ANN-based Prediction Model Research Articles

Ann-based predictive model of geometrical deviations in dry turning of AA7075 (Al-Zn) alloy

This work presents the use of a shallow feedforward artificial neural network (ANN) to develop a prediction model for geometrical deviations in the dry turning of the AA7075 (Al-Zn) alloy. The study focuses on the influence of cutting speed and feed on the arithmetic mean roughness, straightness, and circular runout of cylindrical specimens. The main novelty of this ANN-based model compared to traditional models lies in the simultaneous consideration of geometrical variables at macro and micro scales. The analysis showed that feed was the most influential variable, particularly at higher values, whereas cutting speed had a lesser impact. For all three analysed output variables, the optimal results were achieved by combining low feed and high cutting speed values. The proposed ANN model showed a reasonable adjusted R2 value for all the variables, ranging from 0.87 to 0.97. The ANN performance was compared with other regression models, providing a better fit to the experimental data for all the output variables analysed. Testing of the ANN on additional data not included in the training and validation set confirmed its practical usefulness for predicting geometrical deviations under the studied cutting conditions.

An artificial neural network visible mathematical model for predicting slug liquid holdup in low to high viscosity multiphase flow for horizontal to vertical pipes

AbstractSlug liquid holdup (SLH) is a critical requirement for accurate pressure drop prediction during multiphase pipe flows and by extension optimal gas lift design and production optimization in wellbores. Existing empirical correlations provide inaccurate predictions because they were developed with regression analysis and data measured for limited ranges of flow conditions. Existing SLH machine learning models provide accurate predictions but are published without any equations making their use by other researchers difficult. The only existing ML model published with actual equations cannot be considered optimum because it was selected by considering artificial neural network (ANN) structures with only one hidden layer. In this study, an ANN-based model for SLH prediction with actual equations is presented. A total of 2699 data points randomly divided into 70%, 15%, and 15% for training, validation, and testing was used in constructing 71 different network structures with 1, 2, and 3 hidden layers respectively. Sensitivity analysis revealed that the optimum network structure has 3 hidden layers with 20, 5, and 15 neurons in the first, second, and third hidden layers, respectively. The optimum network structure was translated into actual equations with the aid of the weights, biases, and activation functions. Trend analysis revealed that this study’s model reproduced the expected effects of inputs on SLH. Test against measured data revealed that this study’s model is in agreement with measured data with coefficient of determinations of 0.9791, 0.9727, 0.9756, and 0.9776 for training, testing, validation, and entire datasets, respectively. Comparative study revealed that this study’s model outperformed existing models with a relative performance factor of 0.002. The present model is presented with visible mathematical equations making its implementation by any user easy and without the need for any ML framework. Unlike existing ANN-based models developed with one hidden layered ANN structures, the present model was developed by considering ANN structures with one, two, and three hidden layers, respectively.

Comparison of electricity savings in community units through ESS and PV generation using ANN-based prediction model under Korean climatic conditions

Comparison of electricity savings in community units through ESS and PV generation using ANN-based prediction model under Korean climatic conditions

Performance Prediction and Heating Parameter Optimization of Organic-Rich Shale In Situ Conversion Based on Numerical Simulation and Artificial Intelligence Algorithms.

In situ conversion technology is a green and effective way to realize the development of organic-rich shale. Supercritical CO2 can be used as a good heating medium for shale in situ conversion. Numerical simulation is an important means to explore the shale in situ conversion process, but it requires a lot of time and computational cost for in situ conversion simulation under different working conditions. Therefore, a computational framework for rapid prediction of shale in situ conversion development performance and heating parameter optimization is proposed by coupling artificial neural network (ANN) and particle swarm optimization (PSO). The results indicated that kerogen pyrolysis and hydrocarbon product release mainly occurred within 2 years of shale in situ conversion. The production curves of pyrolysis hydrocarbon obviously slowed after in situ conversion for 2 years. The database was constructed by a large number of in situ conversion simulations, and Pearson correlation analysis and the random forest method were adopted to obtain seven main controlling factors affecting reservoir temperature and hydrocarbon production. The determination coefficient of the obtained ANN-based prediction models is higher than 97%, and the mean square error (MSE) is lower than 0.3%. The basic reservoir case can choose to inject 350-450 °C supercritical CO2 (Sc-CO2) fluid with a rate of 600 m3/day to obtain a more promising development effect. The heating parameter optimization for three typical reservoir cases using PSO was performed, and reasonable injection temperature and injection rate were obtained. It realized accurate development prediction and rapid heating parameter optimization, which helps the effective application of shale in situ conversion development design.

