AI-Driven Predictive Modeling of Nanoparticle-Enhanced Solvent-Based CO2 Capture Systems: Comprehensive Review and ANN Analysis

The cooling performance of Newtonian and non-Newtonian nanofluids in a square duct was identified experimentally. The flow regime was laminar. Two-step method was used to prepare stable dispersions of γ-Al2O3 and TiO2 nanoparticles. Water and ethylene glycol were used as the base fluids of Newtonian, and a 0.5 mass% carboxymethyl cellulose in water was used as the base fluid of the non-Newtonian nanofluids. Nanoparticle concentration was in the range of 0.1–1.5% by volume. Heat transfer coefficient of nanofluids enhances significantly relative to the base fluids. The enhancement of heat transfer coefficient of nanofluids is proportional to the Peclet number and nanofluid concentration. Nanoparticles type and the base fluid affected the Newtonian nanofluids performance, while for non-Newtonian nanofluids this is not the case. The improvement in nanofluids heat transfer coefficient is more than just related to their thermal conductivity enhancement. The obtained experimental data were modeled by the artificial neural network (ANN). Two empirical correlations were also fitted on the data. The experimental data are well predicted by the ANN models and empirical correlations. Statistical criteria show that the ANN models are more accurate.

The search for better climate change adaptation techniques for addressing environmental and economic issues due to changing climate is of paramount interest in the current era. One of the many ways Pacific Island regions and its people get affected is by dry spells and drought events from extreme climates. A drought is simply a prolonged shortage of water supply in an area. The impact of drought varies both temporally and spatially that can be catastrophic for such regions with lack of resources and facilities to mitigate the drought impacts. Therefore, forecasting drought events using predictive models that have practical implications for understanding drought hydrology and water resources management can allow enough time to take appropriate adaption measures. This study investigates the feasibility of the Artificial Neural Network (ANN) algorithms for prediction of a drought index: Standardized Precipitation-Evapotranspiration Index (SPEI). The purpose of the study was to develop an ANN model to predict the index in two selected regions in Queensland, Australia. The first region, is named as the grassland and the second as the temperate region. The monthly gridded meteorological variables (precipitation, maximum and minimum temperature) that acted as input parameters in ANN model were obtained from Australian Water Availability Project (AWAP) for 1915–2013 period. The potential evapotranspiration (PET), calculated using thornthwaite method, was also an input variable, while SPEI was the predictand for the ANN model. The input data were divided into training (80%), validation (10%) and testing (10%) sets. To determine the optimum ANN model, the Levenberg-Marquardt and Broyden-Fletcher-Goldfarb-Shanno quasi-Newton backpropagation algorithms were used for training the ANN network and the tangent sigmoid, logarithmic sigmoid and linear activation algorithms were used for hidden transfer and output functions. The best architecture of input-hidden neuron-output neurons was 4-28-1 and 4-27-1 for grassland and temperate region, respectively. For evaluation and selection of the optimum ANN model, the statistical metrics: Coefficient of Determination (R 2 ), Willmott’s Index of Agreement (d), Nash-Sutcliffe Coefficient of Efficiency (E), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were employed. The R 2 , d, E, RMSE and MAE for optimum ANN models were 0.9839, 0.9909, 0.9838, 0.1338, 0.0882 and 0.9886, 0.9935, 0.9874, 0.1198, 0.0814 for grassland and temperate region, respectively. When prediction errors were analysed, a value of 0.0025 to 0.8224 was obtained for the grassland region, and a value of 0.0113 to 0.6667 was obtained for the temperate region, indicating that the ANN model exhibit a good skill in predicting the monthly SPEI. Based on the evaluation and statistical analysis of the predicted SPEI and its errors in the test period, we conclude that the ANN model can be used as a useful data-driven tool for forecasting drought events. Broadly, the ANN model can be applied for prediction of other climate related variables, and therefore can play a vital role in the development of climate change adaptation and mitigation plans in developed and developing nations, and most importantly, in the Pacific Island Nations where drought events have a detrimental impact on economic development.

