Input Parameters Research Articles

Optimized artificial neural network application for estimating oil recovery factor of solution gas drive sandstone reservoirs

The most crucial aspect in determining field development plans is the oil recovery factor (RF). However, RF has a complex relationship with the reservoir rock and fluid properties. The application of artificial neural networks is able to produce complex correlations between reservoir parameters that affect the recovery factor. This research provides a new approach to improve the accuracy of the ANN model in the form of steps including removing outlier data, selecting input parameters, selecting transferring functions, selecting the number of neurons, and determining hidden layers. By applying these steps, an ANN model was selected with nine input parameters consisting of oil viscosity, water saturation, initial oil formation volume factor, formation thickness, initial pressure, permeability, specific gravity of oil, porosity, and original oil in place. Furthermore, based on the correlation coefficient, a tangent sigmoid transferring function, 30 neurons, and two hidden layers were determined. The proposed ANN correlation gives the best accuracy compared to the previous correlations. This is proved by the highest correlation coefficient of 0.91657.

Integrating selective flocculation techniques for enhanced efficiency in manufacturing processes: A novel approach through artificial neural network modeling

The use of medium and low-grade iron ore is gradually becoming more important due to the depletion of high-grade iron ore reserves and stringent environmental acts/rules. Slimes from iron ore washing were discarded in tailing dams; however, there is currently consideration for recovering iron values from ultra-fines as well. There are enormous fine dumps that are still unutilised. Hence, this study attempt to delve the optimization of iron ore slimes an indeed requirement for manufacturing and design in industries. Leveraging a flocculation process, coupled with the implementation of an Artificial Neural Network (ANN) predictive model, the Kiriburu processing plant serves as the primary source for iron ore slime samples. Chemical analyses of the collected iron samples reveal a composition featuring 58.24 % iron content, 3.47 % Al2O3, 4.72 % SiO2, and 5.18 % LOI (Loss on Ignition). The investigation explores the performance of the flocculation technique under varying pH levels, different pulp densities, and diverse flocculant dosages. Furthermore, the varying parameters selected are pH from 6 to 11, pulp density from 1 % to 15 %, and flocculant dose from 0.03 to 0.27 mg/g. The study's findings showcase a substantial improvement in the Fe grade of iron ore, escalating from 58.24 % to 66.12 %, with an impressive recovery rate of 82.54 % achieved using a flocculant dosage of 0.09 mg/g at pH 10. Additionally, a performance assessment of the selective flocculation method for iron ore slimes is conducted using an ANN predictive model, with recovery as the pivotal parameter. The input parameters for this model encompass pH, pulp density, and flocculant dosages. Employing a three-layer ANN model with a 3–3–1 architecture and utilizing feed-forward back propagation, the study demonstrates a close alignment between predicted values and experimental data, confirming the model's effectiveness for practical manufacturing applications. Information regarding the potential applications of the model's iron ore slime beneficiation efficacy for the manufacturing sector should be considered. This could entail lower waste, more effectiveness, or cost savings. Emphasise any possible ramifications for sustainability or the environment that would make the study pertinent in a larger perspective.

Rheological behavior predictions of non-Newtonian nanofluids via correlations and artificial neural network for thermal applications

Nanofluids possess enhanced viscous and thermal features that can be utilized to improve the heat transfer performance of several applications involving sustainable manufacturing and industrial ecology, such as in heating/cooling systems, electronics, transportation etc. Therefore, it is important to understand and optimize the flow pattern of these fluids. This research emphasizes the predictions of viscosity of water/ethylene-glycol (EG) based non-Newtonian nanofluids. Four experiment-based data sets are used to predict and validate the effective viscosity, i.e., Fe3O4-Ag/EG, MWCNT-alumina/water-EG, Fe3O4-Ag/water-EG, and MWCNT-SiO2/EG-water, via existing correlations and artificial neural network (ANN). The modeling is based on three input parameters, i.e., particle concentrations, temperatures, and shear rates, and one output parameter, i.e., viscosity. The predicted outcomes are compared to the three existing correlation structures. The error matrix consists of the coefficient of determination (R2), average absolute deviation (AAD %), the sum of squared error (SSE), that are employed to evaluate the performance of the model. Results from ANN are found to be more precise, with R2 values greater than 0.99 for all datasets, compared to data fitting into existing correlations, in which Fe3O4-Ag/water-EG resulted in an R2 value as low as 0.72, to determine the nanofluids’ effective viscosity.