Development and performance evaluation of intelligent algorithm for optimal control of a hybrid heat pump system during the cooling season

The purpose of this study is to develop a control method for a hybrid heat pump system based on an artificial neural network (ANN) to reduce energy use and create a more comfortable thermal environment. The proposed optimal control method uses an ANN-based predictive model to predict the heat storage tank and indoor temperature during the cooling period and controls the flow rate of the circulation pump on the heat source and load side of the system. The performance of the predictive model for the heat storage tank temperature (R2 = 0.9988; coefficient of variation of the root mean square error [CV(RMSE)] = 1.06 %; normalized mean bias error [NMBE] = 0.16 %; and mean absolute error [MAE] = 0.09℃) and the indoor temperature (R2 = 0.9893; CV(RMSE) = 1.66 %; NMBE = 0.16 %; and MAE = 0.15℃) was excellent. The temperature control of the heat storage tank using the optimal algorithm exhibited an improvement of 18.39 % for the CV(RMSE), 3.10 % for the NMBE, and 1.31 °C for MAE compared with rule-based. For the indoor temperature, the optimal algorithm improved the CV(RMSE) by 1.30 %, NMBE by 0.42 %, and MAE by 0.29℃ compared to rule-based. The energy use was reduced by 52.85 % for the entire system using the optimal control method compared with the existing control strategy under similar outdoor conditions. Using the proposed control method, it is thus possible to improve thermal comfort and reduce carbon emissions in the building sector by improving the control and energy performance of hybrid heat pump systems.

ANN-based prediction models for green water events around a FPSO in irregular waves

The green water problem has been a critical concern for the safety of offshore structures in extreme environmental conditions. However, accurately predicting the green water events using the conventional diffraction analysis method is challenging, primarily due to the highly nonlinear physics of the green water phenomenon. In this study, a novel methodology for the prediction green water events is proposed by integrating the linear diffraction method integrated with deep learning techniques. The proposed methodology is designed as a screening tool to predict the occurrence of green water events in terms of relative wave motions around the hull. In the proposed model, a linear prediction model is employed to account for the dominant physics of ship hydrodynamics and wave mechanics, while the ANN model is introduced to complement its nonlinear effects. First, the prediction performance was compared between the linear model and the ANN-based models for the peak value of relative wave motion. The ANN-based prediction model significantly reduced the prediction error of the linear prediction model. Moreover, the ANN model based on the time series itself shows a higher predictive performance than the ANN model using features extracted from the time series. Next, the predictive performance of the ANN model was analyzed when applied to different wave conditions. It was found that the prediction error greatly decreased even for different wave conditions. Finally, the screening performances of the prediction models are investigated. The ANN-based prediction model was found to be a more effective screening tool than the linear prediction model in terms of screening quality index.

Improving wastewater treatment plant performance: an ANN-based predictive model for managing average daily overflow and resource allocation optimization using Tabu search

Improving wastewater treatment plant performance: an ANN-based predictive model for managing average daily overflow and resource allocation optimization using Tabu search

Developing a computational toolbased on an artificial neural network for predicting and optimizing propolis oil, an important natural product for drug discovery.

Propolis is a promising natural product that has been extensively researched and studied for its potential health and medical benefits. The lack of requisite high oil-containing propolis and existing variation in the quality and quantity of essential oil within agro-climatic regions pose a problem in the commercialization of essential oil. As a result, the current study was carried out to optimize and estimate the essential oil yield of propolis. The essential oil data of 62 propolis samples from ten agro-climatic areas of Odisha, as well as an investigation of their soil and environmental parameters, were used to construct an artificial neural network (ANN) based prediction model. The influential predictors were determined using Garson's algorithm. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. The results revealed that the most suited model was multilayer-feed-forward neural networks with an R2 value of 0.93. According to the model, altitude was found to have a very strong influence on response, followed by phosphorous & maximum average temperature. This research shows that using an ANN-based prediction model with a response surface methodology technique to estimate oil yield at a new site and maximize propolis oil yield at a specific site by adjusting variable parameters is a viable commercial option. To our knowledge, this is the first report on the development of a model to optimize and estimate the essential oil yield of propolis.

