| 1. |
- Homod, R. Z., et al.
(author)
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Crude oil production prediction based on an intelligent hybrid modelling structure generated by using the clustering algorithm in big data
- 2023
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In: Geoenergy Science and Engineering. - : Elsevier. - 2949-8910. ; 225
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Journal article (peer-reviewed)abstract
- Since the behavior of a complex dynamic system for a large oil field in Iraq is significantly influenced by many nonlinearities, its dependent parameters exhibit non-stationary with a very high delay time. Developing white-box modelling approaches for such dynamic oil well production cannot handle these large data sets with all dependent dimensions and their non-linear effects. Therefore, this study adopts the hybrid model that combines white-box and black-box to address such problems because the model outputs require various variable types to achieve optimal fitness to measured values. The hybrid model structure needs to evolve with changes in the physical parameters (white-box part) and Neural Networks' Weights (black-box part). The model structure of the proposed hybrid network relied on converting fuzzy rules in a Takagi–Sugeno–Kang Fuzzy System (TSK-FS) into a multilayer perceptron network (MLP). The hybrid parameters are formulated concerning six-dimensional dependent variables to describe them in matrix form or layer and by which can quantify total model outputs. After mapping categorical variables to tuples of MLP, the Gauss-Newton regression (GNR) provides an optimal update of the hybrid parameters to get the best fitting of the model outputs with the target of the dataset. The clustering technique and GNR promote predictive performance due to reducing uncertainties in the hybrid parameters. Due to time being the most effective of the independent variables for predicting oil production, datasets are classified into different clusters based on time. The actual field dataset for training and validation is collected from Zubair Oil Field (9 oil wells), which is implemented to build the proposed model. The results of the hybrid model indicate that the development of the proposed structure has achieved the high capability to represent such big data which is the most imperative feature of the proposed model. Furthermore, obtained results show its accuracy far outpacing competitors and achieving a significant improvement in predictive performance.
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| 2. |
- Tao, Hai, et al.
(author)
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Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters
- 2023
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In: Environment International. - : Elsevier. - 0160-4120 .- 1873-6750. ; 175
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Journal article (peer-reviewed)abstract
- This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.
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| 3. |
- Alawi, Omer A., et al.
(author)
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Thermohydraulic performance of thermal system integrated with twisted turbulator inserts using ternary hybrid nanofluids
- 2023
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In: Nanotechnology Reviews. - : Walter de Gruyter. - 2191-9089 .- 2191-9097. ; 12:1
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Journal article (peer-reviewed)abstract
- Mono, hybrid, and ternary nanofluids were tested inside the plain and twisted-tape pipes using k-omega shear stress transport turbulence models. The Reynolds number was 5,000 ≤ Re ≤ 15,000, and thermophysical properties were calculated under the condition of 303 K. Single nanofluids (Al2O3/distilled water [DW], SiO2/DW, and ZnO/DW), hybrid nanofluids (SiO2 + Al2O3/DW, SiO2 + ZnO/DW, and ZnO + Al2O3/DW) in the mixture ratio of 80:20, and ternary nanofluids (SiO2 + Al2O3 + ZnO/DW) in the mixture ratio of 60:20:20 were estimated in different volumetric concentrations (1, 2, 3, and 4%). The twisted pipe had a higher outlet temperature than the plain pipe, while SiO2/DW had a lower Tout value with 310.933 K (plain pipe) and 313.842 K (twisted pipe) at Re = 9,000. The thermal system gained better energy using ZnO/DW with 6178.060 W (plain pipe) and 8426.474 W (twisted pipe). Furthermore, using SiO2/DW at Re = 9,000, heat transfer improved by 18.017% (plain pipe) and 21.007% (twisted pipe). At Re = 900, the pressure in plain and twisted pipes employing SiO2/DW reduced by 167.114 and 166.994%, respectively. In general, the thermohydraulic performance of DW and nanofluids was superior to one. Meanwhile, with Re = 15,000, DW had a higher value of η Thermohydraulic = 1.678
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| 4. |
- Assini, J. M., et al.
