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In this investigation, the individual and synergetic influences of the SiC whiskers and WC particles on the various features of TiC were studied. Fully dense specimens were secured when 40 vol% SiC additive was added to the TiC and TiC-WC systems. The nucleation and growth of the new SiC grains were found as the two main mechanisms positively affecting the sinterability of TiC. Although WC particles were completely dissolved into the TiC matrix in the TiC-WC system, they were entrapped in the newly grown SiC grains in the TiC-WC-SiC system, contributing to strengthening of the specimen. The highest values of flexural strength (590 MPa) and Vickers hardness (2525 HV 0.5 kg) were attained for the sample codoped with SiC whiskers and WC particles. [ABSTRACT FROM AUTHOR]

This investigation aimed to study the individual and synergic impacts of SiC and TiN additives on the sinterability and microstructure of TiB 2 -based ceramics. The hot-pressing method at 2000 °C was implemented to fabricate the samples. All three samples reached their near full densities thanks to the beneficial role of the incorporated phases. In terms of the TiB 2 –SiC composite, the in-situ SiO 2 was produced over the sintering process because of a chemical reaction between TiO 2 oxide and SiC reinforcement. On the other hand, the X-ray diffraction (XRD) pattern, along with the microstructural observations, revealed the in-situ formation of hexagonal BN (hBN) in the TiB 2 –TiN system. Moreover, the dissolution of boron into the TiN additive led to the formation of a solid solution, and consequently, a strong diffusion bonding was achieved. Although no hBN compound was identified in the ternary system of TiB 2 –SiC–TiN, the results confirmed the generation of TiC and TiC 0·3 N 0.7 ingredients during the hot pressing. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to investigate the influence of WC as a sintering aid on the sinterability and mechanical characteristics of TiC. For this purpose, two specimens, namely TiC-5 vol% WC and monolithic TiC, were manufactured using the hot-pressing route at 2000 °C for 2 h under 50 MPa. The addition of WC uplifted the sintering behavior of TiC. The XRD and microscopical evaluations revealed the precipitation of graphitized carbon in the TiC matrix. The results also manifested the complete dissolution of WC in the matrix, forming the (Ti,W)C solid solution. The addition of WC improved the Vickers hardness and flexural strength of TiC by ~12% and ~20%, respectively. Grain size, remaining porosity, and composition of the matrix were identified as the essential factors in boosting the mechanical properties of the TiC-WC specimen compared to the monolithic TiC. [ABSTRACT FROM AUTHOR]

The impact of adding 40 vol% SiC whiskers on the sinterability, flexural strength, and hardness of TiC ceramics was studied. The hot-pressing route was employed to fabricate the TiC–SiC composite, as well as the monolithic TiC ceramic. The microstructural studies and X-ray diffractometry revealed the precipitation of carbon within the TiC matrix owing to the formation of non-stoichiometric TiC x during the hot-pressing. The addition of SiC whiskers noticeably improved the sinterability of TiC, attaining a fully dense composite sample. The nucleation and growth of new SiC grains were identified as the dominant factors in enhancing the relative density of the TiC-40 vol% SiC composite. Moreover, the SiC additive had an effective impact on both flexural strength and hardness of TiC, standing at 543 MPa and 2280 HV 0.5 kg , respectively. [ABSTRACT FROM AUTHOR]

The objective of this research was to evaluate the impact of the morphology of SiC additive on the microstructure development, sintering behavior, hardness, and fracture toughness of TiB 2 -based ceramics. For this purpose, three different samples were produced via the hot-pressing method at 1950 °C under 25 MPa for 120 min. Subsequently, the samples were characterized by XRD, SEM, and optical microscopy. The samples containing SiC particles (SiC p) reached their theoretical densities. XRD results manifested the formation of an in-situ TiC during the hot-pressing. Thermodynamical investigation and SEM fractographs revealed the role of a liquid phase in boosting the sintering behavior of as-sintered ceramics. The SEM images also confirmed the in-situ generation of B 4 C in the specimens doped with SiC whiskers (SiC w). Finally, the most significant Vickers hardness (28.9 GPa) and fracture toughness (6.4 MPa m1/2) was secured for the sample toughened by 30 vol% SiC w , whereas the lowest values, namely a Vickers hardness of 18.1 GPa and a fracture toughness of 5.1 MPa m1/2, were obtained for the composites doped with 30 vol% SiC p , and 15 vol% SiC w + 15 vol% SiC p , respectively. [ABSTRACT FROM AUTHOR]