Microstructure investigation and optimization of process parameters of ultrasonic welding for Al–Cu dissimilar joints using design of experiment

Ultrasonic Metal Welding (UMW) employs high-power ultrasonic vibrations to join thin metal sheets of different materials, effectively addressing the thermal damage issues associated with traditional welding methods. This study aimed to optimize the joining of thin aluminum (Al) and pure copper (Cu) metal sheets using high-power ultrasonic vibration. The investigation focused on the effect of input parameters, including time, static pressure, and welding power, on the output parameter of joint strength. The results indicated that the welding time had the most significant effect on the load obtained from the Tensile-shear test. The optimal condition was determined to be a welding time of 0.98 s, a pressure of 2 bar, and a power of 85%, resulting in a maximum predicted tensile-shear test load of 753.3 N, with an error value of 11.4% obtained from the software. Microscopic images reveal that the welding mechanism in ultrasonic welding involves continuous plastic deformation, dispersion of oxide layers, and the formation and extension of micro-bonds along the weld line.

Prediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: A combined application of artificial neural network and response surface methodology

This research study mainly focuses on identifying the significant factors to be considered to discover the accuracy and reliability of the predictive models. The experimental results were employed to develop three different models: an artificial neural network (ANN), a response surface methodology (RSM), and a hybrid model. Brake thermal efficiency, specific fuel consumption, and regulated emissions were predicted using ANN, and inputs such as fuel blend concentration, CR, and engine speed were optimized using the RSM and hybrid models. The accuracy and reliability of the model results were validated with the least mean square error, mean absolute percentage error, and a higher signal-to-noise ratio. The higher R2 between 0.99426 and 0.9998 was observed by ANN whereas R2 by RSM and the hybrid model were relatively less. Similarly, the mean square error of ANN was relatively less compared to RSM and hybrid. However, the mean absolute percentage error observed in the validation test results for the optimized input parameters discovered by RSM, was less than 5 % for all the responses and higher in the hybrid model. Thus, the authors concluded that the ANN's predictive ability was much higher and RSM is the best suited for optimizing the engine parameters compared to the hybrid model.

Machine learning approaches for assessing rechargeable battery state-of-charge in unmanned aircraft vehicle-eVTOL

The long stability of electric vertical take-off and landing (eVTOL) aircraft is majorly associated with energy storage devices like batteries. Lithium-ion battery (LIB) is frequently used battery in most of eVTOL because they have high charge storage capacity, good health of battery and long-life cycles. To maintain the health of battery, the state-of-charge (SoC) and state-of-health (SoH) are the most important parameters. This study demonstrates the SoC evaluation of batteries in eVTOL aircrafts and then forecasts SoC of batteries using different machine learning (ML) approaches such as Support Vector Regression, Random Forest, Linear Regression. The experimental dataset was collected by an open portal at Carnegie Mellon University wherein over 15 million records including a hundred charge/discharge cycles, and several working conditions are available. SoC of batteries was first calculated by using collected dataset. Input parameters for SoC forecasting by ML models were prepared with different features such as voltage, current, charging/discharging energy and temperature. By feature selection analysis, EDischarge and voltage were found to be the most effective features for SoC of battery. The experimental dataset was first split into 80 % of training and 20 % of testing and then applied for three ML models (Support Vector Regression, Random Forest, Linear Regression). As compared to other ML models, Random Forest presented the best performance having the lowest error values (RMSE ≈ 0.000985, R2= 0.9996) due to non-linear relationship between every feature and SoC. The studies suggested that ML approach for battery’s SoC forecasting would provide promising methods to manage the health of battery for eVTOL aircraft.

