Taoreed O. Owolabi

Estimation of surface energies of hexagonal close packed metals using computational intelligence technique

We developed SVR-based model for estimating surface energy of materials.We used the developed SVR-based model to estimate surface energy of HCP metals.Our results show excellent agreement with the experimental values.Our model outperforms existing theoretical models. Surface phenomena such as corrosion, crystal growth, catalysis, adsorption and oxidation cannot be adequately comprehended without the full knowledge of surface energy of the concerned material. Despite these significances of surface energy, they are difficult to obtain experimentally and the few available ones are subjected to certain degree of inaccuracies due to extrapolation of surface tension to 0K. In order to cater for these difficulties, we have developed a model using computational intelligence technique on the platform of support vector regression (SVR) to establish a database of surface energies of hexagonal close packed metals (HCP). The SVR based-model was developed through training and testing SVR using fourteen experimental data of periodic metals. The developed model shows accuracy of 99.08% and 100% during training and testing phase, respectively, using test-set cross validation technique. The developed model was further used to obtain surface energies of HCP metals. The surface energies obtained from SVR-based model are closer to the experimental values than the results of the well-known existing theoretical models. The outstanding performance of this developed model in estimating surface energies of HCP metals with high degree of accuracy, in the presence of few experimental data, is a great achievement in the field of surface science because of its potential to circumvent experimental difficulties in determining surface energies of materials.

Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete

Concrete compressive strength prediction is very important in structure and building design, particularly in specifying the quality and measuring performance of concrete as well as determination of its mix proportion. The conventional method of determining the strength of concrete is complicated and time consuming hence artificial neural network (ANN) is widely proposed in lieu of this method. However, ANN is an unstable predictor due to the presence of local minima in its optimization objective. Hence, in this paper we have studied the performance of support vector machine (SVM), a stable and robust learning algorithm, in concrete strength prediction and compare the result to that of ANN. It is found that SVM displayed a slightly better performance compared to ANN and is highly stable.

Modeling of average surface energy estimator using computational intelligence technique

Purpose – The surface energy per unit area of material is known to be proportional to the thermal energy at the melting point of the material. The purpose of this paper is to employ the values of the melting points of metals to develop a model that estimates the average surface energies of metals. Average surface energy estimator (ASEE) was developed with the aid of computational intelligence technique on the platform of support vector regression (SVR) using the values of the melting point of the materials as the descriptor. Design/methodology/approach – The development of ASEE which involves 12 data set was conducted by training and testing SVR model using test-set-cross-validation technique. The developed model (ASEE) was used to estimate average surface energies of 3d, 4d, 5d and other selected metals in the periodic table. The average surface energies obtained from ASEE are in good agreement with the experimental values and with the values from other theoretical models. Findings – The accuracy of this...

Estimation of surface tension of methyl esters biodiesels using computational intelligence technique

We developed MESTE for estimating surface tensions of methyl esters biodiesels.Surface tensions of eight different classes of methyl esters were estimated.Results of MESTE were compared with that of Parachor model and Goldhammer model.Performance of MESTE outperforms that of Parachor model and Goldhammer model. Due to environmental benefits, methyl esters biodiesel got a considerable attention as a viable substitute to petroleum-based diesel. Surface tension plays significant role in atomization of this biodiesel since it controls the combustion process inside the engine through fuel-air mixing. Experimental determination of the surface tension of biodiesel is expensive and time consuming which limits its application as substitute for petroleum-based diesel. This is because proper choice of any methyl esters for diesel engine applications depend on the value of surface tension as high value of surface tension brings about difficulty in droplet formation. This work employs computational intelligence technique on the platform of sensitivity based linear learning method (SBLLM) to develop methyl esters surface tension estimator (MESTE) which estimates surface tension of methyl esters biodiesel with high degree of accuracy. Surface tensions of eight different classes of methyl esters were estimated at different temperatures by training and testing of neural network using SBLLM. The estimated surface tensions were compared with experimental results as well as surface tension obtained from Parachor model and Goldhammer model. The outstanding performance of the developed MESTE suggests its potential in estimating surface tension of methyl esters biodiesel for enhancing the atomization in biodiesels engine applications.

Estimation of physical, mechanical and hydrological properties of permeable concrete using computational intelligence approach

Four different models were developed for estimating properties of PC using SVR.The developed models were optimized using test-set-cross validation technique.The developed models are characterized with high CC, low RMSE and low MAE.The values of the estimated properties agreed well with the experimental values. Permeable concrete (PC) has gained a wide range of applications as a result of its unique properties which result into highly connected macro-porosity and large pore sizes. However, experimental determination of these properties is intensive and time consuming which necessitates the need for modeling technique that has a capability to estimate the properties of PC with high degree of accuracy. This present work estimates the physical, mechanical and hydrological properties of PC using computational intelligent technique on the platform of support vector regression (SVR) due to excellent generalization and predictive ability of SVR in the presences of few descriptive features. Four different models were built using twenty-four data-points characterized with four descriptive features. The estimated properties of PC agree well with experimental values. Excellent generalization and predictive ability recorded in the developed models indicate their high potentials for enhancing the performance of PC through quick and accurate estimation of its properties which are experimentally demanding and time consuming.

