Yazar "Fatihah, Suja" seçeneğine göre listele

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    The effectiveness of rosehip seeds powder as a plant-based natural coagulant for sustainable treatment of steel industries wastewater
    (Desalination Publ, 2022) Abujazar, Mohammed Shadi S.; Karaagac, Sakine Ugurlu; Abu Amr, Salem S.; Fatihah, Suja; Bashir, Mohammed J. K.; Alazaiza, Motasem Y. D.; Ibrahim, Eiman
    This study aims to investigate the performance plant-based natural coagulant from rosehip seed powder in the treatment of iron and steel factory wastewater. The concentrations of COD, total suspended solids (TSS), ammonia-nitrogen (NH3-N), manganese (Mn), iron (Fe), zinc (Zn), aluminum (Al), and nickel ( Ni) in effluent wastewater were examined. Coagulation investigations were carried out using an orbital shaker and a flocculation apparatus to investigate the effects of iron and steel factory effluent, pH, and rosehip seeds powder dosage on coagulation efficacy. The rosehip powder removes a large amount of COD, TSS, NH3-N, Mn, Fe, Zn, Al, and Ni from effluent at pH 8 with percentages of 86.1%, 99%, 79%, 86%, 91.7%, 90.6%, 73.7%, and 100%, respectively, at 1 g/L. The effects of pH ranges ranging from (5-10) reveal that the wastewater sample's natural pH (8) demonstrates the maximum practicable removal effectiveness. FTIR analysis revealed the presence of numerous functional groups involved in the coagulation process. One may argue that rosehip seed powder holds great potential as a natural plant-based coagulant for water treatment and could be used to treat effluent from iron and ste el factories.
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    Factorial design and optimization of pinecone seed powder as a natural coagulant for organic and heavy metals removal from industrial wastewater
    (Desalination Publ, 2023) Abujazar, Mohammed Shadi S.; Karaagac, Sakine Ugurlu; Abu Amr, Salem S.; Fatihah, Suja; Bashir, Mohammed J. K.; Alazaiza, Motasem Y. D.; Yusof, Arij
    Various chemical coagulants have previously been used for wastewater treatment with substantial efficacy in eliminating heavy metals and other criteria. However, their economic effectiveness and the remnant of harmful chemical precipitates that pose hazards to human health and the environment. As a result, utilizing plant-based natural coagulants is seen as an alternative non-toxic, biodegradable, and ecologically beneficial strategy. This study aims to investigate the performance of pinecone seed powder as a natural coagulant in iron and steel factory wastewater treatment, as well as to optimize the operating parameters to determine the feasibility of employing pinecone seed powder in wastewater treatment. Using 0.6 g/200 mL pinecone as a controlling factor, pH, and settling time, the response surface methodology, a statistical experimental design was utilized to increase the chemical oxygen demand (COD), ammoniacal nitrogen (NH3-N), and heavy metals removal effimodels for the parameters specified were determined to be significant with a low probability.
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    Productivity modelling of an inclined stepped solar still for seawater desalination using boosting algorithms based on experimental data
    (Desalination Publ, 2022) Wazirali, Raniyah; Abujazar, Mohammed Shadi S.; Abujayyab, Sohaib K. M.; Ahmad, Rami; Fatihah, Suja; Kabeel, A. E.; Karaagac, Sakine Ugurlu
    Solar energy has recently become a viable option for desalinating seawater, primarily in arid regions. However, increasing the productivity of solar still by integrating experimental base and modelling methods is still subject to prediction errors; therefore, the main objective of this research is to postulate and test boosting algorithms for predicting the efficiency and productivity of the system. Five boosting regressors were deployed and evaluated: categorical boosting, adaptive boosting, extreme gradient boosting, gradient boosting machine, and gradient boosting machine (LightGBM). The proposed regressors are implemented based on the system's actual recorded dataset (consisting of 720 observations). The dataset consists of input variables, which are the wind speed (V), cloud cover, humidity, ambient temperature (T), solar radiation (SR), (T-io), (T-w), (T-v), and (T-t). Also, the output variable is represented by the productivity of the system. The dataset was separated into training (70%) and testing (30%) sets. In order to decrease regressors errors, hyperparameter optimization was employed. GradientBoosting approach provided the best prediction, with 95% R-2 accuracy and 39.57 root mean square error (RMSE) error. The LightGBM technique achieved 94% R-2 accuracy and 40.07 RMSE error in the testing dataset. The results reveal that GradientBoosting outperforms the cascaded forward neural network in predicting system productivity (CFNN).