Artificial Neural Network Training Algorithm Research Articles

A Comparative Study of Artificial Neural Network Training Algorithms to Predict Photovoltaic Module Output Power

Introduction: Solar energy is a crucial component and contributes a large portion of renewable resources, the demand for which has recently increased. During previous years, it was difficult to predict the amount of energy obtained from photovoltaic systems. The angle of inclination of the solar panel, the amount of radiation, the speed of the wind, the amount of humidity, and the temperature of the weather are major factors that effectively affect the production of electrical energy. Scientists have used many strategies to predict the power generated by PV modules accurately, but each method has different pros and cons. Method: This study tested three training algorithms for artificial neural networks (ANN): scaled conjugate gradient (SCG), Levenberg Marquardt (LM), and Bayesian regularization (BR), determining which one performed best in terms of prediction speed and accuracy. Twenty-eight thousand two hundred ninety-six samples of experimental data for primary influencing environmental factors were fed into the artificial neural network, which consists of 15 hidden layers. Before training the network, we preprocessed the data to remove factors that have a secondary effect. Result: The analytical results showed that the artificial neural network trained according to the LM algorithm is the best in terms of accuracy and speed of predicting the resulting photovoltaic energy. Conclusion: The results showed that although the regression evaluation and MSE values for the LM, SCG, and BR algorithms are close (98.129%, 0.0622, 97.849%, 0.0587, 98.151%, and 0.0585, respectively), the LM training algorithm is the best in terms of speed of calculation and display of results.

Prediction of monkeypox infection from clinical symptoms with adaptive artificial bee colony-based artificial neural network

In 2022, the World Health Organization declared an outbreak of monkeypox, a viral zoonotic disease. With time, the number of infections with this disease began to increase in most countries. A human can contract monkeypox by direct contact with an infected human, or even by contact with animals. In this paper, a diagnostic model for early detection of monkeypox infection based on artificial intelligence methods is proposed. The proposed method is based on training the artificial neural network (ANN) with the adaptive artificial bee colony algorithm for the classification problem. In the study, the ABC algorithm was preferred instead of classical training algorithms for ANN because of its effectiveness in numerical optimization problem solutions. The ABC algorithm consists of food and limit parameters and three procedures: employed, onlooker and scout bee. In the algorithm standard, artificial onlooker bees are produced as much as the number of artificially employed bees and an equal number of limit values are assigned for all food sources. In the advanced adaptive design, different numbers of artificial onlooker bees are used in each cycle, and the limit numbers are updated. For effective exploitation, onlooker bees tend toward more successful solutions than the average fitness value of the solutions, and limit numbers are updated according to the fitness values of the solutions for efficient exploration. The performance of the proposed method was investigated on CEC 2019 test suites as examples of numerical optimization problems. Then, the system was trained and tested on a dataset representing the clinical symptoms of monkeypox infection. The dataset consists of 240 suspected cases, 120 of which are infected and 120 typical cases. The proposed model's results were compared with those of ten other machine learning models trained on the same dataset. The deep learning model achieved the best result with an accuracy of 75%. It was followed by the random forest model with an accuracy of 71.1%, while the proposed model came third with an accuracy of 71%.

Obtaining an accurate prediction model for viscosity of a new nano-lubricant containing multi-walled carbon nanotube-titanium dioxide nanoparticles with oil SAE50