(author)
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High levels of lipoprotein(a) in transgenic mice exacerbate atherosclerosis and promote vulnerable plaque features in a sex-specific manner
- 2023
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In: Atherosclerosis. - 0021-9150. ; 384
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Journal article (peer-reviewed)abstract
- Background and aims: Despite increased clinical interest in lipoprotein(a) (Lp(a)), many questions remain about the molecular mechanisms by which it contributes to atherosclerotic cardiovascular disease. Existing murine transgenic (Tg) Lp(a) models are limited by low plasma levels of Lp(a) and have not consistently shown a pro atherosclerotic effect of Lp(a). Methods: We generated Tg mice expressing both human apolipoprotein(a) (apo(a)) and human apoB-100, with pathogenic levels of plasma Lp(a) (range 87-250 mg/dL). Female and male Lp(a) Tg mice (Tg(LPA+/0;APOB+/0)) and human apoB-100-only controls (Tg(APOB+/0)) (n = 10-13/group) were fed a high-fat, high-cholesterol diet for 12 weeks, with Ldlr knocked down using an antisense oligonucleotide. FPLC was used to characterize plasma lipoprotein profiles. Plaque area and necrotic core size were quantified and immunohistochemical assessment of lesions using a variety of cellular and protein markers was performed. Results: Male and female Tg(LPA+/0;APOB+/0) and Tg(APOB+/0) mice exhibited proatherogenic lipoprotein profiles with increased cholesterol-rich VLDL and LDL-sized particles and no difference in plasma total cholesterol between genotypes. Complex lesions developed in the aortic sinus of all mice. Plaque area (+22%), necrotic core size (+25%), and calcified area (+65%) were all significantly increased in female Tg(LPA+/0;APOB+/0) mice compared to female Tg(APOB+/0) mice. Immunohistochemistry of lesions demonstrated that apo(a) deposited in a similar pattern as apoB-100 in Tg(LPA+/0;APOB+/0) mice. Furthermore, female Tg(LPA+/0;APOB+/0) mice exhibited less organized collagen deposition as well as 42% higher staining for oxidized phospholipids (OxPL) compared to female Tg(APOB+/0) mice. Tg(LPA+/0;APOB+/0) mice had dramatically higher levels of plasma OxPL-apo(a) and OxPL-apoB compared to Tg(APOB+/0) mice, and female Tg(LPA+/0;APOB+/0) mice had higher plasma levels of the proinflammatory cytokine MCP-1 (+3.1-fold) compared to female Tg(APOB+/0) mice. Conclusions: These data suggest a pro-inflammatory phenotype exhibited by female Tg mice expressing Lp(a) that appears to contribute to the development of more severe lesions with greater vulnerable features.
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| 5. |
- Halder, Bijay, et al.
(author)
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Machine learning-based country-level annual air pollutants exploration using Sentinel-5P and Google Earth Engine
- 2023
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In: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13
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Journal article (peer-reviewed)abstract
- Climatic condition is triggering human health emergencies and earth’s surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth’s health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human’s health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50–60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.
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| 6. |
- Homod, Raad Z., et al.
(author)
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Deep clustering of cooperative multi-agent reinforcement learning to optimize multi chiller HVAC systems for smart buildings energy management
- 2023
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In: Journal of Building Engineering. - : Elsevier. - 2352-7102. ; 65
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Journal article (peer-reviewed)abstract
- Chillers are responsible for almost half of the total energy demand in buildings. Hence, the obligation of control systems of multi-chiller due to changes indoor environments is one of the most significant parts of a smart building. Such a controller is described as a nonlinear and multi-objective algorithm, and its fabrication is crucial to achieving the optimal balance between indoor thermal comfort and running a minimum number of chillers. This work proposes deep clustering of cooperative multi-agent reinforcement learning (DCCMARL) as well-suited to such system control, which supports centralized control by learning of agents. In MARL, since the learning of agents is based on discrete sets of actions and stats, this drawback significantly affects the model of agents for representing their actions with efficient performance. This drawback becomes considerably worse when increasing the number of agents, due to the increased complexity of solving MARL, which makes modeling policy very challenging. Therefore, the DCCMARL of multi-objective reinforcement learning is leveraging powerful frameworks of a hybrid clustering algorithm to deal with complexity and uncertainty, which is a critical factor that influences to the achievement of high levels of a performance action. The results showed that the ability of agents to manipulate the behavior of the smart building could improve indoor thermal conditions, as well as save energy up to 44.5% compared to conventional methods. It seems reasonable to conclude that agents' performance is influenced by what type of model structure.
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| 7. |
- Tao, Hai, et al.
(author)
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Influence of water based binary composite nanofluids on thermal performance of solar thermal technologies: sustainability assessments
- 2023
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In: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 17:1
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Journal article (peer-reviewed)abstract
- Recent technological advances have made it possible to produce particles with nanometer dimensions that are uniformly and steadily suspended in traditional solar liquids and have enhanced the impact of thermo-physical parameters. In this research, a three-dimensional flat plate solar collector was built using a thin flat plate and a single working fluid pipe. The physical model was solved computationally under conditions of conjugated laminar forced convection in the range 500 ≤ Re ≤ 1900 and a heat flux of 1000 W/m2. Distilled water (DW) and different types of hybrid nanofluids (namely, 0.1%-Al2O3@Cu/DW, 0.1%-MWCNTs@Fe3O4/DW, 0.3%-MWCNTs@Fe3O4/DW, 0.5%-Ag@MgO/DW, 1%-Ag@MgO/DW, 1%-S1 and 1%-S2, where MWCNTs are multi-wall carbon nanotubes, S1 means 2CuO–1Cu and S2 means 1CuO–2Cu nanocomposites) were evaluated via a set of parameters. The numerical results revealed that, by increasing the working fluid velocity (the Reynolds number), the average heat transfer coefficient, pressure loss, heat gain and solar collector efficiency were increased. Meanwhile, outlet fluid temperature and flat plate surface temperature were decreased. At Re = 1900, 1%-S2 and 1%-S1 presented higher thermal performance enhancement by 44.28% and 36.72% relative to DW. Moreover, low thermal performance enhancement of 7.59% and 7.44% were reported by 0.1%-Al2O3@Cu/DW and 0.3%-MWCNTs@Fe3O4/DW, respectively.
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