• Proposing an environmental/economic model for optimal energy management. • Considering renewable energy resources in the presence of Electric Vehicles • Presenting Modified Harmony Search Algorithm for optimizing Micro-grid • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management Modern power systems are seeking for alternative energy sources will less emission to conventional fossil-fuel power plant to mitigate the global concerns on environmental issues. On one hand, renewable energies have turned to the first choice of system operators. On the other hand, the system should provide the required infrastructure to appropriately accommodate such energy sources. In this respect, microgrids (MG) would provide the needed conditions for integrating renewable energy sources (RESs). Thus, the optimal energy management of such systems in the presence of highly uncertain renewable power generation is of great importance. Accordingly, this paper provides a stochastic programming framework for the optimal scheduling of an MG equipped with RESs and plug-in electric vehicles (PEVs). The power sources considered include a wind energy system in the form of wind turbine (WT), a solar photovoltaic (PV) system, a fuel cell (FC), a microturbine (MT), besides a battery storage system (BSS). The mentioned problem is formulated as a single-objective optimization problem aimed at minimizing the total operating cost over the scheduling period. The MG is considered in the grid-connected mode where it can transact power with the upstream system. The uncertainty of the problem is due to the intermittent power output of the wind energy system and the PV unit, as well as uncertain behavior of the EV owners in charging/discharging their vehicles. The proposed stochastic optimization problem is the solved using an effective and efficient optimization algorithm named "modified harmony search (MHS) algorithm". Finally, the simulation results are discussed and the superior performance of the suggested algorithm is verified through making a comprehensive comparison with some well-known methods. [ABSTRACT FROM AUTHOR]

This investigation intended to assess the influence of SiC morphology on the sinterability and physical-mechanical features of TiB 2 -SiC composites. For this aim, different volume percentages of SiC particles and SiC whiskers were introduced to TiB 2 samples hot-pressed at 1950 °C for 2 h under an external pressure of 25 MPa. The characterization of as-sintered specimens was carried out using X-ray diffraction, optical microscopy, and scanning electron microscopy. The relative density studies revealed that SiC w had a more significant impact on the sinterability of TiB 2 -based composites. The XRD investigation confirmed the production of an in-situ TiC phase during the hot-pressing; however, some peaks related to the graphitized carbon also appeared in the patterns of SiC w -doped ceramics. The addition of 25 vol% SiC p halved the average grain size of TiB 2 while introducing the same content of SiC w decreased this value by just around 20%. Finally, the highest Vickers hardness and fracture toughness were obtained for the sample reinforced with 25 vol% SiC w , standing at 29.3 GPa and 6.1 MPa m1/2, respectively. [ABSTRACT FROM AUTHOR]

In this work, NiAl-WC composite was synthesized by a mechanical activated thermal explosion process from the powder mixture of nickel, tungsten, aluminum, and graphite. The phase, morphology, and microstructure development during milling and after the thermal explosion process were evaluated by XRD, FE-SEM, and TEM-EDS analysis techniques, respectively. The results indicated that after the thermal explosion of an as-mixed powder mixture, unreacted starting powder remained, and some new phases like Al 0.42 Ni 0.52 and W 2 C formed, which confirmed that the synthesis reactions were not completed. But, the final product of 5 h milled powder after the thermal explosion consisted of desired phases, NiAl and WC. XRD results of 5 h milled powder showed that the mechanical activation had two effects on NiAl-WC composite synthesis, reduction of crystallite and particle size, and better mixing of reactant powders and formation of W 2 C phase as an intermediate phase. These effects increased kinetics of reactions, and finally, NiAl-WC composite formed. [ABSTRACT FROM AUTHOR]

• Encasement of stone columns can improve bearing capacity by 3–6 times. • Lateral displacement (bulging) of stone columns decreases with encasement of stone column. • Elasticity modulus of encasement geogrid material can decrease vertical settlement of superstructure. • Short stone columns with low height/diameter ratio are more prone to bulging in shallow depths. • Encasement of short stone columns is more necessary. Improvement of weak base soils under low-rise buildings, storage tanks, bridge piers, and also the treatment of soft foundations of factories and embankments can be achieved by using stone columns. This technique represents an economical method for the improvement of soft and compressible soils. Moreover, because of their environmental compatibility, low cost, and ease of implementation, they have been used for the improvement of weak soils around the world. This paper has investigated the effects of the dimensions, length and main related parameters on the strain–stress behavior of encased and unreinforced (uncased) stone columns. Settlement assessment, the effects of bulging on efficiency, and also geogrid-capped stone columns have been studied. The experimental results have been compared using parametric finite element modeling software. These results showed that short stone columns, even under small stresses, punched into soft soil. In that case, stress concentration occurred along the columns and they punched into the soft soil instead of resisting against wedge rupture formation below the overlying structure. Stone columns at least doubled the bearing capacity of the soft soils and decreased the differential and total settlements if designed according to appropriate conditions, and even if short in length. [ABSTRACT FROM AUTHOR]

