Optimal 6E design of an integrated solar energy-driven polygeneration and CO2 capture system: A machine learning approach

Renewable energy-driven decentralized polygeneration systems herald great potential in tackling climate change issues and promoting sustainable development. In this light, this study introduces a new machine learning-based multi-objective optimization approach of an integrated solar energy-driven polygeneration and CO2 capture system for meeting a greenhouse’s power, freshwater, and CO2 demands. The integrated solar-assisted polygeneration system comprises a 486-kW gas turbine, two steam turbines, two organic Rankine cycles, a humidification-dehumidification desalination unit to recover waste heat while producing freshwater, and a post-combustion CO2 capture unit. The proposed system is mathematically modelled and evaluated via a dynamic simulation approach implemented in MATLAB software. Moreover, sensitivity analysis is conducted to identify the most influential decision variables on the system performance. The machine learning-based multi-objective optimization strategy combines Genetic Programming (GP) and Artificial Neural Networks (ANN) to minimize total costs, environmental impacts, and economic and environmental emergy rates whilst maximizing the system exergy efficiency and freshwater production. Finally, the system performance is further investigated through comprehensive Energy, Exergy, Exergoeconomic, Exergoenvironmental, Emergoeconomic, and Emergoenvironmental (6E) analyses. The three-objective optimization of the integrated system reduces total costs, environmental impacts, and monthly environmental emergy rate by 11.4%, 34.31% and 6.38%, respectively. Furthermore, reductions up to 56.81%, 50.19% and 77.07%, respectively, are obtained for the previous indicators by the four-objective optimization model. Hence, the proposed multi-objective optimization methodology represents a valuable tool for decision-makers in implementing more cost-effective and environment-friendly solar-assisted integrated polygeneration and CO2 capture systems

Assessment of Radiation Dose to the Lens of the Eye and Thyroid of Patients Undergoing Head and Neck Computed Tomography at Five Hospitals in Mashhad, Iran

Introduction: In recent years, the number of computed tomography (CT) scans, which is a high-dose technique, has increased significantly. Head and neck CT is performed frequently and thyroid, particularly in children, has always been considered a sensitive organ. In recent years, radiobiologists and health physicists have been more concerned about the safety of lenses of the eyes, as cataract is no longer considered a deterministic effect. Material and Methods: In the present study, incurred doses to the thyroid and lens of the eye of 140 patients who underwent common head and neck CT at five hospitals were measured by thermoluminescent dosimeters (TLD-100). The patients were divided into two age groups of pediatrics and adults. TLD chips were placed on the patient’s skin surface. For each patient, scan parameters, sex and age were recorded. Exposed TLDs were read by a manual TLD reader. Results: The verage absorbed dose of the thyroid, as well as the lenses of the left and right eyes were 5.89±1.74, 15.84±2.81 and 16.25±2.57, respectively, for the pediatric patients and 5.00±1.17, 17.64±1.69 and 24.41±1.89 for adults. Patient-specific organ doses were influenced by the scanned region, scan protocol and patient's age. Conclusion: In the present study, the mean eye dose was much lower than the 500 mGy threshold recommended by International Commission on Radiological Protection (ICRP) for lens of the eye damage, thus, it appears to be clinically safe. While CT scan remains a crucial tool, further dose reduction can be achieved by controlling different factors affecting patient doses