Prediction of maize cultivar yield based on machine learning algorithms for precise promotion and planting

This study proposed a model that utilized machine learning algorithms to predict the yield of maize (Zea mays L.) cultivars. This will enable the selection of good cultivars with high yields that are suitable for planting in specific areas, such as a district or county. The breeding value of the cultivars and 11 types of time-series meteorological variables were selected as the input parameters of the model. The yield was set as the output parameter of the model. Random forest (RF), Levenberg–Marquardt neural network, and multilayer perceptron neural network algorithms were used to construct the model. The results showed that the RF outperformed the other algorithms in predicting the yield of maize cultivars by achieving the maximum coefficient of determination (R2) of 0.77 and minimal root-mean-square error of 320.25 kg/acre, mean absolute error of 229.84 kg/acre, and mean absolute percentage error of 7.1%. The constructed model can be used to effectively predict the yield of specific varieties to enable the selection of good varieties in specific areas, such as a district or county. A prediction of the yield of a specific maize cultivar in a particular planting environment can have considerable value. It can facilitate the objective identification of better adapting cultivars among farmers and support the precise promotion and planting of cultivars.

A model of task-level human stepping regulation yields semistable walking.

A simple lateral dynamic walker, with swing leg dynamics and three adjustable input parameters, is used to study how motor regulation affects frontal plane stepping. Motivated by experimental observations and phenomenological models, we imposed task-level multiobjective regulation targeting the walker's optimal lateral foot placement at each step. The regulator prioritizes achieving step width and lateral body position goals to varying degrees by choosing a mixture parameter. Our model thus integrates a lateral mechanical template , which captures fundamental mechanics of frontal-plane walking, with a lateral motor regulation template , an empirically verified model of how humans manipulate lateral foot placements in a goal-directed manner. The model captures experimentally observed stepping fluctuation statistics and demonstrates how linear empirical models of stepping dynamics can emerge from first-principles nonlinear mechanics. We find that task-level regulation gives rise to a goal equivalent manifold in the system's extended state space of mechanical states and inputs, a subset of which contains a continuum of period-1 gaits forming a semistable set: perturbations off of any of its gaits result in transients that return to the set, though typically to different gaits.

Simulation of pulse-like ground motions with directionality effect for the 2001 Mw 7.6 Bhuj, India earthquake and sensitivity analysis of uncertain model input parameters

Ground motions exhibiting pulse-like (PL) features present a distinct and significant threat to the structural integrity of constructed environments, due to intense pulse, large amplitude, and rapid energy release. The lack of recorded PL ground motion data in the Indian region presents a significant challenge for accurate seismic risk assessment of the built environment. In the absence of recorded PL ground motions, the generation of synthetic PL ground motions can provide earthquake acceleration time histories for seismic response analysis of structures and also to evaluate ground motion prediction equations (GMPEs). Within this framework, the modified stochastic finite-fault approach based on the dynamic corner frequency is used to generate the synthetic ground motions for the 2001 Gujarat earthquake. These artificially generated ground motions are validated using available peak ground acceleration data from thirteen stations. After validation, the ground motions are generated at locations equidistant from the fault in all four directions (i.e., east, west, north, south). The expected acceleration time histories at specific sites, the spatial distribution of ground motion characteristics, and the effect of site non-linearity are investigated. Based on the geographical positioning of sites for the 2001 event, it was observed that 65.15 % of ground motions on the western side, relative to the epicenter, exhibited PL characteristics, whereas 23.64 % of ground motions on the eastern side were observed to have PL properties. The effect of uncertainty in the model input parameters on the simulated ground motions is examined by performing a sensitivity analysis. The reliability and validity of the simulated ground motions are assessed by deriving an empirical equation and compared with the results obtained from available GMPEs for the region.

Resilient and sustainable semiconductor supply chain network design under trade credit and uncertainty of supply and demand

Supply Chain (SC) performance appraisal extends beyond economic evaluation to encompass environmental and social impacts, as well as resilience to supply and demand disruptions. Thus, SC network design decisions should simultaneously optimize all these performance metrics as a primary objective. However, existing studies optimized these objectives independently or in a limited scope, failing to comprehensively address the interconnected nature of these performance metrics. Therefore, this study proposes a mixed integer linear programming (MILP) model with four objectives to address a multi-tier resilient and sustainable semiconductor SC network design problem under trade credit, supply, and demand uncertainties. The model aims to determine the optimal number and location of various facilities, the amount to order from suppliers, economic production quantities, quantity allocation between SC entities, and ideal supplier selections. The proposed supply chain network contributes to the global effort to reduce carbon emissions and enhance the circular economy by considering carbon trading, recycling, and the proper disposal of end-of life products. Uncertainties in input parameters are addressed using a fuzzy programming approach, while lexicographic optimization and the ɛ-constraint method are used to solve the proposed model. This paper presents a numerical example to illustrate the applicability of the proposed model and provide managerial insights. The numerical analysis results show that provided an appropriate confidence level for the uncertain parameters, the sets of Pareto optimal solutions can be generated. And it also shows that the total SC cost increases with decreasing carbon emissions level and increasing job opportunities created. This research advances sustainable and resilient supply chain management practices while offering practical guidance for decision-makers in real-world contexts.

