New Model For Estimation Research Articles

Development and comparison of different artificial intelligence-based models for viscosity of in-situ cross-linked acids

The use of in-situ cross-linked acids (ICAs) is common in stimulating oil and gas reservoirs. However, their rheological models have not been widely incorporated into simulation software due to their complex behavior and lack of valid data. Accordingly, in this study, development of new models for estimation of viscosity of ICAs as a function of pH and shear rate was intended. For this purpose, 33 experimental data points were collected from literature and methods of genetic programming, neural network (NN), and fuzzy logic (FL) were used to develop models. In summary, all three models performed equally well and better than a previously published model, with an R 2 = 0.99 and an average absolute percent error of 7%. In terms of computational costs, genetic programming correlation was found to be 76 and 786% faster than NN and FL, respectively. Therefore, the model developed by genetic programming was suggested to be used in numerical solvers for estimation the viscosity of ICAs as a function of pH and shear. Sensitivity analysis on temperature, pH, and shear rate showed that pH would have the highest impact on the apparent viscosity of the considered ICAs.

Lagrangian characteristics in the western North Pacific help to explain variability in Pacific saury fishery

A new model for estimation of daily probability for the Pacific saury (Cololabis saira) encounter was proposed. The model performance was tested for the period of 2004–2018 (August–November) using the data from the Russian vessel monitoring system. The following physical oceanographic variables were used for encounter probability prediction: the absolute values and gradients (∇) of speed (V) of passive particles, imitating water parcels, and Lagrangian indicators. The positive effects on the encounter probability of saury were found for V, ∇V, and for the gradient of the finite-time Lyapunov exponent (∇Λ), while the effect of particle path length was negative. That means that saury preferred places close to the boundaries of the oceanographic features, where Lagrangian fronts are situated, but not inside the features themselves, because Λ is small in regular flows and large at Lagrangiam fronts. The model did not include information about years and volume of saury catches, but its monthly mean of catch probability in September had the highest correlation with Russian annual catches outside the national waters between Russia and Japan (r = 0.76, p = 0.001) and total annual catches there (r = 0.73, p = 0.002). Timeseries analysis of principle components (PC) from daily predictions of saury catch probabilities has also shown that the third PC correlated highly with the annual biomass of saury (r ≥ 0.8, p < 0.05). The model seems to be useful to manage Russian fishery and may help to explain the reasons for the saury biomass decline. The latter is very important to take into account for development of the stock assessment models.

Proposing a New Model for Estimation of Oil Rate Passing Through Wellhead Chokes in an Iranian Heavy Oil Field

Proposing a New Model for Estimation of Oil Rate Passing Through Wellhead Chokes in an Iranian Heavy Oil Field

A New Model for Estimation of Energy Extraction from Bifacial Photovoltaic Modules

The energy yield from bifacial solar photovoltaic (PV) systems can be enhanced by optimizing the tilt angle. Bifacial modules boost the energy yield by 4% to 15% depending on the module type and ground reflectivity with an average of 9%. The selection of tilt angle depends on several factors, including the geographical location, weather variation, etc. Compared to the variable tilt angle, a constant angle is preferred from the point of view of the cost of installation and the cost of maintenance. This paper proposes a new method for analysing bifacial modules. A simpler rear-side irradiance model is presented to estimate the energy yield of a bifacial solar photovoltaic module. The detailed analysis also explores the optimum tilt angle for the inclined south–north orientation to obtain the maximum possible yield from the module. Taking four regions into account, i.e., Kharagpur, Ahmedabad, Delhi, and Thiruvananthapuram, in the Indian climate, we studied several cases. The Kharagpur system showed a monthly rear irradiance gain of 13%, and the Delhi climate showed an average performance ratio of 19.5%. We studied the impact of albedo and GCR on the tilt angle. Finally, the estimated model was validated with the PVSyst version 6.7.6 as well as real field test measurements taken from the National Renewable Energy Laboratory (NREL) located in the USA.

