Optimization of Simple Solar Still Performance Using Fuzzy Logic Control

Ahmed, Anmmar Mahmoud; Ahmed, Ahmed H.; Daoud, Raid W.; Ahmed, Omer K.

doi:10.1109/iec49899.2020.9122819

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…The optimal productivity is achieved at a water depth of 3 cm in the distillation basin, recording a yield of 0.39 kg at 2 p.m. This finding is consistent with previous research outcomes, as documented in studies [34,35], which assert that a reduction in water depth enhances water productivity, attributing this to the lower heat capacity of shallower depths in comparison to their deeper counterparts. For the solar still connected to the PV/T solar collector, the productivity at water depths of 7 and 10 cm is observed to be inferior relative to other depths.…”

Section: Temperatures Of the Systemsupporting

confidence: 90%

Comparative Evaluation of a Conventional and Photovoltaic/Thermal-Integrated Solar Distiller under Iraqi Climatic Conditions

Jasim,

Ahmed

2023

JESA

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The integration of photovoltaic/thermal (PV/T) collectors with solar distillation processes is explored in this investigation, which seeks to augment the efficiency of solar distillers. Development and experimental assessment of a novel solar system, employing a PV/T collector for performance enhancement of a solar distiller, were carried out in Kirkuk City, Iraq, located at 35.46˚N latitude and 44.38˚E longitude. Two experimental models were constructed: a conventional double-slope solar distiller served as the baseline, whereas the second model was a double-slope solar distiller integrated with a PV/T collector. Four water depths (3, 4, 7, and 10 cm) were investigated to ascertain their effects on the system's output and efficiency. It was observed that the integration of the solar distiller with the PV/T collector resulted in a significant enhancement in system efficiency, achieving 18.81%, compared to a 5.15% efficiency demonstrated by the conventional distiller. The study further confirmed the substantial influence of water depth in the basin on the performance of both the hybrid and traditional solar distillation systems. Optimal results were achieved with a water depth of 3 cm, yielding productivity of 1.55 and 0.447 litre/day, and efficiency of 18.81% and 5.15%, for the integrated and conventional systems, respectively. When the water depth was increased to 5 cm, the respective productivity decreased to 1.252 and 0.243 litre/day, with corresponding efficiency of 16.87% and 3.22%.

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Section: Temperatures Of the Systemsupporting

confidence: 90%

Comparative Evaluation of a Conventional and Photovoltaic/Thermal-Integrated Solar Distiller under Iraqi Climatic Conditions

Jasim,

Ahmed

2023

JESA

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“…The model's effectiveness is determined by the accuracy of the diabetic data provided; as a result, the researcher must submit credible data to the classifier for the disease to be predicted accurately [8]. In the medical area, fuzzy logic (FL) algorithms are well-suited to handle ambiguity and uncertainty in large datasets that are ideal for decision-making in diabetes diagnosis [9]. FL's adjustment constraint during the learning process was solved by introducing adaptive neuro-fuzzy inference system (ANFIS), which combines the benefits of fuzzy control interpolation with adaptability through neural network backpropagation [10].…”

Section: Introductionmentioning

confidence: 99%

A proposed approach for diabetes diagnosis using neuro-fuzzy technique

et al. 2022

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Diabetes is a chronic disease characterized by a decrease in pancreatic insulin production. The immune system will be harmed due to this condition, which will raise blood sugar levels. However, early detection of diabetes enables patients to begin treatment on time, therefore reducing or eliminating the risk of severe consequences. One of the most significant challenges in the healthcare unit is disease diagnosis. Traditional techniques of disease diagnosis are manual and prone to inaccuracy. This paper proposed an approach for diagnosing diabetes using the adaptive neuro-fuzzy inference system (ANFIS) based on Pima Indians diabetes dataset (PIDD). The three stages of the proposed approach are pre-processing classification and evaluation. Normalization, imputation, and anomaly detection are part of the pre-processing stage. The pre-processing was done by normalizing the data, replacing the missing values, and using the local outlier factor (LOF) technique. In the classification stage, ANFIS classifiers were trained using the hybrid learning algorithm of the neural network. Finally, the evaluation procedures use the last stage’s sensitivity, specificity, and accuracy metrics. The obtained classification accuracy was 92.77%, and it seemed rather promising compared to the other classification applications for this topic found in the literature.

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