Artificial Neural Network and Response Surface-Based Combined Approach to Optimize the Oil Content of Ocimum basilicum var. thyrsiflora (Thai Basil).

Ocimum basilicum var. thyrsiflora is valuable for its medicinal properties. The barriers to the commercialization of essential oil are the lack of requisite high oil-containing genotypes and variations in the quantity and quality of essential oils in different geographic areas. Thai basil's essential oil content is significantly influenced by soil and environmental factors. To optimize and predict the essential oil yield of Thai basil in various agroclimatic regions, the current study was conducted. The 93 datasets used to construct the model were collected from samples taken across 10 different agroclimatic regions of Odisha. Climate variables, soil parameters, and oil content were used to train the Artificial Neural Network (ANN) model. The outcome showed that a multilayer feed-forward neural network with an R squared value of 0.95 was the most suitable model. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. Garson's algorithm was used to discover the influential predictors. Soil potassium content was found to have a very strong influence on responses, followed by maximum relative humidity and average rainfall, respectively. The study reveals that by adjusting the changeable parameters for high commercial significance, the ANN-based prediction model with the response surface methodology technique is a new and promising way to estimate the oil yield at a new site and maximize the essential oil yield at a particular region. To our knowledge, this is the first report on an ANN-based prediction model for Ocimum basilicum var. thyrsiflora.

ANN for design and fabrication of ENi-P-nano TiO2 coatings on AH36 steel

Designing optimal ENi-P-nano TiO2 with higher surface hardness is time-consuming and expensive. Thus, this study is aims to develop the optimal optimiser-based ANN model to forcast Vickers hardness for newer process parametric combinations and find the ideal combination before manufacture. Consequently, three ANN model were devised using ADAM, AdaMax, and RMSprop optimisers with identical data set for training and testing. mean Squared Error (MSE) and R 2 values were used to compare and assess their performance. Rectified Linear Unit (ReLU) is used to provide nonlinearity to the neural network's output since the activation function affects ANN model performance. The study also highlights hyperparameter setting's impact on optimiser performance. The ADAM-based ANN model was determined as optimal design through comparing the predicted and experimental Vickers hardness for the unknown combinations using Taguchi DOE. In addition, FESEM and XRD micrographs confirmed the optimal coating's morphology and phases. This study results shown the compatibility of utilising ANN-based prediction models for ENi-P-nanoTiO2 coating design and prediction, however future research much incoporates more process parameters and their level ranges.

SBLMD–ANN–MOPSO-based hybrid approach for determining optimum parameter in CNC milling

Chatter is a type of self-excited vibration that expresses variations in frequency and energy dispersion during the milling process and invariably results in poor part quality and a lower material removal rate. An efficacious chatter detection approach is necessary to anticipate the chatter's nascent stage. Feature extraction is an important step in identifying chatter. In this paper, an efficient PF-based multi-mode signal processing method, i.e., Spline-Based Local Mean Decomposition (SBLMD), has been used to decompose the experimentally acquired sound signals into a series of PFs and then, selected PFs have been used to reconstruct the new chatter signal, which is rich in information. Further, two three-layer ANN-based prediction models are established to predict the CI and MRR. Three process-related variables, such as tool speed, feed speed, and cut depth, have been used to develop ANN models. Statistical comparisons have been conducted to obtain the optimal training algorithm and have found that the LM-based training algorithm was the best among the five other training algorithms. For the estimates of CI and MRR, the neurons in the hidden layer are optimized at 5 and 7 with the least mean squared errors (MSE), respectively. After applying MOPSO to ANN-based prediction models, the MOPSO-optimized ANNs are very sensitive to optimizing input layers. The optimal ranges for the Cut Depth (CD), Feed Speed (FS) and Tool Speed (TS) are 1.56–1.77 mm, 77–97 mm/min, and 2380–2880 rpm, respectively.