Machine learning prediction and optimization of CO2 foam performance for enhanced oil recovery and carbon sequestration: Effect of surfactant type and operating conditions

Foam flooding handles the challenges of gas injection constituting a high viscosity medium that sweeps oil more efficiently meanwhile creating a barrier that prevents leakage of gas phase. Therefore, it is a promising enhanced oil recovery (EOR) method, simultaneously an efficient carbon dioxide (CO2) storage technology. The foam's performance displays susceptibility to various reservoir and operational parameters. Numerous laboratory experiments would need to be carried out to study foams and find the optimum foam formulation for the given core conditions. Data-driven approaches can be an alternative method to the time-consuming experimental and conventional modeling techniques, which often face challenges to incorporate the effect of important related parameters such as temperature and type of surfactant to the performance of foam. In the current study, machine learning (ML) models were constructed to predict the CO2 foam apparent viscosity in carbonate and sandstone formations focusing on experimental data from the literature for CO2 foams stabilized by different surfactants. Various anionic, cationic, and nonionic surfactants were considered and characterized based on the hydrophilic-lipophilic balance (HLB) number. Predictive models were developed using ML algorithms based on the data available for eight key process parameters. Artificial neural networks and extra randomized tree algorithms provided the most precise prediction among the applied algorithms. Various predictions for foam apparent viscosity on hypothetical case studies were made on altered input parameters, enabling CO2 mobility to co-optimize EOR and CO2 sequestration while selecting the optimum surfactant for each case.

Estimating aboveground biomass dynamics of wheat at small spatial scale by integrating crop growth and radiative transfer models with satellite remote sensing data

Aboveground biomass (AGB) of plants is an agroecological indicator that can be used to monitor crop growth status and quantify biomass carbon stock in agricultural ecosystems. Although satellite remote sensing data and crop models are widely employed to estimate AGB dynamics, their application in small-scale research plots is often constrained by the unavailability of freely and timely high-resolution satellite imageries and the challenge of acquiring reliable model inputs like initial soil water and nitrogen content. This study integrated a crop growth model (APSIM-Wheat) and radiative transfer model (PROSAIL) to estimate AGB dynamics at a small plot scale (ca. 5 to 20 m2) within variety trials (ca. 1 to 2 ha) by assimilating trial-level remote sensing data into the hybrid APSIM-PROSAIL model. The APSIM-PROSAIL integration, developed by deriving PROSAIL input parameters from APSIM-Wheat output variables, was verified by evaluating its capacity to reproduce trial-level Sentinel-2 (S2) NDVI dynamics across 30 variety trials in 2020. The comparison of the simulated and observed trial-level S2 NDVI dynamics yielded an overall RRMSE of 18.2% (n = 646 observations), suggesting the potential of using observed S2 NDVI dynamics to constrain APSIM-PROSAIL and estimate environment variables in cropping systems. This constraint was subsequently assessed in 28 variety trials in 2021, where APSIM-PROSAIL inversely estimated unknown trial-specific variables including initial soil water and nitrogen content across the trials. Using the estimated initial soil status and the plot information including genotypes and management as sown, we demonstrated that this method could also facilitate accurate retrievals of plot-level AGB dynamics with an overall RRMSE of 20.9% (n = 976 measurements). The proposed APSIM-PROSAIL demonstrated the capability to accurately retrieve plot-level AGB dynamics and reproduce trial-level S2 NDVI dynamics across a broad and diverse range of variety trials in the Australian Wheatbelt, indicating its potential utility to facilitate biomass carbon stock assessment. It holds the potential as a tool to explore the relationships between canopy spectral information and crop traits in response to genotype-environment-management interactions in agricultural ecosystems. The method enables the examinations of within-field variabilities for crop traits through retrieving them at small spatial scales from coarse remote sensing data.

