SARS-CoV-2 (COVID-19) is a new Coronavirus, with first reported human infections in late 2019. COVID-19 has been officially declared as a universal pandemic by the World Health Organization (WHO). ...The epidemiological characteristics of COVID-2019 have not been completely understood yet. More than 200,000 persons were killed during this epidemic (till 1 May 2020). Therefore, developing forecasting models to predict the spread of that epidemic is a critical issue. In this study, statistical and artificial intelligence based approaches have been proposed to model and forecast the prevalence of this epidemic in Egypt. These approaches are autoregressive integrated moving average (ARIMA) and nonlinear autoregressive artificial neural networks (NARANN). The official data reported by The Egyptian Ministry of Health and Population of COVID-19 cases in the period between 1 March and 10 May 2020 was used to train the models. The forecasted cases showed a good agreement with officially reported cases. The obtained results of this study may help the Egyptian decision-makers to put short-term future plans to face this epidemic.
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•Performance of passive still, active still, and condenser is studied.•Distilling systems are modeled by different artificial intelligence-based models.•Accumulated productivity of ...active still is improved by 53.21%.•Artificial neural network unified with Harris Hawks optimizer is the best model.•In the best model, R2 is 0.97 and 0.98 for active and passive stills, respectively.
In this paper, a new productivity prediction model of active solar still was developed depending on improving the performance of the traditional artificial neural networks using Harris Hawks Optimizer. This optimizer simulates the behavior of Harris Hawks to catch their prey, and this method is used to determine the optimal parameters of artificial neural networks. The proposed model, called Harris Hawks Optimizer – artificial neural network, is compared with two other models named support vector machine and traditional artificial neural network, in addition to the experimental-based behavior of the solar still. The models were applied to predict the yield of three different distillation systems, namely, passive solar still, active solar still, and active solar still integrated with a condenser. Experimentally, the productivity of the active distiller integrated with the condenser was increased by 53.21% at a fan speed of 1350 rpm. The performance of the models was assessed using different statistical criteria such as root mean square error, coefficient of determination, and others. Among the three models, Harris Hawks Optimizer – artificial neural network had the best accuracy in predicting the solar still yield compared with the real experimental results.
An innovative predictive model was employed to predict the key performance indicators of a full-scale wastewater treatment plant (WWTP) operated with an activated sludge treatment process. The ...data-driven model was obtained using data gathered from Cairo, Egypt. The proposed model consists of Random Vector Functional Link (RVFL) Networks incorporated with Manta Ray Foraging Optimizer (MRFO). RVFL is used as an advanced Artificial Neural Network (ANN) that avoids the common conventional ANN problems such as overfitting. MRFO is employed to determine the best RVFL parameters to maximize the prediction accuracy of the model. The developed MRFO-RVFL is compared with conventional RVFL to figure out the role of MRFO as an optimization tool to enhance model performance. Both models were trained and tested using experimental data measured during a long period of 222 days. This study aims to provide an accurate prediction of the most widely treated effluent indicators of BOD5 and TSS in the wastewater treatment plants. In this study, ten well-known influent wastewater parameters, BOD5, TSS, and VSS, influent flow rate, pH, ambient temperature, F/M ratio, SRT, WAS, and RAS, the output BOD5 and TSS were modeled and predicted using the integrated MRFO-RVFL algorithms and compared with the standalone RVFL model. The performance of the models was evaluated using different assessment measures such as R2, RMSE, and others. The obtained results of R2 and RMSE for the MRFO-RVFL model were 0.924 and 3.528 for BOD5 and 0.917 and 6.153 for TSS, which were much better than the results of conventional RVFL with 0.840 and 6.207 for BOD5 and 0.717 and 10.05 for TSS. Based on the obtained results, the selective model (MRFO-RVFL) exhibited a higher performance and validity to predict the TSS and optimal BOD5.
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•Neural network was used to predict the effluent quality of wastewater plant.•Optimization of random vector functional link model using manta ray foraging (MRFO).•Higher accuracy was observed using the hybrid prediction model of RVFL-MRFO.•Eight statistical metrics have been employed to evaluate the investigated models.