After inventing Artificial Neural Networks, a deep learning algorithm, simulation of hydrology and water resource-related problems become more efficient. The investigation aimed to discover an efficient Artificial Neural Networks (ANN) model for obtaining weekly reference evapotranspiration (ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ) in the Tirupati region. Air temperature (T), Sunshine hours (S), Wind speed (W) and Relative Humidity (RH) are among the climate variables commonly utilized to evaluate the ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> . Multiple and partial correlation analyses were performed between the ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> calculated by the Penman-Monteith (PM) method (PMET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ) and these variables by deleting one variable each time to determine the most impacting variable, RH, W, S, and T were found to be impacting variables in the order of lowest to highest. As a result, the most desirable ANN model (ANN ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ) was created using all the variables as inputs and eliminating one of the least influential variables each time to assess ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> . The ANN models are developed and validated using climatic data from 1992 to 2001. The model's ability was evaluated using numerical indicators and scatter & comparison plots by matching the PM ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> to the ANN ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> . The numerical indexes are employed to validate the usefulness of the generated models. The ANN (1-5-1) considering one input variable (T), ANN (2-5-1) considering two input variables (T & S), ANN (3-4-1) considering three input variables (T, S, & W), and ANN (4-3- 1) considering four input variables (T, S, W, & RH), were found to have 83.53%, 89.85%, 94.21%, and 99.30% efficiency during the validation, respectively. Therefore, the ANN models may accurately predict the weekly ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> in the research area and elsewhere in climatological situations similar to the study area.

The working conditions in oil sand mines in Northern Alberta, Canada, are greatly affected by the climate and geology. The ground is very hard and competent in winter but very soft during summer [1]. This changing behaviour of the ground has a great impact on the truck’s frame and the health of the operator because he is exposed to large Whole Body Vibrations (WBV). When the human body is exposed to large WBV for prolonged periods, it begins to have chronic back problems resulting in diseases of the lumbar spine, disc degeneration and other pathological effects to the spine and skeletal structure [2]. Many of these health effects are irreversible and people suffering from WBV disorders can experience pains for the rest of their lives. Studies carried out to correlate WBV to health problems have resulted in the setting of standards with the help of various government agencies, physiologists and industry experts. The main WBV standards that the mining industry has to observe are those of International Standards Organization (ISO) and the British Standards Institute [2]. ISO 2631-1 uses a three dimensional coordinate system where the axes are orthogonal to each other as shown in Figure 1 [3]. The standards require that the measuring device (accelerometer) should be placed at a point where vibrations are entering the body. In this study the data was collected for the seat accelerations in X, Y and Z directions by installing the accelerometer on the seat pan of a CAT 797 haul truck. An Artificial Neural Network (ANN) model was developed to predict the seat vibrations in very large capacity haul trucks in the X and Y directions. The study was done to find if the vibrations in the truck and their effects on the operator can be correlated to the truck’s operational parameters like speed, payload and strut pressures. There is a need for an onboard monitoring system on large haul trucks to alert the driver when he exceeds specified speed limits at certain locations along the haul road which are unsafe for him. Caterpillar 797 trucks use Vital Information Management System (VIMS) to track the truck’s behaviour when driven along a haul road. It includes onboard truck measuring equipment and off-board VIMS software which enables data logging and downloading into a computer. A record of real time parameters gives a detailed view of what happens to the truck body while performing the various functions [4]. The disadvantage of using a piezo-resistive accelerometer to collect the data collection is that it has a limited high frequency response [5]. Thus, it is necessary that the data be collected at higher frequencies to obtain a better model that can give more accurate predictions. In this work the accelerometer was replaced with an ANN model. The ANN model is developed for the dump truck such that it models the seat vibrations in the truck in response to the truck’s speed, payload, changing ground stiffness and profile. These truck parameters can be estimated from the strut pressure responses measured in terms of machine rack, machine roll and machine pitch. The machine’s roll, rack and pitch give an idea of the truck’s response to the changing ground conditions and operator’s driving skills. These values are calculated using the changing pressures of the four struts on which the whole truck frame is resting. These parameters were used as input variables during the development of the ANN model. This study focused only on the seat accelerations in the X-direction and Y-direction as output variables, as these are more important than the accelerations in the Z-direction. The ANN model is very efficient for non-linear processes and tends to improve its performance as it learns more and more about the system. Thus when the ANN model is used, very accurate acceleration values can be predicted. This is important for the health of the operator and also for the lifespan of the truck as the high amplitude vibrations are detrimental to truck’s structural components. Using the NeuroShell® 2 software, a multilayer perceptron ANN was trained with the back propagation algorithm. The model’s performance was optimized following the systematic approach developed by [6,7]. This is done by looking for the simplest network architecture that can converge. During the development of the ANN model for Seat X acceleration, the number of hidden layer neurons was increased from 10 to 70 while keeping the number of learning epochs constant at 50. The number of neurons which gave the best value for R 2 (Validation)