Current study aimed to combine the multi-layer Perceptron (MLP) neural network technique with five metaheuristic computational algorithms, namely invasive weed optimization (IWO-MLP), dragonfly algorithm (DA-MLP), evolution strategy (ES-MLP), genetic algorithm (GA-MLP), and imperialist competitive algorithm (ICA-MLP) for estimating the monthly natural gas consumption (NGC). For the case of this study, the NGC data was collected from the United States (U.S.) was chosen. To achieve this, a dataset composed of eight independent factors, namely, dry gas production, NGPL production, gross withdrawals, supplemental gaseous fuels, net storage withdrawals, marketed production, net imports, and balancing items, along with a dependent variable of natural gas consumption is provided. Out of 39 samples, a 3-fold ratio is taken to select the datasets of training and testing randomly. During the calculation phase, and through a trial and error procedure, the optimal parameters of the MLP, IWO-MLP, DA-MLP, ES-MLP, GA-MLP, ICA-MLP networks are taken into consideration. To assess the reliability of the proposed technique with the real collected data, three well-known statistical criteria, including mean square error (MSE) and root mean square error (RMSE) as well as coefficient of determination (R ²), are introduced. These indices help to measure the level of accuracy of the used models. It is found that the IWO can be a better predictive network comparing to other hybridized techniques reducing the error performance of the MLP method. The predicted results reveal that hybridizing optimization algorithms could improve the prediction accuracy and helps the MLP to perform more efficiently. The recorded in testing MSE for the IWO-MLP, DA-MLP, ES-MLP, GA-MLP, ICA-MLP prediction techniques (i.e., used for U.S. natural gas consumption estimation) were found 0.000013, 0.002358, 0.024744, 0.013244, and 0.048572, respectively. In the sense of U.S. natural gas consumption prediction, the training R 2 value to 0.9999, 0.9996, 0.9971, 0.9996, and 1.0000 and testing R 2 value of 0.9999, 0.9995, 0.9956, 0.9984, and 0.8115 show that the outputs of the ensemble models are more correlated with the real data. [ABSTRACT FROM AUTHOR]

This study pursues optima modification of heating, ventilating, and air conditioning (HVAC) systems embedded in residential buildings through predicting heating load (HL) and cooling load (CL). This purpose is carried out by employing four wise metaheuristic algorithms, namely wind-driven optimization (WDO), whale optimization algorithm (WOA), spotted hyena optimization (SHO), and salp swarm algorithm (SSA) synthesized with a multi-layer perceptron (MLP) neural work in order to overcome the computational shortcomings of this model. The used dataset consists of overall height, glazing area, orientation, relative compactness, wall area, glazing area distribution, roof area, and surface area as independent factors, and the HL and CL as target factors. The results indicated a high capability of all four metaheuristic ensembles for understanding the non-linear relationship between the mentioned factors. Meanwhile, a comparison between the used models revealed that SSA-MLP (Error HL = 1.9178 and Error CL = 2.1830) is the most accurate model, followed by WDO-MLP (Error HL = 1.9863 and Error CL = 2.2424), WOA-MLP (Error HL = 2.1921 and Error CL = 2.5390), and SHO-MLP (Error HL = 3.1092 and Error CL = 4.5930). Regarding the satisfying accuracy of the SSA-based ensemble, it can be a reliable tool for estimating the HL and CL for future smart city planning. [ABSTRACT FROM AUTHOR]

• Prediction of purity has been studied in biodiesel production. • Using machine learning to predict the methyl ester purity of an ultrasound-assisted reactor. • The models performed based on the eight influential inputs. The main effort of this study is to examine the feasibility of four novel machine learning models namely Alternating Model Tree, Random Tree, Least Median Square, and Multi-Layer Perceptron Regressor to estimate the biodiesel purity. Then, the mentioned methods are utilized to identify a relationship between the input and output parameters of the biodiesel system. The parameter response was taken as the essential output of fatty acid methyl ester, while the input parameters opted the oil type, catalyst type, catalyst concentration, reaction temperature, methanol-to-oil ratio, reaction time, frequency as well as amplitude. The predicted results obtained by the tools mentioned supra were evaluated according to several known statistical indices. The obtained results proved that the AMT is the best predictive network. [ABSTRACT FROM AUTHOR]