Performance Comparison of ANN-Based Model and Empirical Correlations for Void Fraction Prediction of Subcooled Boiling Flow in Vertical Upward Channel

The accurate prediction of void fraction parameter in subcooled boiling flow is very important for nuclear safety since it has significant influences on the mass flow rate, the onset of two-phase flow instability, and the heat transfer characteristics in a nuclear reactor core. Many different models and empirical correlations have been established over a variety of input conditions; however, this classical approach could lead to unsatisfactory prediction due to the uncertainties of model parameter and model forms. To cope with these limitations, Artificial Neural Network (ANN) is a powerful machine learning tool for modeling and solving non-linear and complicated physical problems. Therefore, this work is aim at developing an ANN-based model to predict the local void fraction of subcooled boiling flows. The comparison results of the performance between the ANN-based model and empirical correlations for the void fraction prediction of subcooled boiling in vertical upward channel showed the potential use of ANN-based model in the Computational Fluid Dynamics (CFD) codes to accurately simulate the subcooled boiling phenomena.

Comparing Artificial Neural Networks with Multiple Linear Regression for Forecasting Heavy Metal Content

This paper adopts two modeling tools, namely, multiple linear regression (MLR) and artificial neural networks (ANNs), to predict the concentrations of heavy metals (zinc, boron, and manganese) in surface waters of the Oued Inaouen watershed flowing towards Inaouen, using a set of physical-chemical parameters. XLStat was employed to perform multiple linear and nonlinear regressions, and Statista 10 was chosen to construct neural networks for modeling and prediction. The effectiveness of the ANN- and MLR-based stochastic models was assessed by the determination coefficient (R²), the sum squared error (SSE) and a review of fit graphs. The results demonstrate the value of ANNs for prediction modeling. Drawing on supervised learning and back propagation, the ANN-based prediction models adopt an architecture of [18-15-1] for zinc, [18-11-1] for manganese, and [18-8-1] for boron, and perform effectively with a single cached layer. It was found that the MLR-based prediction models are substantially less accurate than those based on the ANNs. In addition, the physical-chemical parameters being investigated are nonlinearly correlated with the levels of heavy metals in the surface waters of the Oued Inaouen watershed flowing towards Inaouen.

Two-Stage stochastic optimization for operating a Renewable-Based Microgrid

• A conventional MILP algorithm does not consider the uncertainty in operating a renewable-based Microgrid. • The two-Stage stochastic optimization algorithm takes into consideration the potential future PV profile. • The proposed method improves the Microgrid reliability by a 19.4% decrease in the Energy not supplied. • The proposed method decreases the operation cost by 18.8%. Carbon emissions are increasing as a result of urbanization and population growth all over the world. Scientists agree that these emissions are one of the main causes of climate change and global warming. The power industry is shifting to renewable energy sources (RES) such as solar power (PV) and employing different Energy Storage Systems (ESS) to enable a clean energy future and compensate for the scarcity of fossil fuel. However, the intermittent behaviour of renewable resources causes some obstacles such as power fluctuation, committing extra reserve units, and load shedding. Microgrid (MG) technology is introduced as a promising solution for integrating different RES and loads into the grid. Operating the MG in islanded mode with a limited ESS capacity requires a sophisticated scheduling method. Previous studies on MG addressed the operation issue, neglecting the supply–demand uncertainty or adopting the worst-case scenario. Uncertainty is an inherent characteristic in power systems; on the other hand, considering the worst-case scenario may unnecessarily increase the operation and planning costs. To address the uncertainty and fluctuant characteristics of RES-based MG, this paper proposes a two-stage stochastic optimization integrated with a novel ANN-based prediction model. A new model for PV power prediction is proposed by which the predicted data is in a probability density function (PDF) form. A stochastic optimization (SO) method is proposed to minimize the operation cost and load shedding during the islanding mode. In the proposed SO, the optimal scheduling decision is made in the current moment taking into account the probability of potential supply, load, and ESS capacities in the near future. For this purpose, an ANN-based prediction model is developed to represent the PV output uncertainty in the SO problem. The proposed prediction model proves efficient in the prediction with nRMSE of 9.7% and nMAE of 9.1%. The proposed method is applied to a real Microgrid designed by the Natural Energy Laboratory of Hawaii Authority (NELHA) and compared to a conventional optimization method. The proposed scheduling method reduces both the average Energy Not Supplied (ENS) and operation costs by 19.4% and 18.8%, respectively, with no additional investment cost. A sensitivity study is also conducted to assess the performance of the proposed method in terms of ENS, cost, and simulation time.