This study aims to investigate the viscosity behavior of multi-walled carbon nanotube (MWCNT) - titanium dioxide (TiO2) (40−60) - SAE50 oil nanofluid using an Artificial Neural Network (ANN) modeling approach. The main objective is to develop a highly accurate predictive model for viscosity by considering three input parameters: temperature, solid volume fraction (SVF), and shear rate (SR). Rheological measurements provide experimental data used to train and validate the ANN model. The ANN model's architecture, activation functions, and training algorithms are carefully chosen. Data are divided to three subsets including train, validation and test. ANN is trained using trainlm algorithm for 50 times to vanish the effect of random nature of ANN weight initialization. The trained ANN model is then utilized to predict the viscosity of the nanofluid under varying conditions. The results demonstrate the efficacy of the proposed ANN model in capturing the complex relationship between viscosity and the input parameters, providing accurate viscosity predictions for the MWCNT-TiO2-oil SAE50 nanofluid. Furthermore, the influence of temperature, SVF, and SR on viscosity is analyzed, offering valuable insights into the flow behavior of the nanofluid. According to the obtained results, the developed ANN model presents a reliable and efficient approach to estimate the viscosity of the MWCNT-TiO2-SAE50 oil nanofluid, eliminating the need for costly and extensive experimental measurements within the analyzed range. ANN could model the nanofluid viscosity with R2 = 0.9998 and MSE= 0.000189 that is quite acceptable. Also, the experimental data revealed that for the investigated nanofluid, temperature and shear rate have impressive effect on the viscosity (changing viscosity more than 100% for the analyzed margin), on the other hand, the nanoparticle volume fraction effect is much lower, to be more precise, increasing the nanoparticle percentage will increase the viscosity mean value around 30%.

Learning cortical hierarchies with temporal Hebbian updates.

A key driver of mammalian intelligence is the ability to represent incoming sensory information across multiple abstraction levels. For example, in the visual ventral stream, incoming signals are first represented as low-level edge filters and then transformed into high-level object representations. Similar hierarchical structures routinely emerge in artificial neural networks (ANNs) trained for object recognition tasks, suggesting that similar structures may underlie biological neural networks. However, the classical ANN training algorithm, backpropagation, is considered biologically implausible, and thus alternative biologically plausible training methods have been developed such as Equilibrium Propagation, Deep Feedback Control, Supervised Predictive Coding, and Dendritic Error Backpropagation. Several of those models propose that local errors are calculated for each neuron by comparing apical and somatic activities. Notwithstanding, from a neuroscience perspective, it is not clear how a neuron could compare compartmental signals. Here, we propose a solution to this problem in that we let the apical feedback signal change the postsynaptic firing rate and combine this with a differential Hebbian update, a rate-based version of classical spiking time-dependent plasticity (STDP). We prove that weight updates of this form minimize two alternative loss functions that we prove to be equivalent to the error-based losses used in machine learning: the inference latency and the amount of top-down feedback necessary. Moreover, we show that the use of differential Hebbian updates works similarly well in other feedback-based deep learning frameworks such as Predictive Coding or Equilibrium Propagation. Finally, our work removes a key requirement of biologically plausible models for deep learning and proposes a learning mechanism that would explain how temporal Hebbian learning rules can implement supervised hierarchical learning.

Optimal parameter design of a slot jet impingement/microchannel heat sink base on multi-objective optimization algorithm

Jet impingement and microchannel cooling as effective cooling methods have been widely employed for thermal management of high heat flux electronics. In this work, a novel hybrid slot jet impingement/microchannel heat sink, combining the advantages of two cooling technologies, was proposed to achieve vertical flushing of the jet and timely discharge of the waste fluid. To further enhance the cooling performance, a 3-D numerical simulation was applied to evaluate the impact of inlet and outlet branch passage inclination ratios (Hin,1/Hin,2 and Hout,1/Hout,2), channel unit width (W1), channel width ratio (φ) and channel height (H3) on heat sink thermal-hydraulic characteristics. According to the parametric analysis, the variations in the inlet and outlet branch passage inclination ratios have significant effect on the mass flow distribution among the heat transfer channels. Afterward, to acquire the optimal compromise solution for practical applications, the artificial neural network training and multi-objective optimization algorithms, i.e. multi-objective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA), are utilized to optimize the geometrical parameters, i.e. W1, φ and H3. During the optimization, the average heated surface temperature (T¯heat) and the total pressure drop (ΔP) are two conflicting objectives to evaluate the heat transfer and resistance characteristics. Finally, an optimal compromise solution (W1=1.03mm, φ=0.58 and H3=2.44mm) is chosen from the Pareto front by using the classical decision-making algorithm, i.e. TOPSIS. Compared with one objective minimization in the Pareto front, the optimal compromise solution has a sound balance between heat transfer and resistance performance. Additionally, for the optimal compromise solution, with an inlet velocity of 1.5 m/s and a heat flux of 200 W/cm2, the maximum and average temperature of the heated surface does not exceed 73 °C and 67 °C, respectively, and the total pressure drop is only 5.7 kPa.