A New Model for Estimation of Bubble Point Pressure Using a Bayesian Optimized Least Square Gradient Boosting Ensemble

Accurate estimation of crude oil Bubble Point Pressure (Pb) plays a vital rule in the development cycle of an oil field. Bubble point pressure is required in many petroleum engineering calculations such as reserves estimation, material balance, reservoir simulation, production equipment design, and optimization of well performance. Additionally, bubble point pressure is a key input parameter in most oil property correlations. Thus, an error in a bubble point pressure estimate will definitely propagate additional error in the prediction of other oil properties. Accordingly, many bubble point pressure correlations have been developed in the literature. However, they often lack accuracy, especially when applied for global crude oil data, due to the fact that they are either developed using a limited range of independent variables or developed for a specific geographic location (i.e., specific crude oil composition). This research presents a utilization of the state-of-the-art Bayesian optimized Least Square Gradient Boosting Ensemble (LS-Boost) to predict bubble pointpressure as a function of readily available field data. The proposed model was trained on a global crude oil database which contains (4800) experimentally measured, Pressure–Volume–Temperature (PVT) data sets of a diverse collection of crude oil mixtures from different oil fields in the NorthSea, Africa, Asia, Middle East, and South and North America. Furthermore, an independent (775) PVT data set, which was collected from open literature, was used to investigate the effectiveness of the proposed model to predict the bubble point pressure from data that were not used during the model development process. The accuracy of the proposed model was compared to several published correlations (13 in total for both parametric and non-parametric models) as well as two other machine learning techniques, Multi-Layer Perceptron Neural Networks (MPL-ANN) and Support Vector Machines (SVM). The proposed LS-Boost model showed superior performance andremarkably outperformed all bubble point pressure models considered in this study.

New Model for Estimation of the Age at Onset in Spinocerebellar Ataxia Type 3.

The aim of this study was to develop an appropriate parametric survival model to predict patient's age at onset (AAO) for spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) populations from mainland China. We compared the efficiency and performance of 6 parametric survival analysis methods (exponential, weibull, log-gaussian, gaussian, log-logistic, and logistic) based on cytosine-adenine-guanine (CAG) repeat length at ATXN3 to predict the probability of AAO in the largest cohort of patients with SCA3/MJD. A set of evaluation criteria, including -2 log-likelihood statistic, Akaike information criterion (AIC), bayesian information criterion (BIC), Nagelkerke R-squared (Nagelkerke R^2), and Cox-Snell residual plot, were used to identify the best model. Among these 6 parametric survival models, the logistic model had the lowest -2 log-likelihood (6,560.12), AIC (6,566.12), and BIC (6,566.14) and the highest value of Nagelkerke R^2 (0.54), with the closest graph to the bisector Cox-Snell residual graph. Therefore, the logistic survival model was the best fit to the studied data. Using the optimal logistic survival model, we indicated the age-specific probability distribution of AAO according to the CAG repeat size and current age. We first demonstrated that the logistic survival model provided the best fit for AAO prediction in patients with SCA3/MJD from mainland China. This optimal model can be valuable in clinical and research. However, the rigorous clinical testing and practice of other independent cohorts are needed for its clinical application. A unified model across multiethnic cohorts is worth further exploration by identifying regional differences and significant modifiers in AAO determination.

New Models for Estimation of Solar Radiation for Five Cities of Pakistan

New Models for Estimation of Solar Radiation for Five Cities of Pakistan

Extended Kalman Filtering Models for Power Harmonics and Frequency Estimation

This paper introduces a new model for estimation of the fundamental frequency as well as power harmonics components using extended and linear Kalman filters. It also compares the proposed method with existing techniques including a commercial power harmonic measuring device compatible to IEC 61000-4-7 standard in an experimental setting. The proposed method is shown to result in significantly enhanced estimation of the fundamental frequency and slightly improved harmonic estimation.

A New Model for Estimation of Individual Blood Flow Effect during Multimode Thermal Therapy of Tumor.

An accurate temperate control is the key during multimode thermal therapy of tumor. However, the tumor tissue temperature is greatly influenced by local blood flow changes of individuals. A simple but effective method is proposed for estimation of the local blood flow and its impact on the ablation boundary temperature. The proposed model is focused on the tumor domain, namely the targeted treatment region. In the natural thawing process post tumor freezing during the therapy, the main energy transferring to the tumor tissue comes from the blood flow of the surrounding normal tissue on the tumor boundary. By fitting the rewarming temperature measured in the tissue, the inversed problem is solved by the model to calculate the boundary convection condition and thus to predict the corresponding blood perfusion rate. The model is validated by the animal experimental data. The calculated blood perfusion rates are within the published range, but differ individually. The results prove that the new model and the estimated personalized convection coefficient can better predict the tissue temperature distribution during the therapy.Clinical Relevance-The model estimates the local blood flows around the tumor of individuals and the influence on heat transfer process. It can be used to better predict and control the temperature on the tumor boundary during the therapy that is critical to the therapeutic effect. The model also greatly cuts down the calculation time which facilitates the possibility of intraoperative real time monitoring.

