan; Sahin, Halil Turgut
    A pre-treatment process was evaluated in this work for wastewater from precipitation of contaminants through centrifugation. Artificial neural networks (ANNs) were used to analyze and optimize the turbidity values. Thirty experimental runs were utilized including microwave (MW) power, duration, centrifuge time, and centrifuge speed as input variables, generated by the Central Composite Full Design (CCFD) approach. The experimental turbidity ranged from 8.1 to 19.7 NTU, while predicted values ranged from 8.4 to 19.7 NTU by ANN. The ANN model showed a robust prediction performance with low mean squared error values during training and testing. Moreover, high R2 values showed a remarkable agreement between the experimental observations and ANN predictions. The results obtained from the input values (A:150.00, B:60.00, C:15.00, D:30.00) of sample 2, which gave the lowest turbidity value, showed the most removal of pollution. The results obtained from the input values (A:250.00, B:60.00, C:7.00, D:20.00) of sample 30, which gave the highest turbidity value, showed the least removal of pollution. The results showed that increasing RPM and time of the centrifugation process significantly affected the removal of pollution in wastewater.
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    Machine Learning-Based Modeling of Methyl Blue Adsorptive Removal from Aqueous Solutions with Lignin
    (Wiley-V C H Verlag Gmbh, 2025) Bayram, Okan; Ozkan, Ugur; Kardes, Serkan; Sahin, Halil Turgut
    Dyes are chemical compounds extensively utilized in numerous industries such as textile, paper, leather, and cosmetics. These substances can cause serious environmental problems by mixing into wastewater during production processes. Methyl blue (MB), which is an anionic dye, has a wide range of applications and poses serious ecological and health risks if discharged untreated into water bodies. In this study, the adsorption process of MB removal using lignin was explained by examining the parameters of temperature, contact time, pH, initial MB dye concentration and initial lignin amount. In addition, lignin was characterized by FT-IR, SEM-EDS, XRD, Zeta potential, DSC, and BET. Then, the results obtained by artificial neural networks (ANN) and support vector regression (SVR) methods were modeled. The analysis revealed that the process is endothermic, follows a pseudo-second-order (PSO) kinetic model, and conforms to the Langmuir isotherm model, with a maximum adsorption capacity (q(max)) of 175.439 +/- 8.772 mg/g, R-2 = 96.200% for ANN and R-2 = 93.500% for SVR. The results obtained showed that lignin can be used for MB removal and that it would be suitable for use in machine learning algorithms.
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    Optimization of dissolved oxygen in the removal of wastewater generated in a sawmill using response surface methodology (RSM)
    (2024) Özkan, Uğur; Serkan, Kardeş; Cambazoğlu, Merve; Sahin, Halil Turgut
    This study aims to optimize dissolved oxygen levels in treating wastewater generated during the sawmill drying process, using response surface methodology (RSM). The experimental approach, employing three independent variables centrifuge time, centrifuge RPM, and microwave power, evaluates their impact on the effectiveness of wastewater treatment based on dissolved oxygen levels. Parameter ranges are set at 5-20 minutes for centrifuge time, 15-35 for centrifuge RPM, and 100-250 Watts for microwave power. Optimization results reveal the highest dissolved oxygen value with a centrifuge time of 20.00 minutes, centrifuge RPM of 35.00, and microwave power of 100.00 Watts, yielding a maximum value of 9.85 mg/L. ANOVA analysis of the obtained data confirms the compatibility of the proposed model with experimental results (p<0.05), with R2 and R2 (adj) values calculated at 98.53% and 95.90%, respectively. These findings authenticate the reliability of the proposed model and its alignment with experimental data. In addition, the Lack of fit value obtained as a result of ANOVA analysis was found to be 0.075. Ultimately, response surface methodology (RSM) demonstrates potential contributions to optimizing dissolved oxygen in wastewater treatment experiments.