ABSTRACT
Meeting the energy and water demands of remote areas has created significant challenges globally. To address this issue, the utilization of hybrid energy-water systems, integrated with renewable energies, has been highlighted as a viable solution. This work has been focused on the multi-objective optimization of a hybrid energy system, encompassing photovoltaic panels, batteries, diesel generators, and desalination units. The design goals to achieve the optimal configuration include minimizing system costs, reducing carbon dioxide emissions, enhancing the renewable factor, and improving reliability. Also, for the mentioned design goals, the performance of three desalination methods including reverse osmosis (RO), multi-stage flash (MSF), and multiple-effect distillation (MED) was evaluated by Hybrid Optimization of Multiple Energy Resources (HOMER) software. Our findings reveal that the RO desalination method, when combined with renewable energy, outperforms other methods both economically and environmentally. Notably, the RO method reduces net present cost (NPC) by 6.18% and 8.25% and carbon dioxide emissions by 38% and 46%, respectively, compared to MED and MSF methods. Additionally, sensitivity analysis, considering factors such as interest rate, photovoltaic panel cost, battery cost, and fuel cost, was conducted on NPC. The results showed that with a 2% decrease in the interest rate, the amount of NPC increases by about 2.4% due to the increase in the share of renewable energy. Therefore, reducing the interest rate helps to design a system with less carbon dioxide emissions. This work, by highlighting the economic and environmental implications of different desalination methods, as well as key cost factors, contributes to the optimal design of combined energy-water schemes for remote areas.
Subject(s)
Water Purification , Water Purification/methods , Electric Power Supplies , Renewable Energy , Carbon DioxideABSTRACT
Polyethylene as a thermoplastic has received the uppermost popularity in a vast variety of applied contexts. Polyethylene is produced by several commercially obtainable technologies. Since Ziegler-Natta catalysts generate polyolefin with broad molecular weight and copolymer composition distributions, this type of model was utilized to simulate the polymerization procedure. The EIX (ethylene index) is the critical controlling variable that indicates product characteristics. Since it is difficult to measure the EIX, estimation is a problem causing the greatest challenges in the applicability of production. To resolve such problems, ANNs (artificial neural networks) are utilized in the present paper to predict the EIX from some simply computed variables of the system. In fact, the EIX is calculated as a function of pressure, ethylene flow, hydrogen flow, 1-butane flow, catalyst flow, and TEA (triethylaluminium) flow. The estimation was accomplished via the Multi-Layer Perceptron, Radial Basis, Cascade Feed-forward, and Generalized Regression Neural Networks. According to the results, the superior performance of the Multi-Layer Perceptron model than other ANN models was clearly demonstrated. Based on our findings, this model can predict production levels with R2 (regression coefficient), MSE (mean square error), AARD% (average absolute relative deviation percent), and RMSE (root mean square error) of, respectively, 0.89413, 0.02217, 0.4213, and 0.1489.
ABSTRACT
Resting electrocardioghic (ECG) abnormalities might be value for mortality prediction. The aim of this study is to evaluate whether ECG abnormalities are associated with increased mortality in older residents of Kahrizak Charity Foundation (KCF). A total of 247 participants ≥60-years of KES were enrolled in this study. Adjudicated all cause mortality was collected over 3 years between 2006 and 2009. The subjects were classified as having major, minor or no ECG abnormalities according to the Minnesota Code. The addition of ECG to risk factors were examined to predict cardiovascular diseases (CVD) and all-cause mortality by using Cox proportional hazards regression models. At baseline, 104(42.1%) had major ECG abnormalities and 73(29.6%) had minor abnormalities. During a median follow-up of 3.2 years, 73 participants died from all-cause mortality and 31 deaths from CVD. Major ECG abnormalities were associated with an increased risk of CVD mortality in all models. The associations between minor ECG abnormalities at baseline and CVD mortality were not statistically significant. After adjustment for age and sex, Body mass index (BMI), smoking, diabetes, hypertension (HTN), hyperlipidemia and history of CVD, the participants with the major ECG abnormalities had higher risks of CVD mortality (HR: 3.12(95% CI, 1.02-9.57) and all-cause mortality (HR: 2.45(95% CI, 1.23-4.85) compared with those with normal ECG.