Numerical analysis of the shear strength of circular reinforced concrete columns subjected to cyclic lateral loads using linear genetic programming

PurposeThe shear strength of reinforced concrete (RC) columns under cyclic lateral loading is a crucial concern, particularly, in the seismic design of RC structures. Considering the costly procedure of testing methods for measuring the real value of the shear strength factor and the existence of several parameters impacting the system behavior, numerical modeling techniques have been very much appreciated by engineers and researchers. This study aims to propose a new model for estimation of the shear strength of cyclically loaded circular RC columns through a robust computational intelligence approach, namely, linear genetic programming (LGP).Design/methodology/approachLGP is a data-driven self-adaptive algorithm recently used for classification, pattern recognition and numerical modeling of engineering problems. A reliable database consisting of 64 experimental data is collected for the development of shear strength LGP models here. The obtained models are evaluated from both engineering and accuracy perspectives by means of several indicators and supplementary studies and the optimal model is presented for further purposes. Additionally, the capability of LGP is examined to be used as an alternative approach for the numerical analysis of engineering problems.FindingsA new predictive model is proposed for the estimation of the shear strength of cyclically loaded circular RC columns using the LGP approach. To demonstrate the capability of the proposed model, the analysis results are compared to those obtained by some well-known models recommended in the existing literature. The results confirm the potential of the LGP approach for numerical analysis of engineering problems in addition to the fact that the obtained LGP model outperforms existing models in estimation and predictability.Originality/valueThis paper mainly represents the capability of the LGP approach as a robust alternative approach among existing analytical and numerical methods for modeling and analysis of relevant engineering approximation and estimation problems. The authors are confident that the shear strength model proposed can be used for design and pre-design aims. The authors also declare that they have no conflict of interest.

Evaluation of mass transfer coefficient for gas condensates in porous systems: Experimental and modeling

The gas condensate reservoirs are significant resources of energy for various municipal and industrial purposes. An unfavourable phenomenon attributed to this kind of reservoirs is liquid drop-out due to excessive near-wellbore pressure drop during production. Gas injection is normally employed to recover the liquid loss in the underground formation. The amount of condensate recovery is determined by assuming a thermodynamic local equilibrium, which seems irrational due to the limited contact area, high velocity, and low residence time for fluids around the wellbore. In this work, a series of experiments are conducted based on the Taguchi design of experiment (DOE) to study the steady-state mass transfer during gas injection. Three injection gases (CO2, N2, and CH4), three liquids to represent the liquid condensate (C5, C6, and C7), and three mean grain sizes within the range of 150–300 μm are the vital design variables of the experiments where a broad range of gas velocity (0.00472–0.0283 cm/s) is employed. Results of Taguchi DOE approach are then analyzed to find the optimum design levels and influence/contribution of each factor. The experimental data are utilized to develop new models for estimation of the mass transfer coefficient in terms of Schmidt number (Sc), Peclet number (Pe), normalized velocity, and mean grain size. The comparison between the modeling results and real data confirms the reliability and precision of the proposed correlations. Based on the research outputs, the injection rate (60% contribution factor) and grain size (nearly 10% contribution factor) have the highest and lowest effects on the mass transfer coefficient in porous systems, respectively. It is also found that the difference between the equilibrium concentration and the actual concentration of volatile component in the gas phase increases as the gas velocity increases. Based on the DOE, the optimal design levels for the injection rate, gas type, liquid type, and mean grain size are 12 cm3/min, C1, C5, and 300 μm, respectively. The new models provide accurate/reliable mass transfer coefficient (R2 = 0.9906) for non-equilibrium mass transfer cases to simulate re-vaporization of condensate into the gas phase, while implementing gas injection into the gas condensate reservoirs.