Comparison of Various Prediction Model for Biodiesel Cetane Number using Cascade-Forward Neural Network

Cetane number (CN) is one of the important fuel properties of diesel fuels. It is a measurement of the ignition quality of diesel fuel. Numerous studies have been published to predict the CN of biodiesels. More recently, the utilization of soft computing methods such as artificial neural networks (ANN) has received considerable attention as a prediction tool. However, most studies in the use of ANN for estimating the CN of biodiesels have only used one algorithm to train a small number of datasets. This study aims to predict the CN of 63 biodiesels based on the fatty acid methyl esters (FAME) composition by developing an ANN model that was trained with 10 different algorithms. To the best of our knowledge, this is the first study to predict the CN of biodiesels using numerous ANN training algorithms utilizing sizeable datasets. Results revealed that the ANN model trained with Levenberg-Marquardt gave the highest prediction accuracy. LM algorithm successfully predicted the CN of biodiesels with the highest correlation and determination coefficient (R = 0.9615, R2 = 0.9245) as well as the lowest errors (MAD = 2.0804, RMSE = 3.1541, and MAPE = 4.2971). Hence, the Cascade neural network trained with the LM algorithm could be considered a promising alternative to the empirical correlations for predicting biodiesel’s CN.

FORECASTING NIGERIAN ECONOMIC GROWTH BASED CORPORATE INCOME TAX

Developing countries like Nigeria have been hit hard by recent economic crises, and uncertainty is one of the characteristics that all prices and revenues share. A forecast of Nigerian economic growth was attempted using an artificial neural network (ANN) and statistical model to analyse the contribution of non-oil income tax generation to Nigerian economic development. While the primary objective is to develop and implement the proposed models capable of simulating a real non-oil income tax and evaluate their performances. The dataset (Corporate Income Tax) from 2015–2020 was obtained from the National Bureau of Statistics of Nigeria (NBSN). Three training algorithms for ANN were adopted, such as conjugate gradient back-propagation with Fletcher-Reeves restarts, Bayesian regularisation, and gradient descent with an adaptive learning rate, whereas in the statistical part, multiple linear regressions were applied. Comparing all the models revealed that the Bayesian regularisation produced more accurate results than the other models.

Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19.

The exponential spread of coronavirus disease 2019 (COVID-19) causes unexpected economic burdens to worldwide health systems with severe shortages in hospital resources (beds, staff, equipment). Managing patients' length of stay (LOS) to optimize clinical care and utilization of hospital resources is very challenging. Projecting the future demand requires reliable prediction of patients' LOS, which can be beneficial for taking appropriate actions. Therefore, the purpose of this research is to develop and validate models using a multilayer perceptron-artificial neural network (MLP-ANN) algorithm based on the best training algorithm for predicting COVID-19 patients' hospital LOS. Using a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020 to December 20, 2020 were analyzed. In this study, first, the correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at a P-value < 0.2 were used in model construction. Then, the prediction models were developed based on 12 training algorithms according to full and selected feature datasets (90% of the training, with 10% used for model validation). Afterward, the root mean square error (RMSE) was used to assess the models' performance in order to select the best ANN training algorithm. Finally, a total of 343 patients were used for the external validation of the models. After implementing feature selection, a total of 20 variables were determined as the contributing factors to COVID-19 patients' LOS in order to build the models. The conducted experiments indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian regularization (BR) training algorithm for whole and selected features with an RMSE of 1.6213 and 2.2332, respectively. MLP-ANN-based models can reliably predict LOS in hospitalized patients with COVID-19 using readily available data at the time of admission. In this regard, the models developed in our study can help health systems to optimally allocate limited hospital resources and make informed evidence-based decisions.