Sustainable consumption of energy is a crucial task in the building sector. In this paper, a novel hybrid technique, namely elephant herding optimization (EHO) is used to optimize the heating ventilation and air conditioning (HVAC) system through analyzing the relationship between the cooling load (CL) and key factors. In other words, the EHO algorithm is combined with a multi-layer perceptron neural network (MLP) to predict the CL. Two metaheuristic algorithms of ant colony optimization (ACO) and Harris hawks optimization (HHO) are also used as benchmark models. Based on the carried out sensitivity analysis, the EHO with population size = 200 suggests the best-fitted computational parameters of the MLP. The findings revealed that metaheuristic algorithms can develop efficient MLPs for the mentioned objective. However, the proposed EHO-MLP (Error = 2.1284 and Correlation = 0.8934) presents the most accurate perdiction of the CL, followed by the HHO-MLP (Error = 2.3265 and Correlation = 0.8829) and ACO-MLP (Error = 2.6011 and Correlation = 0.8721). Based on the obtained results, the EHO-MLP can be a reliable alternative to traditional models for estimating the CL. [ABSTRACT FROM AUTHOR]

This paper introduces a novel and green way for preparation of Nd 2 Sn 2 O 7 nanostructures applying date palm extract. Various quantities of new fuel, date palm extract, have been applied to achieve the most favorable condition for pure and uniform nanostructures of Nd 2 Sn 2 O 7 through a facile way. Our results revealed that usage of new fuel, date palm extract, and its quantity had substantial role in purity, homogeneity and shape of Nd 2 Sn 2 O 7. To characterize the resulting nanostructures in terms of shape, porosity, dimension, composition and level of purity, different techniques have been served. Moreover, the performance of the fabricated Nd 2 Sn 2 O 7 with date palm extract for electrochemical hydrogen storage has been explored through chronopotentiometry way in alkaline medium, for the first time. The discharge capacity of the fabricated Nd 2 Sn 2 O 7 with date palm extract has been recorded after twenty cycles around 4013 mA h/g (≈14.2 wt%) and the efficiency of the mesoporous Nd 2 Sn 2 O 7 for hydrogen storage has been very favorable at room temperature. [ABSTRACT FROM AUTHOR]

One of the most studied topics in analytical chemistry and physics is to develop bio-sensors. Aptamers are small single-stranded RNA or DNA oligonucleotides (5–25 kDa), which have advantages in comparison to their antibodies such as physicochemical stability and high binding specificity. They are able to integrate with proteins or small molecules, including intact viral particles, plant lectins, gene-regulation factor, growth factors, antibodies and enzymes. The aptamers have reportedly shown some unique characteristics, including long shelf-life, simple modification to provide covalent bonds to material surfaces, minor batch variation, cost-effectiveness and slight denaturation susceptibility. These features led important efforts toward the development of aptamer-based sensors, known as apta-sensors classified into optical, electrical and mass-sensitive based on the signal transduction mode. This review provided a number of current advancements in selecting, development criteria, and aptamers application with the focus on the effect of apta-sensors, specifically for disease-associated analyses. The review concentrated on the current reports of apta-sensors that are used for evaluating different food and environmental pollutants. [ABSTRACT FROM AUTHOR]

• An efficient method for image thresholding is proposed. • The Harris hawk optimizer is used for image segmentation. • The Harris hawk optimizer is tested over a multidimensional real problem. • The quality of the segmentation results is higher than other algorithms. • The performance of the algorithm is tested on digital mammograms. Segmentation is a crucial phase in image processing because it simplifies the representation of an image and facilitates its analysis. The multilevel thresholding method is more efficient for segmenting digital mammograms compared to the classic bi-level thresholding since it uses a higher number of intensities to represent different regions in the image. In the literature, there are different techniques for multilevel segmentation; however, most of these approaches do not obtain good segmented images. In addition, they are computationally expensive. Recently, statistical criteria such as Otsu, Kapur, and cross-entropy have been utilized in combination with evolutionary and swarm-based strategies to investigate the optimal threshold values for multilevel segmentation. In this paper, an efficient methodology for multilevel segmentation is proposed using the Harris Hawks Optimization (HHO) algorithm and the minimum cross-entropy as a fitness function. To substantiate the results and effectiveness of the HHO-based method, it has been tested over a benchmark set of reference images, with the Berkeley segmentation database, and with medical images of digital mammography. The proposed HHO-based solver is verified based on other comparable optimizers and two machine learning algorithms K -means and the Fuzzy IterAg. The comparisons were performed based on three groups. This first one is to provide evidence of the optimization capabilities of the HHO using the Wilcoxon test, and the second is to verify segmented image quality using the PSNR, SSIM, and FSIM metrics. Then, the third way is to verify the segmented image comparing it with the ground-truth through the metrics PRI, GCE, and VoI. The experimental results, which are validated by statistical analysis, show that the introduced method produces efficient and reliable results in terms of quality, consistency, and accuracy in comparison with the other methods. This HHO-based method presents an improvement over other segmentation approaches that are currently used in the literature. [ABSTRACT FROM AUTHOR]