Using Background-Oriented Schlieren to Visualize Convection in a Propagating Wildland Fire

ABSTRACT Heat and mass transfer are important processes associated with wildland fire. Both radiant and convective heat transfer are important processes with convection often being the dominant mechanism. Unlike radiation, there is no direct method of measuring convection. Since convective heat transfer is governed by the fluid flow, understanding the fluid flow provides good understanding on the convective heat transfer. In fluid mechanics, flow visualization is a common methodology used to understand flow characteristics. Schlieren imagery is a common flow visualization technique which captures changes in fluid density such as the ones occur around a fire. Background-Oriented Schlieren (BOS) is a flow visualization technique that uses a background image with various patterns to visualize the density gradient caused by density fluctuations in a fluid. We applied BOS to measure the flow associated with laboratory-scale line fires. The reproducible fires were spreading in pine needle fuel beds in a wind tunnel with and without imposed wind. This initial application of BOS in a fire environment successfully visualized the flow around the flame. The visualized flow underwent a secondary process to produce the velocity field of the flow. Results indicate that even in conditions where the fire is known to be dominated by radiation, wind carried the thermal plume ahead of the flame front and expanded the thermal plume. In contrast, in the no wind condition, the thermal plume remained vertical above the fire. Using the BOS imagery, a new model for estimation of convective heat transfer was introduced. In addition to estimation of the convective heat transfer ahead of the fire, this new model enables visualization of convective motion.

New model for dental age estimation: Willems method applied on fewer than seven mandibular teeth.

Willems method measures the developmental stages of the seven left permanent mandibular teeth and is frequently used for dental age estimation. The aim of this study was to test its accuracy on a large sample of the Croatian population and to develop new models for estimation based on one to seven mandibular teeth. The developmental stages were evaluated on the digital, standardized orthopantomograms of 1868 Croatian children aged 5 to 16. Univariate regression was used for age estimation based on one tooth and regression with forward and backward elimination for selection of the best combination on two to six teeth. Tested in parallel with Willems method, the accuracy of the new models was assessed within intervals of ± 0.5, ± 1, ± 1.5 and ± 2years and shows the percentage of correct estimations. Using Willems method, the average overestimation was 0.41years for boys and 0.22years for girls. Newly developed models that use two to seven teeth proved to be significantly more accurate (p < 0.001). The accuracy of age estimation increases significantly with the number of teeth in a model. Predictably, within the interval ± 0.5years, a model with three teeth has 3% fewer accurate age estimations than a model including seven teeth. In both theory and practice, Willems method has been considered suitable for age estimation among Croatian children. However, these newly developed models significantly surpass its accuracy. Models using two to seven teeth represent a simple, reliable, and accurate method for age estimation, even in cases with missing mandibular teeth.

Accuracy of Estimated Creatinine in Multistage Ultramarathon Runners

Ultramarathon running is increasing in popularity worldwide, as is the growing body of research on these athletes. Multiple studies have examined acute kidney injury through estimated baseline creatinine (Cr) and glomerular filtration rate (GFR). Values are estimated through an age-based formula of GFR and the modification of diet in renal disease (MDRD) equation. However, the accuracy of this practice in a cohort of healthy athletes is unknown. A prospective analysis of the first 40-km (25-mi) stage of 6-stage 250-km (155-mi) multistage ultramarathons in the Sahara, Namibia, Atacama, and Gobi Deserts. Runners had prerace measured baseline Cr compared to estimated values through age-based estimated GFR and back calculation of Cr through the MDRD equation. Forty-eight participants (27% female, age 39±10 y) had Cr values analyzed. The mean measured Cr was 0.99±0.17, which was 11% higher than an estimated Cr of 0.88±0.14 (P<0.01). Estimated age-based GFR was 95.9±5.8 mL⋅min-1 compared to GFR based on measured Cr and MDRD of 86.1±14.6 (P<0.001). Estimated values of GFR and Cr by standard age-based values and MDRD equation were significantly inaccurate, which would overinflate the incidence of acute kidney injury. Future studies should devise a new model for estimation of baseline Cr that is validated in this population.

Modeling CO2 absorption in aqueous solutions of DEA, MDEA, and DEA + MDEA based on intelligent methods