Prediction of wind drift and evaporation losses from sprinkler irrigation using neural network and multiple regression techniques

Time-frequency analysis and simulation of the watershed suspended sediment concentration based on the Hilbert-Huang transform (HHT) and artificial neural network (ANN) methods: A case study in the Loess Plateau of China

Artificial Intelligence (AI) has gained popularity in recent years for offshore engineering applications, and one such challenging application is detection of mooring line failure of a floating offshore platform. For most types of floating offshore platforms, accurately detecting any mooring line damage and/or failures is of great interest to their operators. This paper demonstrates the use of an Artificial Neural Network (ANN) model for detecting mooring line failure for a spread-moored FPSO. The ANN model representation, in terms of its input variables, is based on assessing when changes in a platform’s motion characteristics are in-fact indicators of a mooring line failure. The output of the ANN model indicates the status condition for the mooring lines (intact or failed). This ANN model only requires GPS / DGPS monitoring data and does not require data on the environmental conditions at the platform. Since the mass of an FPSO changes with the stored volume of oil, the vessel’s mass is also an input variable. The ANN training uses the results from numerical simulations of a spread-moored FPSO with fourteen mooring lines. The numerical simulations create the FPSO’s response to a range of metocean conditions for 360-degree directions, and they cover several levels of vessel draft (mass). Furthermore, the simulations cover both the intact mooring configuration and the full permutation where each of the fourteen mooring lines is modeled as broken at the top. The global performance analysis of the FPSO is presented in a different paper (Part 2 of these paper series). The training of the ANN model employs a back-propagation learning algorithm and an automatic method for determining the size of ANN hidden layers. The trained ANN model can detect mooring line failure, even for vessel draft (mass), sea states and environmental directions that are not included in the training data. This demonstrates that the ANN model can recognize and classify patterns associated with mooring line failure and separate such patterns from those associated with intact mooring lines under conditions not included in the original training data. This study reveals a great potential for using an ANN model to monitor the station keeping integrity of a floating offshore platform with changing storage, or mass status, and to detect mooring line failure using only the vessel’s mass and deviations in the platform’s motions derived from GPS / DGPS data.

Measured evapotranspiration (LE) of screenhouse banana plantations was utilized to derive and compare two types of machine-learning models: artificial neural network (ANN) and multiple linear regression (MLR). The measurements were conducted by eddy-covariance systems and meteorological sensors in two similar screenhouse banana plantations during two consecutive seasons, 2016 and 2017. Most of the study focused on the season of 2017, which includes a more extended data set (141 days) than 2016 (52 days). The results show that in most cases, the ANN model was superior to MLR. When trained and validated over the whole data set of 2017, the ANN and MLR models provided R2 of 0.92 and 0.89, RMSE of 37.5 and 45.1 W m−2 and MAE of 21 and 27.2 W m−2, respectively. Models could be derived using a training dataset as short as one month and still provide reliable estimations. Depending on the chosen calendar month for training, R2 of the ANN model varied in the range 0.81–0.89, while for the MLR model, it ranged 0.73–0.88. When trained using a data set as short as one week, there was some deterioration in model performance; the corresponding ranges of R2 for the ANN and MLR models were 0.37–0.89 and 0.37–0.71, respectively. As expected for a screenhouse decoupled environment, solar radiation (Rg) was the variable that most influenced LE; using Rg as the sole input variable, the ANN model resulted in R2, RMSE and MAE of 0.88 and 47 W m−2 and 25.6 W m−2, respectively, values that are not much worse than using all input variables (solar radiation, air temperature, air relative humidity and wind speed). Using Rg alone as the input to the MLR model only slightly deteriorated R2 (=0.88); however, RMSE (=124 W m−2) and MAE (=75.7 W m−2) were significantly larger compared to a model based on all input variables. To examine model performance in different seasons, models were trained using the data set of 2017 and validated in 2016, and vice versa. Results showed that training on the data of 2017 and validation in 2016 provided superior results than the opposite, presumably since the 2017 measurement season was longer and weather conditions were more diverse than in the 2016 data set. It is concluded that the ANN and MLR models are reasonable options for estimating evapotranspiration in a banana screenhouse.