Comparison of Artificial Neural Network Training Algorithms for Predicting the Weight of Kurdi Sheep using Image Processing

Comparison of Artificial Neural Network Training Algorithms for Predicting the Weight of Kurdi Sheep using Image Processing

Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms

The conventional experimental methods to determine biomass heating value are laborious and costly. Numerous correlations to estimate biomass' higher heating values have been proposed using proximate analysis. Recently, the utilisation of artificial neural network (ANN) has been extensively applied to predict HHV. However, most studies of ANN to estimate the biomass’ HHV only use one algorithm to train a small number of biomass datasets. The specific objective of this study is to predict the HHV of 350 samples of biomass from the proximate analysis by developing an ANN model which was trained with 11 different algorithms. This study fills a gap in the research on how to predict the HHV of biomass using numerous ANN training algorithms utilising sizeable biomass datasets. Results show that the ANN trained with Levenberg-Marquardt gave the highest accuracy. The Levenberg–Marquardt algorithm shows the best fit giving the highest R and R2 values and the lowest MAD, MSE, RMSE and MAPE. Compared with previous biomass HHV prediction studies, the ANN model developed in this study provides improved prediction accuracy with higher R2 and lower RMSE. Results from this study have also indicated that the Levenberg-Marquardt should be the first-choice supervised algorithm for feedforward-backpropagation.

Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods

The modern-day urban energy sector possesses the integrated operation of various microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility grid burden. However, these cluster microgrids require a precise electric load projection to manage the operations, as the integrated operation of multiple microgrids leads to dynamic load demand. Thus, load forecasting is a complicated operation that requires more than statistical methods. There are different machine learning methods available in the literature that are applied to single microgrid cases. In this line, the cluster microgrids concept is a new application, which is very limitedly discussed in the literature. Thus, to identify the best load forecasting method in cluster microgrids, this article implements a variety of machine learning algorithms, including linear regression (quadratic), support vector machines, long short-term memory, and artificial neural networks (ANN) to forecast the load demand in the short term. The effectiveness of these methods is analyzed by computing various factors such as root mean square error, R-square, mean square error, mean absolute error, mean absolute percentage error, and time of computation. From this, it is observed that the ANN provides effective forecasting results. In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The effectiveness of these optimization algorithms is verified in terms of training, test, validation, and error analysis. The proposed system simulation is carried out using the MATLAB/Simulink-2021a® software. From the results, it is found that the Levenberg–Marquardt optimization algorithm-based ANN model gives the best electrical load forecasting results.

Neural network based autonomous control of a speech synthesis system

• Control of composite tasks by adaptive heuristically enhanced gradient approximation. • Training an artificial neural network (ANN) to control the parameters of a speech synthesizer. • The training error is estimated from the “quality” of the produced speech and not at the outputs of the ANN. • Currently used ANN training algorithms cannot cope with the above requirement. • The presented methodology may be applied to other applications too, e.g., coordination of robotic manipulators. This work is inspired by the ability of neural systems to control the behavior of specialized actuator mechanisms in living organisms by monitoring the end-effect of their actions. We consider as an example of such an actuator mechanism the human vocal tract where neurons learn to activate its muscles that move the velum, jaw, tongue and the lips, in order to exhibit desired phonetic activity. As a technical approximation to such a setup, we use an artificial neural network (ANN) and a speech synthesizer and we study the capability of the ANN to estimate the synthesizer’s parameters targeting desired speech activity. In this setup, we assume that the training error is obtained by measuring the “perceived distance” between the original (target) and the synthesized speech signals. Thus, the training error needs to be measured after processing the output of the speech synthesizer, instead of measuring it directly at the outputs of the ANN. This operational requirement on error measurement restricts the application of widely used ANN training algorithms that are based on back propagation of gradients but can be met by our earlier proposed “Heuristically Enhanced Gradient Approximation” (HEGA) algorithm. We also propose enhancements to HEGA that further optimize its performance in this demanding application.