ABSTRACTRemoving CO2 as an acidic-potential component from different gaseous flows is a main topic in different industries producing green-house gases, especially in natural gas sweetening units. A group of well-known absorbents for CO2 are the amine solutions. The common amine compounds consisting di-ethanolamine (DEA), methyl-di-ethanolamine (MDEA), and their mixture in aqueous solution have been investigated in this study. The effort was to develop new models for estimation of CO2 loading capacity of the presented amine solutions using genetic programing (GP) and stochastic gradient boosting (SGB) trees as two advanced and novel machine learning approaches in this area. A total of 175 sets of experimental data of CO2 absorption including independent variables (temperature, CO2 partial pressure, concentrations of DEA and MDEA in water) and objective function (CO2 loading capacity) were collected from literature and fed to the mentioned algorithms (GP and SGB) as input dataset. Then, each algorithm was run over the dataset, separately and two new models were created. Finally, strict statistical evaluations were implemented to assess the estimating capability of the new models. The statistical parameters including correlation coefficients (R2 SGB = 0.99848 and R2 GP = 0.99087), root-mean-square deviations (RMSDSGB = 0.00903 mol/mol and RMSDGP = 0.02244 mol/mol) and average absolute relative deviations (AARDSGB = 0.95628% and AARDGP = 8.71909%) show that the utilized powerful algorithms have enhanced the applicability of the new developed models providing good` estimations in operational processes. Final results show superiority and more accuracy of the new SGB model for confident predictions in amine process.

Establishing a new model for estimation of the control delay at priority junctions in Malaysia

In analysis of priority junctions, control delays to minor street automobiles are often approximated utilizing the present mathematical models. Nevertheless, the applicability of such a technique d...

Application of an Adaptive Neural-Based Fuzzy Inference System Model for Predicting Leaf Area

ABSTRACTLeaf Area (LA) is a key index of plant productivity and growth. A multiple linear regression technique is commonly applied to estimate LA as a non-destructive and quick method, but this technique is limited under the realistic situation. Thus, it is indispensable to elaborate new models for estimation. In this research, the performance of the Adaptive Neural-Based Fuzzy Inference System (ANFIS) in predicting the LA of 61 plant species (C) was investigated. Four parameters including leaf length (L), leaf width (W), C, and specific coefficient (K) for each plant were selected as input data to the ANFIS model and the LA as the output. Seven different ANFIS models including different combinations of input data were constructed to reveal the sensitivity analysis of the models. The normalized root mean square error (NRMSE), mean residual error (MRE), and linear regression were applied between observed LA and estimated LA by the models. The results indicated that ANFIS4-K2min which employed all input data was the most accurate (NRMSE = 0.046 and R2 = 0.997) and ANFIS1 which employed only the K input was the worst (NRMSE = 0.452 and R2 = 0.778). In ranking, ANFIS4-K2ave, ANFIS4-K1min, ANFIS4-K1ave, ANFIS3, and ANFIS2 ranked second, third, fourth, fifth, and sixth, respectively. The sensitivity analysis indicated that the predicted LA is more sensitive to the K, followed by L, W, and C. The results displayed that estimations are slightly overestimated. This study demonstrated that the ANFIS model could be accurate and faster alternative to the available laborious and time-consuming methods for LA prediction.

Comparison Model Hargreaves, Annandale and New Model for Estimation of Solar Radiation in Perlis, Malaysia

&lt;p&gt;Sunlight is a potential source of electrical energy in the universe. Utilization of solar radiation is not optimal to be a source of electricity generation. Before harnessed solar radiation in order to know in advance the estimated potential energy. Modelling estimates of solar radiation in three models: models Hargreaves, Annandale and the proposed new model. The new model proposed solar radiation estimates based on models Hargreaves and Annandale. Some models for estimating solar radiation is discussed in this paper which aims to determine the potential of solar radiation into electrical energy potential. A new model is proposed to estimate solar radiation. Comparison of the three models are estimated using statistical analysis e (%), CRM, and RMSE is obtained that the proposed new model is better, when compared to other models.&lt;/p&gt;

A new model for estimation of project total cost in construction projects

This paper presents a new framework for the cost estimation of construction projects concerning the significant issues that contractors may face through the life cycle of the projects. The proposed approach is basically constructed on the basis of cost estimation process in the earned value management (EVM) technique. However, it attempts to resolve the present shortcomings in EVM estimation process and take into the consideration the cost-related issues so-called financial issues such as delay in client payment, and the time value of money. Furthermore, in order to cope with the uncertain conditions of real situations, the presented model takes the advantage of fuzzy sets theory which is a well-known method in dealing with the situations where the uncertainty arises. The proposed approach not only extends the theoretical framework of EVM but also gives a real insight into the project future performance. Finally, ten illustrative cases related to construction projects are provided to compare the obtained results of proposed model with the results of the EVM estimation process and to demonstrate how the model can be implemented in real projects.

A new model for estimation of project total cost in construction projects

A new model for estimation of project total cost in construction projects