One of the famous blue pigments is cobalt aluminate which has a normal spinel that in this work has been synthesized with thermal decomposition technique via the help of olive leaf extract as a precipitation agent. Furthermore, CoAl 2 O 4 /SiO 2 , CoAl 2 O 4 /TiO 2 and CoAl 2 O 4 /graphene nanocomposites were prepared besides the pure one. To investigate the structure and composition of products, X-ray diffraction (XRD) pattern and Fourier transform infrared (FT-IR) analysis were served. Also, the morphologies of products were studied by scanning electron microscopy (SEM) and transmission electron microscopy (TEM) and the effect of different nucleation and growth conditions of the products were investigated. The hydrogen storage ability of the pure CoAl 2 O 4 nanostructures was investigated and to increase the hydrogen storage capacity, different CoAl 2 O 4 nanocomposites were prepared and studied. The maximum discharge capacity and Coulombic efficiencies are 2800 mAh/g and 62% that related toCoAl 2 O 4 /TiO 2 composition and CoAl 2 O 4 , respectively. • The CoAl 2 O 4 spinel was synthesized via olive leaf extract. • Hydrogen storage capacities of CoAl 2 O 4 with its composites were compared. • The Coulombic efficiency of samples was calculated, for the first time. • The discharge capacity increased with the increase of cycle number. • Hydrogen adsorption sites increased by repeating cycles of charge and discharge. [ABSTRACT FROM AUTHOR]

The paper develops a control scheme for a hybrid energy system, including a photovoltaic (PV) panel and a battery energy storage (BES) system. The capital cost of PV systems is considerably high today and it has been continuously trying to mitigate the manufacturing cost and also, several research works have been devoted to developing effective methods to maximize the power output of these generation systems. In this respect, the most efficient technique is named maximum power point tracking (MPPT) for solar PV systems. To this end, this paper utilizes the fuzzy controller and BAT algorithm (BA) to find the optimal values of the parameters of the controller for the MPPT. The presented framework is known as BAT-Fuzzy. Using the mentioned method, the membership functions of the input and output, which are triangular, are autonomously set. The results obtained from simulating the proposed model on a test system shows the superiority of the BAT-Fuzzy technique in terms of the preciseness and convergence rate, while taking into consideration altering values of solar irradiance and temperature. It also performs better, considering the efficiency of the system in practical cases. Besides, the impact of temperature variations and also, different states of the partial shading conditions (PSC) have discussed. The PV system alone does not produce power over some hours of the day and it should be operated jointly with a storage system to generate the required power over all hours of the day. The presented method is simulated using a test system and the results are compared to some well-known methods, verifying that the developed framework is associated with a fast response during transients with enhanced stability. [ABSTRACT FROM AUTHOR]

• EHO, SFLA, SSA, and WDO are Hybridized with MLP. • SSA-MLP presents the most efficient prediction. • Computational weights and biases of the MLP and proposed hybrid algorithms are optimized. Due to the vital significance of precise determination of soil shear strength (SSS) in many civil engineering projects, this study is dedicated to proposing novel intelligent models for estimating this parameter. To this end, elephant herding optimization (EHO), shuffled frog leaping algorithm (SFLA), salp swarm algorithm (SSA), and wind-driven optimization (WDO) are synthesized with artificial neural network (ANN) to create neural ensembles. The results indicated the efficiency of metaheuristic science for dealing with the non-linear analysis of the SSS and influential soil parameters. Also, a comparison between the models revealed that the SSA-MLP (Error = 0.0386 and Correlation = 0.8219) presents the most efficient prediction, followed by WDO-MLP (Error = 0.0403 and Correlation = 0.8025), SFLA-MLP (Error = 0.0408 and Correlation = 0.7559), and EHO-MLP (Error = 0.0436 and Correlation = 0.7195). Therefore, the proposed SSA-MLP can function as a reliable substitute for traditional approaches in prediction of the SSS. [ABSTRACT FROM AUTHOR]