Reduction of passenger cars fuel consumption and associated emissions are two major goals of sustainable transport over the last years. Passenger car fuel consumption is directly related to a number of technological aspects of a given car, driver behaviour, road and weather conditions and, especially at urban level, road structure and traffic flow and conditions. In this paper, passenger car fuel consumption was assumed to be a function of three input variables, i.e. day of week, hour of day and city zone. Over the period of 6 months (during 2015) a car was driven in the randomly chosen routes in the city of Niš (Serbia) in the period from 8 to 23 h. The fuel consumption data recorded through on-board diagnostics equipment were used for the development of Artificial Neural Network (ANN) models. In order to efficiently deal with a number of ANN design issues, to avoid usual trial and error procedure and develop robust, high performance ANN models, the Taguchi method was applied. For experimentation with ANN design parameters (transfer function, the number of neurons in the first hidden layer, the number of neurons in the second hidden layer, training algorithm), the standard L18 orthogonal array with two replications was selected. Statistical results indicate the dominant influence of the training algorithm, followed by the ANN topology, i.e. interaction of the number of neurons in hidden layers, on the ANN models performance. It has been observed that 3-8-8-1 ANN model represents an optimal model for prediction of passenger car fuel consumption. This model has logistic sigmoid transfer functions in hidden layers trained with scaled conjugate gradient algorithm. By using the Taguchi optimized ANN models, analysis of passenger car fuel consumption has been discussed based on traffic conditions, i.e. different days of the week and hours of the day, for each city zone and separately for summer and winter periods.

Stabilized aggregate bases are vital for the long-term service life of pavements. Their stiffness is comparatively higher; therefore, the inclusion of stabilized materials in the construction of bases prevents the cracking of the asphalt layer. The effect of wet–dry cycles (WDCs) on the resilient modulus (Mr) of subgrade materials stabilized with CaO and cementitious materials, modelled using artificial neural network (ANN) and gene expression programming (GEP) has been studied here. For this purpose, a number of wet–dry cycles (WDC), calcium oxide to SAF (silica, alumina, and ferric oxide compounds in the cementitious materials) ratio (CSAFRs), ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ3), and deviator stress (σ4) were considered input variables, and Mr was treated as the target variable. Different ANN and GEP prediction models were developed, validated, and tested using 30% of the experimental data. Additionally, they were evaluated using statistical indices, such as the slope of the regression line between experimental and predicted results and the relative error analysis. The slope of the regression line for the ANN and GEP models was observed as (0.96, 0.99, and 0.94) and (0.72, 0.72, and 0.76) for the training, validation, and test data, respectively. The parametric analysis of the ANN and GEP models showed that Mr increased with the DMR, σ3, and σ4. An increase in the number of WDCs reduced the Mr value. The sensitivity analysis showed the sequences of importance as: DMR > CSAFR > WDC > σ4 > σ3, (ANN model) and DMR > WDC > CSAFR > σ4 > σ3 (GEP model). Both the ANN and GEP models reflected close agreement between experimental and predicted results; however, the ANN model depicted superior accuracy in predicting the Mr value.

Black box-based ANN (Artificial Neural Network) models and the process-based model TOPMODEL have been increasingly applied to various water resources system problems in recent years. One of main focuses in this work is to develop ANN models for daily stream flow forecasting and determine a suitable combination of input variables and a more accurate architecture in the design phase. Another focus is to compare the performance of ANN models and TOPMODEL in one day ahead stream flow forecasting. Baohe River basin, with a humid climate, is selected as the study area. The results show that ANN models with flow data plus precipitation data as the input variables perform much better than that with only precipitation data or only flow data as the input variables. The performance of ANN models will be slightly reduced if evaporation data are added into the input vector. ANN has a very good performance against the TOPMODEL in terms of Nash-Sutcliffe efficiency. Nevertheless, they both can not capture the main peak flow: ANN underestimates the main peak flows while TOPMODEL overestimates two or three peak flows in validation years.