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. This study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM , specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

Trajectory-based global sensitivity analysis in multiscale models

This research introduces a novel global sensitivity analysis (GSA) framework for agent-based models (ABMs) that explicitly handles their distinctive features, such as multi-level structure and temporal dynamics. The framework uses Grassmannian diffusion maps to reduce output data dimensionality and sparse polynomial chaos expansion (PCE) to compute sensitivity indices for stochastic input parameters. To demonstrate the versatility of the proposed GSA method, we applied it to a non-linear system dynamics model and epidemiological and economic ABMs, depicting different dynamics. Unlike traditional GSA approaches, the proposed method enables a more general estimation of parametric sensitivities spanning from the micro level (individual agents) to the macro level (entire population). The new framework encourages the use of manifold-based techniques in uncertainty quantification, enhances understanding of complex spatio-temporal processes, and equips ABM practitioners with robust tools for detailed model analysis. This empowers them to make more informed decisions when developing, fine-tuning, and verifying models, thereby advancing the field and improving routine practice for GSA in ABMs.

Wood Polymer Composite Based on Poly-3-hydroxybutyrate-3-hydroxyvalerate (PHBV) and Wood Flour-The Process Optimization of the Products.

This study involved the optimization of the molded pieces manufacturing process from a poly-3-hydroxybutyrate-co-3-hydroxyvalerate biocomposite containing 30% wood flour by mass. The amount of wood flour and preliminary processing parameters were determined on the basis of preliminary tests. The aim of the optimization was to find the configuration of important parameters of the injection process to obtain molded pieces of good quality, in terms of aesthetics, dimensions, and mechanical properties. The products tested for quality were dog bone specimens. The biocomposite was produced using a single-screw extruder, whereas molded pieces were made using an injection molding process. The Taguchi method was applied to optimize the injection molding parameters, which determine the products quality. Control factors were selected at three levels. The L27 orthogonal plan was used. For each set of input parameters from this plan, four processing tests were performed. The sample weight, shrinkage, elongation at break, tensile strength, and Young's modulus were selected to assess the quality of the molded parts. As a result of the research, the processing parameters of the tested biocomposite were determined, enabling the production of good-quality molded pieces. No common parameter configuration was found for different optimization criteria. Further research should focus on finding a different range of technological parameters. At the same time, it was found that the range of processing parameters of the produced biocomposite, especially processing temperature, made it possible to use it in the Wood Polymer Composites segment.

Additive manufacturing of Fe28.2Mn32.9Ni18.8Cr6Al14.1 high-entropy alloy by applying consumable fillers of metal powders and polyvinyl alcohol

Simultaneous application of welding methods and metal additive manufacturing facilitates the production of various 3D physical samples from metals and alloys. One of the technical impediments, especially for additive manufacturing of high entropy alloys, is the variety of elements in their chemical composition that makes the fabrication of fillers in welding methods very challenging. In the present research, the additive manufacturing of Fe28.2Mn32.9Ni18.8Cr6Al14.1 high entropy alloy with the use of consumable fillers, including metal powders and polyvinyl alcohol binder through gas tungsten arc welding process has been investigated. To perform the gas tungsten arc welding process, the experiments were designed using the Taguchi method. The parameters of current, torch angle, torch speed, and substrate temperature were selected as the input parameters. Then, through Grey analysis, the optimum width and height of the weld bead were determined to deposit thin layers with high height and low width. The results of the experiments confirmed that the proposed method for fabricating consumable fillers successfully performed layer-by-layer deposition of the sample using the gas tungsten arc welding process. Moreover, the elemental mapping images proved the presence of all elements in the Fe28.2Mn32.9Ni18.8Cr6Al14.1 sample. Furthermore, no internal voids were detected upon inspection of the fabricated sample using a micro-computed tomography scanner.