Introducing principles of synaptic integration in the optimization of deep neural networks

Plasticity circuits in the brain are known to be influenced by the distribution of the synaptic weights through the mechanisms of synaptic integration and local regulation of synaptic strength. However, the complex interplay of stimulation-dependent plasticity with local learning signals is disregarded by most of the artificial neural network training algorithms devised so far. Here, we propose a novel biologically inspired optimizer for artificial and spiking neural networks that incorporates key principles of synaptic plasticity observed in cortical dendrites: GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals). GRAPES implements a weight-distribution-dependent modulation of the error signal at each node of the network. We show that this biologically inspired mechanism leads to a substantial improvement of the performance of artificial and spiking networks with feedforward, convolutional, and recurrent architectures, it mitigates catastrophic forgetting, and it is optimally suited for dedicated hardware implementations. Overall, our work indicates that reconciling neurophysiology insights with machine intelligence is key to boosting the performance of neural networks.

Artificial neural network (ANN) modeling for CO2 adsorption on Marcellus Shale

In this work, artificial neural network modeling for CO2 adsorption on various types of Marcellus shale samples is studied. The eight shale geometries are investigated for their CO2 adsorption at 298k and up to 50bar pressure utilizing a gravimetric technique and magnetic suspension balance. ANN modelling was applied to investigate three main objectives which are the impact of various training algorithms, various data initiation points, and altered training/validating ratios and number of neurons required for ANN model. The work can provide insightful knowledge linked to the impact of each of the studied parameters which play an important role in ANN modeling and training algorithms. The outcomes can provide an optimized matrix for unconventional resources, enhanced gas and oil recovery applications intend to apply the artificial intelligence modeling in their assessments.

Scaling an Artificial Neural Network-Based Water Quality Index Model from Small to Large Catchments

Scaling models is one of the challenges for water resource planning and management, with the aim of bringing the developed models into practice by applying them to predict water quality and quantity for catchments that lack sufficient data. For this study, we evaluated artificial neural network (ANN) training algorithms to predict the water quality index in a source catchment. Then, multiple linear regression (MLR) models were developed, using the predicted water quality index of the ANN training algorithms and water quality variables, as dependent and independent variables, respectively. The most appropriate MLR model has been selected on the basis of the Akaike information criterion, sensitivity and uncertainty analyses. The performance of the MLR model was then evaluated by a variable aggregation and disaggregation approach, for upscaling and downscaling proposes, using the data from four very large- and three large-sized catchments and from eight medium-, three small- and seven very small-sized catchments, where they are located in the southern basin of the Caspian Sea. The performance of seven artificial neural network training algorithms, including Quick Propagation, Conjugate Gradient Descent, Quasi-Newton, Limited Memory Quasi-Newton, Levenberg–Marquardt, Online Back Propagation, and Batch Back Propagation, has been evaluated to predict the water quality index. The results show that the highest mean absolute error was observed in the WQI, as predicted by the ANN LM training algorithm; the lowest error values were for the ANN LMQN and CGD training algorithms. Our findings also indicate that for upscaling, the aggregated MLR model could provide reliable performance to predict the water quality index, since the r2 coefficient of the models varies from 0.73 ± 0.2 for large catchments, to 0.85 ± 0.15 for very large catchments, and for downscaling, the r2 coefficient of the disaggregated MLR model ranges from 0.93 ± 0.05 for very large catchments, to 0.97 ± 0.02 for medium catchments. Therefore, scaled models could be applied to catchments that lack sufficient data to perform a rapid assessment of the water quality index in the study area.