In this (modest) study, we developed artificial neural network (ANN) models for predicting body weight using various independent (input) variables in eight-week old New Zealand white purebred and crossbred rabbits. From the whole data sets of similar age groups, 75 percent were used to train the neural network model and 25 percent were used to test the effectiveness of the model. Five predictor variables were used viz, breed, sex, heart girth, body length and height at wither as input variables and body weight was considered as dependent variable from the model. The ANN used was multilayer feed forward network with back propagation of error for efficient learning. Our ANN models (with R2 = 0.68 at ten thousand iterations, and R2 = 0.71 one million iterations) performed better than traditional multivariate linear regression (MLR) models (R2 = 0.66) indicating that the ANN models were able to more accurately capture how the variations in input variables explained the variations in body weight. It is concluded that ANN models are more powerful than MLR models in predicting animals’ body weight. Nonetheless, we recognize that fitting an ANN model requires more computation resources than fitting a tradition MLR model but the benefits of its accuracy outweigh any demerit from the associated computation overhead.

An attempt was made to model the non-linear system of rainfall-runoff process from Bharathapuzha River basin using an information processing paradigm, Artificial Neural Network (ANN). The results were compared with the outputs of the semi-distributed, physically-based SWAT (Soil and Water Assessment Tool) model. The ANN modelling was done using back propagation learning algorithm, tan sigmoid transfer function, and model input strategy having rainfall and other climatic variables as input by assigning number of layers as 5, 10, 15, 20, 25, 30, and 40. Different models were evaluated with respect to coefficient of correlation (r), coefficient of determination (R2 ), and root mean square error (RMSE). Among the ANN models, ANN-BP-A-5 (six input variables, 5 hidden layers) performed best, followed by ANN-BP-A40 (six input variables, 40 hidden layers). Comparison of ANN predicted runoff of the best models (ANN-BP-A-5 and ANNBP-A40) with the SWAT predicted runoff revealed that the simulated runoff using SWAT was more correlated to observed runoff than ANN predicted runoff. The ANN models underestimated the flow during the rainy season, and gave an overestimation during the summer season. However, the R2 values of 0.666 and 0.649 obtained for ANN-BP-A-5 and ANN-BP-A40, respectively, indicated that the performances of ANN models were satisfactory and ANN model can also be used for runoff prediction in data scarce areas.

This article compares two modeling approaches for optimal operation of a turbo chiller installed in an office building: (1) a machine learning model developed with artificial neural network (ANN) and (2) a hybrid machine learning model developed with the ANN model and available physical knowledge of the chiller. Before developing the ANN model of the chiller, the authors used Gaussian mixture model in order to check the validity of measured data. Then, the hybrid model was developed by combining the ANN model and physics-based regression equations from the EnergyPlus engineering reference. It was found that both the ANN and hybrid ANN model are satisfactory to predict the chiller’s power consumption: mean bias error (MBE) = −2.63%, coefficient of variation of the root mean square error (CVRMSE) = 8.05% by the ANN model; MBE = −3.99%, CVRMSE = 11.98% by the hybrid ANN model. However, the hybrid model requires fewer inputs (four inputs) than the ANN model (eight inputs). The energy savings of both models are similar coefficient of performance (COP) = 4.32 by the optimal operation of the ANN model; COP = 4.44 by the optimal operation of the hybrid ANN model. In addition, the hybrid ANN model can be applied where the ANN model is unable to provide accurate predictions.

In this study, artificial neural network (ANN) and thermodynamic models were developed for prediction of the heat capacity (C P ) of amine-based solvents. For ANN model, independent variables such as concentration, temperature, molecular weight and CO2 loading of amine were selected as the inputs of the model. The significance of the input variables of the ANN model on the C P values was investigated statistically by analyzing of correlation matrix. A thermodynamic model based on the Redlich-Kister equation was used to correlate the excess molar heat capacity $$ \left({C}_P^E\right) $$ data as function of temperature. In addition, the effects of temperature and CO2 loading at different concentrations of conventional amines on the C P values were investigated. Both models were validated against experimental data and very good results were obtained between two mentioned models and experimental data of C P collected from various literatures. The AARD between ANN model results and experimental data of C P for 47 systems of amine-based solvents studied was 4.3%. For conventional amines, the AARD for ANN model and thermodynamic model in comparison with experimental data were 0.59% and 0.57%, respectively. The results showed that both ANN and Redlich-Kister models can be used as a practical tool for simulation and designing of CO2 removal processes by using amine solutions.