In this study, a new hybrid artificial intelligence approach is proposed to model the ultrasonic welding of a polymeric material blend. The proposed approach is composed of an ensemble random vector ...functional link model (ERVFL) integrated with a gradient-based optimizer (GBO). First, welding experiments were conducted on acrylonitrile butadiene styrene (ABS) and polycarbonate (PC) blends produced by the injection molding method. The experiments were designed according to the L27 orthogonal array considering three process factors (applied pressure, welding time, and vibration amplitude) and two responses (average temperature and joint strength). Then, the obtained experimental data were used to train the developed model. To verify the accuracy of the model, it was compared with standalone ERVFL in addition to two fine-tuned ERVFL models (ERVFL-SCA and ERVFL-MRFO) in which ERVFL is incorporated with sine cosine algorithm (SCA) or Manta ray foraging optimization (MRFO). The four models were evaluated using five statistical tools. ERVFL-GBO has the highest coefficient of determination and the lowest root mean square error, mean relative error mean absolute error, and coefficient of variance compared with other models which indicate its high accuracy over other tested models.
The sun is considered as the most promising abundant renewable energy source that can be exploited to solve many of human beings’ challenges such as energy and water scarcity. Solar energy can be ...utilized in steam and vapor generation processes which has a great importance in many engineering applications such as water desalination, domestic water heating, and power generation. However, dilute solar flux (∼1000 W/m2) cannot supply the absorber with enough power required to overcome water latent heat of vaporization to evaporate water. Optical concentrators such as parabolic trough collector, parabolic dish reflector, and circular Fresnel lens can be used to concentrate the solar radiation to achieve the required power however they suffer from complexity and high cost. Moreover, the efficiency of the conventional solar desalination devices such as solar stills decreases dramatically with increasing bulk water quantity, due to the heat loss to bulk water. Therefore, the need to solar steam generation (SG) devices, that localize heating on a thin layer of water rather than the water bulk, arises. Thin film technology has shown promising progress in SG in which solar energy is utilized to wastewater desalination. The past five years have seen a significant surge in the development of thin film based SG devices. In this review, recently developed thin film-based SG devices are scrutinized with respect to their physical mechanisms, fabrication methods, structure, advantages, and disadvantages. Different types of thin-film materials, including: metal-based nanoparticles, metal oxides, carbon-based materials, polymers, etc.; as well as different substrates materials, including: wood, paper, cotton fabric, carbon fabric, polystyrene foam, and gauze, have been discussed. Moreover, different preparation and synthetization methods of the steam generation devices have been discussed. Suggestions for future research directions are also presented.
•Thermohydrulic performance of STHE was predected using four ML algorithms; RVFL, KNN, SVM and SMO.•The RVFL model has the best prediction performances with excellent accuracy.•The RVFL was ...considered as a good option for modeling the two-phase process in STHE.
In this study, improved prediction methods based on supervised machine-learning algorithms is proposed to predict the effect of the application of air injection and transverse baffles into shell and tube heat exchanger on the thermohydraulic performance. The injection process is accomplished by injecting air into the shell with different flow rates to obtain the optimal thermohydraulic performance. Four different machine-learning algorithms have been employed to predict the thermohydraulic performance of the heat exchanger to avoid mathematical modeling or carrying out costly experiments. These algorithms are random vector functional link, support vector machine, social media optimization, and k-nearest neighbors algorithm. The algorithms were trained and tested using experimental data. The inputs of the algorithms were the cold fluid and injected air volume flow rates; while the outputs were the outlet temperature of hot and cold fluids, in addition to pressure drop across the heat exchanger. The inlet temperatures of inlet hot and cold fluids and volume mass flow rate of hot fluid are considered as constants. The obtained results demonstrate the high ability of the random vector functional link model to find out the nonlinear relationship between the operating conditions and process responses. Moreover, it provides better prediction capabilities of the outlet temperature of hot and cold fluids and pressure drop values compared with the other three investigated models in terms of performance statistical measures. The root mean square error and mean relative error for RVFL results is approximately one-third and one-fourth of that of SMO, SVM, or k-NN, respectively. The root mean square error was, 0.719167, 2.477069, 1.741808, and 1.855635 for RVFL, SMO, SVM, and KNN, respectively; while mean relative error was 0.016167, 0.061746, 0.043366, and 0.041383 for RVFL, SMO, SVM, and k-NN, respectively.