Deployment of the Microeconomic Consumer Theory in the Artificial Neural Networks Modelling: Case of Organic Food Consumption

Organic food consumption has become a significant trend in consumer behaviour, determined by various motives, among which the price does not play a major role, thus reflecting the Lancaster approach to the microeconomic consumer theory. Additionally, artificial neural networks (ANNs) have proven to have significant potential in providing accurate and efficient models for predicting consumer behaviour. Considering these two trends, this study aims to deploy the Lancaster approach in the emerging area of artificial intelligence. The paper aims to develop the ANN-based predictive model to investigate the relationship between organic food consumption, demographic characteristics, and health awareness attitudes. Survey research has been conducted on a sample of Croatian inhabitants, and ANN models have been used to assess the importance of various determinants for organic food consumption. A Three-layer Multilayer Perceptron Neural Networks (MLPNN) structure has been constructed and validated to select the optimal number of neurons and transfer functions. One layer is used as the first input, while the other two are hidden layers (the first covers the radially symmetrical input, sigmoid function; the second covers the output, softmax function). Three versions of the testing, training, and holdout data structures were used to develop ANNs. The highest accuracy was achieved with a 7-2-1 partition. The best ANN model was determined as the model that was showing the smallest percent of incorrect predictions in the holdout stage, the second-lowest cross-entropy error, the correct classification rate, and the area under the ROC curve. The research results show that the availability of healthy food shops and consumer awareness of these shops strongly impacts organic food consumption. Using the ANN methodology, this analysis confirmed the validity of the Lancaster approach, stating that the characteristics or attributes of goods are defined by the consumer and not by the product itself.

ANN-based model for multiband path loss prediction in built-up environments

Path loss propagation models are critically needed for optimum planning and deployment of wireless communication networks. However, the complexity exhibited by the propagated signals makes the prediction of the received losses difficult in built-up environments. There is however a new paradigm shift towards the application of computational methodologies, such as the Artificial Neural Networks (ANN), for multi-band path loss prediction. In this paper, we have developed a new ANN-based model for path loss prediction. The model was developed using large scale path loss data collected across 485 base stations in 6 urban cities of Nigeria, West Africa. The data collection, which spanned a period of 9 years, were taken over open areas, sub urban and urban environments, and the bands considered were 89.3 MHz, 103.5 MHz, 203.25MHz, 429.25 MHz, 529.25MHz, 615.25MHz, 629.25MHz, 900MHz, 1800 MHz and 2100MHz. The developed model was validated using independent path loss data across different frequencies, environments; and, distances and the results were compared with the popular empirical models such as Hata, COST 231 and Egli models, and to previously published ANN-based multi-frequency models. The global performance of 4.81 dB was obtained in terms of RMSE value with an R-value of 0.96, thus outperforming the existing ANN-based path loss models that were developed for multiple frequencies. Based on these findings, the proposed model can be deployed across all categories since the average RMSE values are all within the acceptable thresholds. Furthermore, the model is multi-frequency, thus will be suitable for multiple and complex environments, and usable for both short- and long-range wireless communication networks

Development of Predictive Models for Determination of the Extent of Damage in Granite Caused by Thermal Treatment and Cooling Conditions Using Artificial Intelligence