A novel ensemble method based on the SBLMD-ANN-MOPSO approach for predicting milling stability regimes

In recent decades, a lot of work has been done to mitigate self-excited vibration effects in milling operations. Still, a robust methodology is yet to be developed that can suggest stability bounds pertaining to higher metal removal rates (MRRs). In the present work, experimentally-acquired acoustic signals in milling operations have been computed using a modified spline-based local mean decomposition technique in order to cite tool chatter features. Further, three artificial neural network training algorithms; resilient propagation, conjugate gradient-based and Levenberg–Marquardt algorithms, and two activation functions; hyperbolic tangent sigmoid and log sigmoid, have been used to train the acquired chatter vibration and MRR data set. Over-fitting or under-fitting issues may arise from the random selection of a number of hidden neurons. The solution to these problems is also proposed in this paper. Among these training algorithms and activation functions, a suitable one has been selected and further invoked to develop prediction models of chatter severity and MRR. Finally, multi-objective particle swarm optimization has been invoked to optimize the developed prediction models to obtain the most favourable range of input parameters pertaining to stable milling with higher productivity.

Solving the Protein Secondary Structure Prediction Problem With the Hessian Free Optimization Algorithm

Trying to extract features from complex sequential data for classification and prediction problems is an extremely difficult task. This task is even more challenging when both the upstream and downstream information of a time-series is important to process the sequence at a specific time-step. One typical problem which falls in this category is Protein Secondary Structure Prediction (PSSP). Recurrent Neural Networks (RNNs) have been successful in handling sequential data. These methods are demanding in terms of time and space efficiency. On the other hand, simple Feed-Forward Neural Networks (FFNNs) can be trained really fast with the Backpropagation algorithm, but in practice they give poor results in this category of problems. The Hessian Free Optimization (HFO) algorithm is one of the latest developments in the field of Artificial Neural Network (ANN) training algorithms which can converge faster and more accurately. In this paper, we present the implementation of simple FFNNs trained with the powerful HFO second-order learning algorithm for the PSSP problem. In our approach, a single FFNN trained with the HFO learning algorithm can achieve an approximately 79.6% per residue ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q_{3}$ </tex-math></inline-formula> ) accuracy on the PISCES dataset. Despite the simplicity of our method, the results are comparable to some of the state of the art methods which have been designed for this problem. A majority voting ensemble method and filtering with Support Vector Machines have also been applied, which increase our results to 80.4% per residue ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q_{3}$ </tex-math></inline-formula> ) accuracy. Finally, our method has been tested on the CASP13 independent dataset and achieved 78.14% per residue ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q_{3}$ </tex-math></inline-formula> ) accuracy. Moreover, the HFO does not require tuning of any parameters which makes training much faster than other state of the art methods, a very important feature with big datasets and facilitates fast training of FFNN ensembles.

Clean technology for sequestering Rhodamine B dye on modified mango pod using artificial intelligence techniques

