Browsing by Author "Hancerliogullari, A."

Now showing 1 - 3 of 3

Assessment of Human Health Risk Caused by Heavy Metals in Kiln Dust from Coal-Fired Clay Brick Factories in Türkiye
(2024.01.01) Turhan, S.; Altuner, E.M.; Bakir, T.K.; Duran, C.; Hancerliogullari, A.; Kurnaz, A.
Heavy metal (HM) pollution from natural processes and different anthropogenic activities pose significant human and environmental health risks because of their stability, non-degradable properties, and high toxicity. HM released into the air in the form of dust can enter the human body via ingestion, inhalation, and dermal contact. Keeping in mind the significance of estimating the risk from HM in different environments, the carcinogenic and non-carcinogenic health risks to workers caused by HM in kiln dust (KD) samples collected from coal-fired clay brick factories in the Western Black Sea Region of T & uuml;rkiye were assessed for the first time in this study. The concentrations of major and minor oxides and HMs in the collected KD samples were analyzed using energy-dispersive X-ray fluorescence spectrometry. The average concentrations of Fe, Mn, Cr, Ni, V, Zn, Cu, As, Co and Pb analyzed in thirty-three KD samples were determined as 65444, 768, 251, 249, 248, 122, 60, 52, 42 and 16 mg/kg dw, respectively. The average levels of Ni, As, Cr, Co and Cu exceed the maximum contaminant levels recommended in the Turkish Regulation on Control of Soil Pollution. Carcinogenic and non-carcinogenic human health risk assessments for workers via three exposure pathways were carried out, estimating the hazard index (HI) and total carcinogenic risk (TCR) index, respectively. The HI values (< 1) revealed no possible non-carcinogenic health risk due to exposure to all HMs in the studied KD samples. The average TCR value revealed that the potential cancer risks for Ni, As, Cr, and Pb were slightly above the safe limit and required monitoring and further investigation for these HMs.
Battery Charge Control in Solar Photovoltaic Systems Based on Fuzzy Logic and Jellyfish Optimization Algorithm
(2023.01.01) Agoub, R.A.A.; Hancerliogullari, A.; Rahebi, J.; Lopez-Guede, J.M.
The study focuses on the integration of a fuzzy logic-based Maximum Power Point Tracking (MPPT) system, an optimized proportional Integral-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This integrated approach aims to enhance energy harvesting efficiency under varying environmental conditions. The study's innovation lies in effectively addressing challenges posed by diverse environmental factors and loads. The utilization of MATLAB 2022a Simulink for modeling and the Jellyfish Optimization Algorithm for PI-controller tuning further strengthens our findings. Testing scenarios, including constant and variable irradiation, underscore the significant enhancements achieved through the integration of fuzzy MPPT and the Jellyfish Optimization Algorithm with the PI-based voltage controller. These enhancements encompass improved power extraction, optimized voltage regulation, swift settling times, and overall efficiency gains.
Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model
(2023.01.01) Abusida, A.M.; Karatay, S.; Rezaeizadeh, R.; Hancerliogullari, A.
The main goal of this study is to compare the performance of the classification algorithms applied to the SCADA database of the Supervisory Control and Data Acquisition (SCADA) system of the General Electricity Company of Libya (GECOL). The company's annual energy and material losses have become seriously important to the Libyan government's research field. The well-established data mining and classification software package known as the WEKA tool is used to minimize these losses,. As necessary data input for algorithms; six different parameters are taken into consideration, namely power production size, energy production duration, energy production date, ambient temperature, humidity level and wind speed. This study is examined in detail for the first time in this article. In addition, considering the temperature variables, humidity, wind and other atmospheric effects of the environment, the energy losses of the company and the country are reduced to a minimum level. As a result, the company's annual electricity consumption is classified as low, medium or high consumption with the simulations. Thus, in cases where energy consumption is high, it is possible to make accurate and rapid decisions regarding the determination and classification of time periods related to energy consumption.