This study evaluated the efficiency of methylene blue (MB) removal by using activated carbon produced from olive pits. The activated carbon (OPAC) was characterized by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and Brunauer–Emmett–Teller (BET). The adsorption process was optimized in two stages using factorial design. Based on the existing literature, the first stage selected the most influential variables (reaction time, dosage, pH, and dye concentration). Response surface methodology (RSM) and artificial neural network (ANN) approaches have been combined to optimize and model the adsorption of MB. To assess the optimal conditions for MB adsorption, RSM was initially applied using four controllable operating parameters. Throughout the optimization process, various independent variables were employed, including initial dye concentrations ranging from 25 to 125 mg/L, adsorbent dosages ranging from 0.1 to 0.9 g/L, pH values spanning from 1 to 9, and contact times ranging from 15 to 75 min. Moreover, the R² value (R² = 0.9804) indicates that regression can effectively forecast the response of the adsorption process within the examined range. Thermodynamic studies were performed for three different temperatures between 293 and 303 K. Isothermal analysis parameters and negative Gibbs free energy indicate that the process is spontaneous and favorable. The data best fit the Langmuir model. This research showcases the effectiveness of optimizing and predicting the color removal process through the combined RSM-ANN approach. It highlights the effectiveness of adsorption using OPAC as a viable primary treatment method for the removal of color from wastewater-containing dyes.

In this study, the effectiveness of Fe₃O₄-based clay as a cost-effective material for removing methylene blue (MB) dye from aqueous solutions was evaluated. The structural properties of the clay and Fe₃O₄-based clay were analyzed using SEM, XRF, BET, XRD, FTIR, and TGA techniques. In this research, the effects of various aspects, such as adsorbent amount, contact time, solution pH, adsorption temperature, and initial dye concentration, on the adsorption of Fe₃O₄-based clay are investigated. The experiments aimed at understanding the adsorption mechanism of Fe₃O₄-based clay have shown that the adsorption kinetics are accurately described by the pseudo-second order kinetic model, while the equilibrium data are well represented by the Langmuir isotherm model. The maximum adsorption capacity (qm) was calculated as 52.63 mg/g at 25 °C, 53.48 mg/g at 30 °C, and 54.64 mg/g at 35 °C. All variables affecting the MB adsorption process were systematically optimized in a controlled experimental framework. The effectiveness of the artificial neural network (ANN) model was refined by modifying variables such as the quantity of neurons in the latent layer, the number of inputs, and the learning rate. The model’s accuracy was assessed using the mean absolute percentage error (MAPE) for the removal and adsorption percentage output parameters. The coefficient of determination (R²) values for the dyestuff training, validation, and test sets were found to be 99.40%, 92.25%, and 96.30%, respectively. The ANN model demonstrated a mean squared error (MSE) of 0.614565 for the training data. For the validation dataset, the model recorded MSE values of 0.99406 for the training data, 0.92255 for the validation set, and 0.96302 for the test data. In conclusion, the examined Fe₃O₄-based clays offer potential as effective and cost-efficient adsorbents for purifying water containing MB dye in various industrial settings.

Occupational risk assessment involves examining and ranking the risks and hazards in a production or service facility, focusing on workplace health and safety. This study aims to address the deficiencies of traditional methods by applying a fuzzy logic approach to enhance occupational risk assessment in the metals sector. Unlike the Fine-Kinney method, the fuzzy model allows for intermediate values, reducing input uncertainties and yielding more realistic results. Probability, exposure and consequence serve as inputs, with the risk score as the output. The developed fuzzy Fine-Kinney model was tested for 252 situations and compared to the traditional Fine-Kinney method. Consistent outputs were found when using intermediate values, with risk score definitions in the fuzzy model aligning with those in Fine-Kinney. The fuzzy logic approach demonstrates an improvement in evaluating hazards by providing a more nuanced and accurate risk assessment, thereby contributing to better workplace safety management.