• Indirect measurement of driven piles capacity is presented in this study. • Developing an FA-ANFIS model for prediction of driven piles capacity. • Results signify effectiveness of FA-ANFIS to predict driven piles capacity. Parametric studies of driven piles response during dynamic and static load experiments, especially in the field, are generally too costly to measure. This study constructs and verifies a computational intelligence technique for predicting the bearing capacity of driven piles. The proposed technique is a hybridization of adaptive neuro-fuzzy inference system (ANFIS) and firefly algorithm (FA). It is worth mentioning that this is the first work that explores the ability of FA-ANFIS in the field of piles capacity prediction. In modeling, different FA-ANFIS and ANFIS models were constructed based on different combination of input parameters, and finally, the performances of all models were compared using statistical functions. The results demonstrated that the proposed FA-ANFIS model with coefficient of determination (R2) of 0.997 was more effective than the ANFIS in predicting the pile capacity, and thereby the effectiveness of FA to optimize ANFIS was proved. [ABSTRACT FROM AUTHOR]

Tailored welded blanks (TWBs) technology can give the important possibilities to obtain components in the automobile industry. Friction stir welding (FSW) is a joining approach, which can adhere parts well with desired properties in weld zone. In this article, the main aim is investigation the effect of weld zone orientation on forming limit diagram (FLD) of the TWBs using experimental and finite element model. Three TWBs are produced FSW with three different orientations of weld zones including 0 0 , 45 0 and 90 0 against major stress direction. The microstructure of the weld zones is divided and then observed via metallography procedures. Tensile tests are carried out to determine the mechanical properties of TWB sections and duplicate the tests numerically to validate the formability results. The Nakazima stretch test is used to derive the limit strains for three different weld orientations (between rolling direction and welding path) and what can be seen is the changeable effect of these ordinations on varying strain paths. The best formability is for TWB with orientation weld 90 0 in tension mode while, results reveals that in Plane strain condition best formability is originated from TWB with weld orientation 0 0 . For biaxial tension condition ε 2 = ε 1 , changing the weld orientation has no effect on formability. The comparison between FEM and experimental tests reveals good agreement and high recommended FEM for this type of study. • Influence of the weld orientation on the Tailor Weld Blanks (TWBs) joined by friction stir welding (FSW). • Three different orientation of weld zones including 0 ∘ , 45 ∘ , a n d 90 ∘ against major stress direction. • Transverse weld orientation has the best formability in uniaxial tension side. • Longitudinal weld orientation is the most formable in plane strain area. • In biaxial tension condition, changing the weld orientation has negligible effect on formability. [ABSTRACT FROM AUTHOR]

Adding small amounts of drag-reducing agents (DRA) to the crude oil can effectively reduce energy losses and its associated pressure drop in transportation pipelines. They do their roles by reducing the turbulent friction factor of the flowing liquids in the pipeline. In this study, the artificial neural networks (ANN) are utilized for estimation of drag reduction (DR) in crude oil pipelines as a function of Reynolds number, concentration, and type of drag-reducing agents, temperature, and type of pipe. Two different training algorithms, i.e. Levenberg-Marquardt (LM) and imperialist competition algorithm (ICA) are used for adjustment of parameters of multi-layer perceptron (MLP) networks. Predictive accuracies of trained MLP by Levenberg-Marquardt (LM-MLP) and imperialist competition algorithm (ICA-MLP) for estimating drag reduction in crude oil pipelines were compared. The comparisons were done by three statistical criteria, i.e. root mean square error (RMSE), the coefficient of determination (R2), and mean absolute error (MAE). The obtained results show that replacing LM by ICA evolutionary algorithm for adjustment of parameters of MLP leads to increasing R2 from 0.9585 to 0.9791 in the training phase, and from 0.9310 to 0.9698 in the testing phase. Furthermore, ICA has reduced the RMSE and MAE of testing group from 3.0396 to 2.2616 to the 2.0080 and 1.5579, respectively. Finally, a simple ANN-based formulation is provided for using our developed ICA-MLP model to estimate DR in the crude oil pipelines. • Prediction of pressure drop has been investigated in crude oil pipelines. • Artificial neural network employed to predict the drag reduction. • The models performed based on the five influential variables. [ABSTRACT FROM AUTHOR]