Sun is considered as an important source of energy, and nowadays it is studied by researches from different areas. The current technologies are not able to convert solar energy into electricity with ...high performance. The tendency is to generate new methods that enhance the design of devices for solar energy conversion. Solar cells are devices that convert solar energy into electrical energy with low cost and easy large-scale manufacturing capabilities. However, such devices have a high degree of nonlinearity, and they possess parameters that must be accurately selected. Considering the above traditional computational methods are used to obtain solar cells parameters are cumbersome with many limitations. This paper presents a review of different meta-heuristics techniques, including Genetic Algorithms, Harmony Search, Artificial Bee Colony, Simulated Annealing, Cat Swarm Optimization, Differential Evolution, Particle Swarm Optimization, Advanced Bee Swarm Optimization, Whale Optimization Algorithm, Gravitational Search Algorithm, Flower Pollination Algorithm, Shuffled Complex Evolution, and Wind-Driven Optimization. Such methods are applied to solar cell parameters estimation which may be beneficial to enhance the efficiency of such devices. This study provides different comparisons to define which of them is the best alternative for solar cells design.
•Solar Cell is an important source of renewable energy.•Metaheuristic algorithm are used to improve the performance of solar cell system.•This paper presents a review for using MH to estimate the parameters of solar cells.
Tubular solar still is a simple light-weight desalination unit with a large condensing surface compared with other types of solar stills. Regrettably, it suffers from the low water yield like other ...types of solar stills. In this work, two main research themes are studied. The first is enhancing the water yield and thermal efficiency of tubular solar still by providing the absorber plate with an electrical heater powered by a solar photovoltaic panel. The performance of the modified solar still is evaluated based on its water yield as well as energy and exergy efficiencies. The second is developing a fine-tuned artificial intelligent model to predict the thermal efficiency and water yield of the solar still. The fine-tuned model consists of a traditional artificial neural network model optimized by a meta-heuristic optimizer called humpback whale optimizer. The prediction accuracy of the developed model is compared with that of the standalone artificial neural network model and an optimized model using a traditional particle swarm optimizer. The results showed that the conventional tubular solar still produces an average accumulated water yield of 2.58 L/m2/day, while the modified tubular solar still produces an average accumulated water yield of 3.41 L/m2/day with 31.85% improvement. The daytime energy efficiency of the modified tubular solar still is 38.61%, but for the conventional one is only 30.67%. Moreover, the new developed model has the highest prediction accuracy among other investigated models. The optimized model using humpback whale optimizer has the highest correlation coefficient ranges between 0.983 and 0.999, the optimized model using particle swarm optimizer has a moderate correlation coefficient between 0.969 and 0.987, and standalone model has the lowest ranges between 0.594 and 0.937. These results revealed the vital role of the electrical heater in enhancing the thermal performance of the solar still and the important role of humpback whale optimizer for improving the prediction accuracy of the traditional neural network models.
Since the importance of introducing new engineering materials is increasing, the need for machining such higher strength materials has also considerably increased. In the present research, an ...endeavor was made to introduce a Taguchi–DEAR methodology for the abrasive water-jet machining process, while machining a SiC-reinforced aluminum composite. Material removal rate, taper angle, and surface roughness were considered as the quality measures. The optimal arrangement of input process factors in the AWJM process was found to be 2800 bar (WP), 400 mg/min (AF), 1000 mm/min (FR), and 4 mm (SOD), among the chosen factors, with an error accuracy of 0.8%. The gas pressure had the most significance for formulating the performance measures, owing to its ability to modify the impact energy and crater size of the machined specimen.
The development of different solar energy (SE) systems becomes one of the most important solutions to the problem of the rapid increase in energy demand. This may be achieved by optimizing the ...performance of solar-based devices under some operating conditions. Intelligent system-based techniques are used to optimize the performance of such systems. In present review, an attempt has been made to scrutinize the applications of artificial neural network (ANN) as an intelligent system-based method for optimizing and the prediction of different SE devices’ performance, like solar collectors, solar assisted heat pumps, solar air and water heaters, photovoltaic/thermal (PV/T) systems, solar stills, solar cookers, and solar dryers. The commonly used artificial neural network types and architectures in literature, such as multilayer perceptron neural network, a neural network using wavelet transform, Elman neural network, and radial basis function, are also briefly discussed. Different statistical criteria that used to assess the performance of artificial neural network in modeling SE systems have been introduced. Previous studies have reported that artificial neural network is a useful technique to predict and optimize the performance of different solar energy devices. Important conclusions and suggestions for future research are also presented.
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