İşletmeler üretim faaliyetlerini sürdürebilmek için birçok tedarikçi ile çalışmak ve sürekli olarak karar vermek durumundadır. İşletmeler için verilmesi gereken en önemli kararlardan biride tedarikçi seçimidir. Satın almanın doğru, hızlı ve en düşük maliyetle gerçekleştirilmesi işletmenin rekabet gücü açısından önemlidir. İşletmeler için birçok tedarikçi arasından uygun olanların seçimi oldukça zor ve bir o kadar da zaman ve para kaybıdır. En uygun ve doğru tedarikçiyi seçme, bir birlerini etkileyen ve birbiriyle çelişen birçok bileşen içerdiğinden oldukça karmaşık bir karar verme sürecidir. Bu karmaşık süreci yönetmek, birbiriyle çelişen kriterleri karşılaştırabilmek ve uygun tedarikçiyi seçebilmek için çok kriterli karar verme yöntemleri kullanılmaktadır. Literatürde tedarikçi seçim sürecinde kullanılan çok sayıda ve farklı çok kriterli karar verme (ÇKKV) yöntemi bulunmaktadır. Bu amaçla çalışmada bir traktör firmasında tedarikçi seçimi gerçekleştirmek amacıyla, en çok kullanılan çok kriterli karar verme tekniklerinden; MOORA, COPRAS ve hedef programlama teknikleri kullanılarak işletme içindeki uzman kişilerle ve literatürde yoğun olarak kullanılan üç temel grupta on beş kriter temel alınarak, işletmedeki beş tedarikçi firma değerlendirilmiş ve her üç yönteme göre en iyi tedarikçi/tedarikçiler belirlenmiştir.

Bu çalışma, bulanık analitik hiyerarşi prosesi (AHP), bulanık hata türleri ve etkileri analizi (HTEA) ve bulanık ortalama çözüm uzaklığına göre değerlendirme (EDAS) yöntemini kombine eden bir karar verme yaklaşımı ile metalik biyomalzemeleri incelemektedir. Çalışmada, paslanmaz çelik, titanyum ve kobalt-krom alaşımları altı ana kriter, otuz bir alt kriter ve üç risk faktörü kullanılarak değerlendirilmiştir. Bulanık AHP yöntemi değerlendirme kriterlerinin ve risk faktörlerinin önemini belirlemek için kullanılırken, bulanık EDAS yöntemi bulanık HTEA yönteminden elde edilen risk öncelik katsayılarını analiz etmek için kullanılmıştır. Sonuçlara göre, ilk üç önemli kriter enfeksiyon, kanserojenlik ve çekme mukavemetidir. Malzemelerin sıralaması; titanyum > paslanmaz çelik > kobalt-krom alaşımları şeklindedir. Sonuç olarak bu çalışma, mevcut malzemelerin tarafsız değerlendirilmesi ve önceliklendirilmesi için bir temel oluşturmaktadır.

In this study, the properties of cobalt–chromium alloys are systematically prioritized to aid in the minimization of orthopedic implant failures and risks in biomedical applications. Within the model, six main groups (including a total of 31 properties) are defined: economic aspects, design and production properties, mechanical properties, physical properties, chemical properties and biological properties. A risk-based fuzzy decision making framework is proposed for prioritization. First, a risk-management decision matrix is created by employing interval type-2 fuzzy failure modes and effects analysis. Afterward, the properties of cobalt–chromium alloys are analyzed by utilizing interval type-2 fuzzy measurement of alternatives and ranking according to compromise solution. In the last phase, a prediction model is devised with an adaptive network fuzzy inference system to save computational time and effort and to enable the incorporation of new scientific results into the biomaterial evaluation process. The results of the current study demonstrate that compatibility, osseointegration, corrosion resistance, fatigue resistance and time-dependent deformation are the top five properties contributing to potential orthopedic implant failures and risks. Furthermore, the developed model produces very satisfactory results with acceptable deviations. Consequently, this study presents a new and reliable guide for the unbiased evaluation of cobalt–chromium alloys.