In this study, an acid-modified mango pod (AMMP) was prepared as adsorbent for removal of Rhodamine dye and the adsorption uptake was also determined using machine learning algorithms namely, Artificial Neural network (ANN) and Adaptive neuro-fuzzy inference systems (ANFIS) respectively. The prepared adsorbent was characterized with SEM, FTIR, EDX, and PXRD techniques. Surface chemistry revealed the presence of O–H stretching of free hydroxyls and alcohols and phenols, C–H stretch of alkanes, C≡C stretch of alkynes, CO stretch of ketones, lactones, and carboxylic anhydrides, –CC- stretching of alkenes, C–N stretch of aliphatic amines and O–H bend of carboxylic acids. Surface morphologies of the AMMP revealed well-developed and open porous surfaces needed for an efficient adsorption of the dye molecule. EDX analysis showed an increase in the carbon contents from 79.94% (raw) to 80.12% (AMMP) by weight and 86.89% (raw) to 89.11% (AMMP) by atom. The crystallinity structure revealed the new and intense peak formations and the presence of ordered (organized) crystalline structures on AMMP. Optimal ANN architectures, activation functions and training algorithms were selected after several stimulations with different network parameters while the ANFIS model was tested with three clustering approaches namely, Grid partitioning, fuzzy c-means, and subtractive clustering to carry out the extensive studies to optimally predict the adsorption efficiency/capacity of Rhodamine dye onto AMMP. The performance of the developed models was evaluated using the following statistical metrics; the optimal ANN model gave RMSE ​= ​10.422, MAD ​= ​3.673, MAPE ​= ​5.409, R2 ​= ​0.977 while with optimal ANFIS model gave a RMSE ​= ​9.246, MAD ​= ​4.938, MAPE ​= ​4.672, R2 ​= ​0.984 ​at the testing phase. The lower value of the statistical parameters indicates better performance in both models. Batch adsorption studies gave qmax of 456.67 ​mg/g, 389.51 ​mg/g, and 410.56 ​mg/g for the experimental data, ANN and ANFIS models respectively thus, suggesting their good correlations. Technoeconomic aspects of the study present that AMMP is approximately 8 times cheaper, translating to saving cost of 225.2 USD/kg when compared with 259.5 USD/kg for commercial activated carbon.

Comparison of different artificial neural network (ANN) training algorithms to predict the atmospheric temperature in Tabuk, Saudi Arabia

Use of Artificial neural network (ANN) models to predict weather parameters has become important over the years. ANN models give more accurate results in weather and climate forecasting among many other methods. However, different models require different data and these data have to be handled accordingly, but carefully. In addition, most of these data are from non-linear processes and therefore, the prediction models are usually complex. Nevertheless, neural networks perform well for non-linear data and produce well acceptable results. Therefore, this study was carried out to compare different ANN models to predict the minimum atmospheric temperature and maximum atmospheric temperature in Tabuk, Saudi Arabia. ANN models were trained using eight different training algorithms. BFGS Quasi Newton (BFG), Conjugate gradient with Powell-Beale restarts (CGB), Levenberg-Marquadt (LM), Scaled Conjugate Gradient (SCG), Fletcher-Reeves update Conjugate Gradient algorithm (CGF), One Step Secant (OSS), Polak-Ribiere update Conjugate Gradient (CGP) and Resilient Back-Propagation (RP) training algorithms were fed to the climatic data in Tabuk, Saudi Arabia. The performance of the different training algorithms to train ANN models were evaluated using Mean Squared Error (MSE) and correlation coefficient (R). The evaluation shows that training algorithms BFG, LM and SCG have outperformed others while OSS training algorithm has the lowest performance in comparison to other algorithms used.

Multiobjective optimization of synechocytis culture in flat-plate photobioreactor toward optimal growth and exergy

Many researchers are analyzing microalgae as a fuel source due to their high potential. Since microalgae are grown on a narrow area of land and less water, microalgae can contain high lipids. Carbon dioxide, water, inorganic salts, temperature and degree of acidity (pH), and light intensity in photobioreactors affect microalgae growth. Microalgae Synechocystis cultivated in BG-11 medium on closed PBRs with an addition of 10 mM NaHCO3. Culture medium illuminated at one side with Orange-red LED (636 nm) at light intensities of 50, 200, 300, 500, 800, 950, and 1,460 µmol photon/m2.s with light intensity adjustment every 24 hours. Optical density and exergy destruction also optimize for artificial neural network training and Multiobjective Genetic Algorithms. The optimum value from the TOPSIS approach is the OD 12.957 OD730 and 8660.35 kJ exergy destruction. The optimum condition is derived from the optimum value. The light intensity of 71 µmol photon/m2s and the dry cell weight of 0.119 g/OD730L are ideal conditions for optimal microalgae development.