Computational intelligence method of estimating solid-liquid interfacial energy of materials at their melting temperatures

The reversible work required in forming an interface between a crystal and its coexisting liquid plays significant roles in many phase transformation and controls many processes such as nucleation, crystal growth, surface roughening and surface melting among others. Despite these significances, its experimental determination is difficult and only few experimental results are available in the literatures. This present work aims at circumventing these experimental challenges by developing a computational intelligence (CI) based model that relates solid-liquid interfacial energies of materials with their melting temperatures using support vector regression (SVR) with test-set cross validation optimization technique. The results of the developed CI-based model show persistent closeness to the few available experimental data than other compared existing theoretical models such as Miedema and den Broeder model, Granasy and Tegze model, Jiang combined model and Ewing model. The outstanding performance of the developed CI-based model as well as its implementation which only needs the value of melting temperature of the concerned material, is of immense importance in circumventing the experimental challenges in the practical attainment of equilibrium between a crystal and its melt for solid-liquid interfacial energy determination.

Computational Intelligence Approach for Estimating Superconducting Transition Temperature of Disordered MgB2 Superconductors Using Room Temperature Resistivity

Doping and fabrication conditions bring about disorder in MgB2 superconductor and further influence its room temperature resistivity as well as its superconducting transition temperature (). Existence of a model that directly estimates of any doped MgB2 superconductor from the room temperature resistivity would have immense significance since room temperature resistivity is easily measured using conventional resistivity measuring instrument and the experimental measurement of wastes valuable resources and is confined to low temperature regime. This work develops a model, superconducting transition temperature estimator (STTE), that directly estimates of disordered MgB2 superconductors using room temperature resistivity as input to the model. STTE was developed through training and testing support vector regression (SVR) with ten experimental values of room temperature resistivity and their corresponding using the best performance parameters obtained through test-set cross validation optimization technique. The developed STTE was used to estimate of different disordered MgB2 superconductors and the obtained results show excellent agreement with the reported experimental data. STTE can therefore be incorporated into resistivity measuring instruments for quick and direct estimation of of disordered MgB2 superconductors with high degree of accuracy.

Development of hybrid extreme learning machine based chemo-metrics for precise quantitative analysis of LIBS spectra using internal reference pre-processing method

Laser induced breakdown spectroscopy (LIBS) is a versatile spectroscopic technique that requires little or no sample preparation and capable of simultaneous elemental sample analysis. Quantitative analysis of its spectra has been a major challenge due to self-absorption of the emitted radiation during plasma cooling and inadequate description of non-linear complex interactions taking place in the laser induced plasma. This work presents a novel chemo-metric tool, extreme learning machine (ELM) and its hybrid HHELM (homogenously hybridized ELM), for the first time in modeling the complex interactions of laser induced plasma and quantification of LIBS spectra. Internal reference preprocessing (IRP) method is also proposed as a novel method of enhancing the performance of ELM based chemo-metrics. Since the proposed chemo-metrics (ELM and HHELM) determine their input weights as well as their hidden biases in a random manner, ELM and HHELM are respectively hybridized with gravitational search algorithm (GSA) for optimization of the number of hidden neurons. Effect of IRP, obtained by normalizing the emission spectra intensities with the emission intensity that has highest upper level excitation energy and lowest transition probability, on the performance of the proposed GSA-ELM and GSA-HHELM chemo-metrics is investigated. The proposed models are implemented using spectra of seven bronze standard samples. Chemo-metrics with IRP (GSA-ELM-IRP and GSA-HHELM-IRP) show better generalization performance than those without IRP (GSA-ELM-WIRP and GSA-HHELM-WIRP) while GSA-HHELM based chemo-metrics perform better than their counterparts. The outstanding performance demonstrated by the proposed chemo-metrics and their self-absorption correction ability would definitely widen the applicability of LIBS and improve its precision for the quantitative analysis.

Estimation of average surface energies of transition metal nitrides using computational intelligence technique

Several properties of transition metal nitrides (TMN) that make them useful in many applications are closely related to the state of their surfaces. Meanwhile, high melting points which characterize these materials make the determination of their surface energies experimentally difficult. This work presents a computational intelligence technique using support vector regression (SVR) to establish, for the first time, a complete database of average surface energies of all members of TMN series. SVR-based model was developed by training and testing SVR with best parameters obtained through test-set–cross-validation technique using thirty-five experimental data of periodic metals. The developed SVR-based model was used to estimate average surface energies of 3d, 4d and 5d-TMN, and the obtained results agree well with the existing theoretical values. Simple and effective computational approach of the developed model together with its accurate estimation of average surface energies of all the members of TMN series contributes to the uniqueness of this developed model over the existing theoretical methods.

Novel techniques for enhancing the performance of support vector regression chemo-metric in quantitative analysis of LIBS spectra

Laser induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy through which elemental compositions of materials can be determined with little or no sample preparation. Small sample requirement as well as it capacity for rapid and real time analysis contributes significantly to the wider applicability of the technique. However, quantitative analysis of LIBS spectra remains a challenge and requires non-linear modeling technique that fully captures the complex interactions in the laser induced plasma and ultimately reduces the effect of self-absorption. Support vector regression (SVR) recently attracts significant attention in chemo-metrics due to its sound mathematical background and unique ability to model non-linear systems with reasonable degree of precision. This work proposes two novel techniques by which the performance of SVR can be improved for the quantitative analysis of LIBS spectra. The first technique, referred to as homogeneously hybridized support vector regression (HSVR), combines two SVR algorithms in which the output of the first algorithm serves as the input to the second algorithm while the second technique, referred to as internal reference preprocessing method (IRP), uses the spectra feature that is normalized with the emission line intensity which is not significantly affected by self-absorption. The hyper-parameters of the developed models are optimized using gravitational search algorithm (GSA). On the basis of root mean square error, GSA-HSVR-WIRP (without IRP) performs better than GSA-SVR-WIRP with over 75% performance improvement while GSA-HSVR-IRP performs better than GSA-SVR-IRP with over 95% performance improvement. The outcome of this work would be very useful for precise LIBS quantitative analysis and would eventually promote wide applicability of the technique.