Assessing freedom from chronic wasting disease in semi-domesticated reindeer in Norway and Sweden

Establishing freedom from disease is a key component of surveillance and may have direct consequences for trade and economy. Transboundary populations pose challenges in terms of variable legislation, efforts, and data availability between countries, often limiting surveillance efficiency. Chronic wasting disease (CWD) is a contagious prion disease of cervids. The long incubation period and slow initial epidemic growth make it notoriously difficult to detect CWD in the early phase of an epidemic. The recent emergence of CWD in wild reindeer in Norway poses a threat to approximately 250,000 semi-domesticated reindeer in Norway and 250,000 in Sweden, including transboundary populations. Here, we provide a first analysis of surveillance data (2016–2022) from all reindeer districts in Norway and Sweden to determine the probability of freedom from CWD infection. During the six years, 6017 semi-domesticated reindeer were tested in Sweden and 51,974 in Norway. Most samples came from healthy slaughtered animals (low risk). Reindeer use large and remote areas and (high risk) samples from fallen stock and animals with clinical signs were difficult to obtain. A scenario tree model was run for seven different set of values for the input parameters (design prevalence within and between districts, probability of introduction, and relative risks) to determine the effect on surveillance sensitivity. At the national level, the mean probability of disease freedom was 59.0 % in Sweden and 87.0 % in Norway by 2021. The most marked effect on sensitivity was varying the design prevalence both within and between districts. Uncertainty about relative risk ratios affected sensitivity for Sweden more than for Norway, due to the higher proportion of animals in the high-risk group in the former (13.8 % vs. 2.1 %, respectively). A probability of disease freedom of 90 % or higher was reached in 8.2 % of the 49 districts in Sweden and 43.5 % of the 46 districts in Norway for a design prevalence of 0.5 %. The probability of freedom remained below 60 % in 29 districts (59.2 %) in Sweden and 10 districts (21.7 %) in Norway. At the national level, only Norway had a sufficiently large number of samples to reach a probability of more than 95 % of disease freedom within a period of 10 years. Our cross-border assessment forms an important knowledge base for designing future surveillance efforts depending on the spatial pattern of prevalence of CWD and risk of spread.

Forecasting daily rainfall in a humid subtropical area: an innovative machine learning approach

ABSTRACT Hydrological modeling is one of the most complicated tasks in sustainable water resources management, particularly in terms of predicting rainfall. Predicting rainfall is critical to build a sustainable society in terms of hydropower operations, agricultural planning, and flood control. In this study, a hybrid model based on the integration of k-nearest neighbor (KNN), XGBoost (XGB), decision tree (DCT), and Random Forest (RF) has been developed and implemented for forecasting daily rainfall for the first time at Sydney airport, Australia. Daily rainfall, temperature, evaporation, and humidity have been selected as input parameters. Three statistical measurements, namely, root mean square error (RMSE), Coefficient of determination (R2), mean absolute error (MAE), and Normalized Root Mean Square Error (NRMSE) have been utilized in order to check the accuracy of the proposed model. A sensitivity analysis was conducted, and the results indicated that for the purpose of prediction, the temperature, humidity, and evaporation were highly sensitive to the rainfall data. According to the results, the developed hybrid model was capable of predicting daily rainfall with high performance for both training and testing parts with RMSE = 0.124, R2 = 0.999, MAE = 0.007, NRMSE = 0.04 and RMSE = 1.246, R2 = 0.991, MAE = 0.109, NRMSE = 0.339, respectively.

Tridimensional porosity modeling through fuzzy logic and geostatistics

The three-dimensional porosity simulation plays a pivotal role in the exploration and prospecting of oil fields. In this study, we introduce a hybrid methodology for subsismic resolution porosity estimation, combining algorithms based on fuzzy logic and geostatistics. We demonstrate how it is possible to use simulated facies association surfaces and structural surfaces as input parameters for the fuzzy logic algorithm to derive porosity trends, subsequently refined using simple kriging with magnetic resonance log obtained from wells. The selected simulation area is the Sapinhoá field, located in the Santos Basin, Brazil, covering the age interval from 123 Ma to 113 Ma. To statistically validate the results, we employed the Pearson correlation coefficient, along with graphs comparing the measured and simulated porosity in the wells. The obtained results showed satisfactory levels of accuracy in the evaluated wells. Moreover, in areas lacking well data, the simulated porosity volumes aligned with the trends modeled using fuzzy logic.

Optimization of input parameters during drilling of glass fiber reinforced polymer composite using grey relational analysis

Optimization of input parameters during drilling of glass fiber reinforced polymer composite using grey relational analysis