Thermal treatment followed by subsequent cooling conditions (slow and rapid) can induce damage to the rock surface and internal structure, which may lead to the instability and failure of the rock. The extent of the damage is measured by the damage factor (DT), which can be quantified in a laboratory by evaluating the changes in porosity, elastic modulus, ultrasonic velocities, acoustic emission signals, etc. However, the execution process for quantifying the damage factor necessitates laborious procedures and sophisticated equipment, which are time-consuming, costly, and may require technical expertise. Therefore, it is essential to quantify the extent of damage to the rock via alternate computer simulations. In this research, a new predictive model is proposed to quantify the damage factor. Three predictive models for quantifying the damage factors were developed based on multilinear regression (MLR), artificial neural networks (ANNs), and the adoptive neural-fuzzy inference system (ANFIS). The temperature (T), porosity (ρ), density (D), and P-waves were used as input variables in the development of predictive models for the damage factor. The performance of each predictive model was evaluated by the coefficient of determination (R2), the A20 index, the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the variance accounted for (VAF). The comparative analysis of predictive models revealed that ANN models used for predicting the rock damage factor based on porosity in slow conditions give an R2 of 0.99, A20 index of 0.99, RMSE of 0.01, MAPE of 0.14, and a VAF of 100%, while rapid cooling gives an R2 of 0.99, A20 index of 0.99, RMSE of 0.02, MAPE of 0.36%, and a VAF of 99.99%. It has been proposed that an ANN-based predictive model is the most efficient model for quantifying the rock damage factor based on porosity compared to other models. The findings of this study will facilitate the rapid quantification of damage factors induced by thermal treatment and cooling conditions for effective and successful engineering project execution in high-temperature rock mechanics environments.

Prediction of Photovoltaic Power by ANN Based on Various Environmental Factors in India

Extreme weather conditions, which affect photovoltaic output power, can have a major impact on electricity generated by PV systems. In India, an annual PV power density of 2000kWh/m-2 may be used. Renewable energy (RE) is expected to play a rising part in the nations in coming years. The sun’s radiation is the primary source of renewable energy (RE). With the objective of predicting PV output power with the least amount of error in mind, it is vital to analyse the impact of major environmental parameters on it. The researchers looked at a variety of environmental factors in this study, including irradiance, humidity levels, meteorological conditions, wind velocity, PV global temperature and dust deposition. Countries such as India would gain immensely from this since it will increase the quantity of PV power generated in their national networks. ANN-based prediction models and multiple regression models were used to predict PV system hourly power output. There were three ANN models that predicted PV output power with RMSEs of 2.1436, 6.1555, and 5.3551, respectively, utilising all features using the correlation feature selection (CFS) or relief feature selection (ReliefF) approaches. It is possible to reduce bias to enhance accuracy by employing two distinct bias calculation methodologies, which were applied in this study. For example, the ANN model outperforms linear regression, M5P decision trees and GAUSSIAN process regression (GPR) models in terms of performance.

Numerical investigation and ANN-based prediction on compressive strength and size effect using the concrete mesoscale concretization model

The concrete mesoscale concretization model (CMCM) is proposed using the statistical distribution and local spatial correlation theory to describe the randomization, continuum, and correlation of the material properties of mesoscale components. The distribution of material properties was obtained by stochastic discretization, spatial correlation correction, and remapping operations. The hypotheses of elastic modulus control (EMC) and characteristic deformation control (CDC) are used to describe the variation of mechanical parameters in the concrete damage plasticity model. The uniaxial compression behavior and size effect of concrete with different side lengths, aspect ratios, aggregate contents, air contents, and contact frictions are simulated using the above model. Without the contact frictions, the size effect of the sample’s side length is apparent, while the size effect of aspect ratio is not observed. The increase of size effect degree is contributed by the increase of air content rather than aggregate content. When the loading boundaries are constrained by friction, both the side length and aspect ratio of samples show the size effect. The increase of aggregate content leads to the increase of size effect degree, while the increase of air content leads to a minor increase of size effect degree. The developed CMCM can capture the mechanical responses, and the ANN-based prediction model is applicable to explore the relationship between compressive strength and sample parameters.

Improvement in shop floor management using ANN coupled with VSM: A case study

In the present article, the authors have employed the Value Stream Mapping (VSM) technique for the existing shop floor process in an earthmoving equipment manufacturing unit. Thereafter, an Artificial Neural Network (ANN)-based information processing technique has been used for generating a prediction model of shop floor management. For developing the ANN-based prediction model, the production time involved in different processes has been collected for 31 working days. This collected data has been used for training and testing the ANN model. Thereafter, to validate the developed ANN model, more 7 days data has been collected and compared with the predicted values of model for the same input attributes. From the results, it has been found that the performance of the developed model is highly adequate for the prediction purpose with the MSE and MAE values for training data and testing data as 0.0008105545 and 0.0000008979 and 0.01012315 and 0.0001658978, respectively. Based on the acquired results it is evident that the proposed methodology may be significant in predicting the production time of the anticipated shop floor.