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The predictions of climate conditions have important impacts on people's lives through agriculture, food security, water resources management, health, natural disasters and environmental degradation, etc. The main focus of this paper is to develop an accurate mathematical model using the artificial neural network (ANN) method, to predict weather conditions such as: precipitation, wind speed, humidity and average temperature, based on a database composed of: the earthsun distance, the annual evolution (time), the sunshine duration and the conversion factor, measured over a period from January 01, 2001 to December 31, 2010 for the city of Médéa (Algeria), the said data are normalized between 0 and 1, the number of neurons in the hidden layer of ANN, single layer MLP type is10 neurons. The architecture of the ANN is of MLP type with a single layer of 10 neurons with a tangential sigmoid function as activation function, and a learning algorithm of Levenberg-Marquardt type, with a mean square error RMSE = 0.1202 and a correlation coefficient R2 =0.8289. In addition, a graphical interface was developed by the Guide function programmed in Matlab in order to facilitate the use of the mathematical model established for the user. [ABSTRACT FROM AUTHOR]

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In this work, the solubilities of some anti-inflammatory (nabumetone, phenylbutazone and salicylamide) and statin drugs (fluvastatin, atorvastatin, lovastatin, simvastatin and rosuvastatin) were correlated using the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) with one-parameter mixing rule and commonly used cubic equations of state Peng-Robinson (PR) and Soave-Redlich-Kwong (SRK) combining with van-der Waals-1 parameter (VDW1) and van-der Waals-2 parameters (VDW2) mixing rules. The experimental data for studied compounds were taken from literature at temperature and pressure in ranges (308-348 K) and (100-360 bar) respectively. The critical properties required for the correlation with PR and SRK were estimated using Gani and Noonalol contribution group methods whereas, PC-SAFT pure-component parameters; segment number (m), segment diameter (σ) and energy parameter (ε/k) have been estimated by tihic’s group contribution method for nabumetone. For phenylbutazone and salicylamide those parameters were determined using a linear correlation. For statin drugs, PC-SAFT parameters were fitted to solubility data, and binary interaction parameters (kij and lij) have been obtained by fitting the experimental data. The result was found to be in good agreement with the experimental data and showed that PC-SAFT approach can be used to model solid-SCF equilibrium with better correlation accuracy than cubic equations of state.

The predictability of the adsorption capacity of the multicomponent adsorption system was modelled using Support Vector Machine (SVM). Two SVM models were built and compared. In the first model, the SVM method was used with an already built-in optimisation algorithm. However, in the second model, the SVM method was used by means of a very recent and efficient optimisation algorithm: the Dragonfly Algorithm (DA). The models’ accuracy was evaluated by three well-established statistical metrics (root mean squared error RMSE, determination coefficient R², and correlation coefficient R). The used data were collected from previous experimental papers published in literature containing all kinds of pollutants, such as heavy metal ions, dyes, and organic compounds, and different natural/ synthetic adsorbents. The dataset contained five important variables with 1023 points; the variables were divided into four inputs (molecular weight, equilibrium concentrations of adsorbate, specific area of adsorbent, and temperature), and one output (adsorption capacity at equilibrium). The data were divided using the holdout function into two subsets (80 % for training set, and 20 % for test set). The programming stage was carried out using MATLAB software. The results showed that the optimised DA-SVM model with RBF-Gaussian kernel function had good ability for global search combined with high prediction accuracy, with R² = 0.997, R = 0.998, and RMSE = 2.539. The obtained model can be used to predict the efficiency of the adsorption system, and provides a tool for process optimisation responding to changes in operating conditions. A new graphical user interface (GUI) was developed with MATLAB GUI to estimate accurately the desired responses by using the best DA-SVM model. [ABSTRACT FROM AUTHOR]

In this work, the thermal conductivity of refrigerants systems from three different hydrofluoroolefins including R1234yf, R1234ze(E), and R1233zd(E) were studied using artificial neural network. A total of 4395 data points of liquid and vapor thermal conductivity at several temperatures (241.92 to 344.46 K) and pressures (0.068 to 21.73 MPa) were used to train and test the model. Five neurons were used in the input layer, fifteen neurons at hidden layer and one was used in the output layer. Bayesian Regulation back propagation algorithm, logarithmic sigmoid transfer function, and linear transfer function were used at the hidden and output layer, respectively. Temperature, pressure, applied heating power; acentric factor, and dipole moment were considered as input variables of the networks. The optimal parameters were obtained through the weights searching method. The average absolute relative deviations and correlation coefficient were 1.48 and 0.9998, respectively. This study shows, therefore, that the artificial neural network model represents an excellent alternative to estimate the thermal conductivity of different refrigerant systems with a good accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Kemija u Industriji is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this work was to model the deep bed and cake filtration process using an alternative approach based on a fractional calculus (FC). The considered experimental data in this study were extracted from published studies. The data used in FC models contained two inputs - initial concentration and time, and volume of filtrate as an output. The FC kinetic constants were tuned by fitting the experimental and predicted data through Dragonfly algorithm implemented in MATLAB programming software. The performance of the developed models was assessed using different metrics by comparing the experimental against the predicted data. The developed fractional model with nth order presented very acceptable metrics in comparison to the pseudo-nth order and displayed strong potential for estimating the volume of filtrate. [ABSTRACT FROM AUTHOR]

Copyright of Kemija u Industriji is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this work was to develop three artificial intelligence-based methods to model the ternary adsorption of heavy metal ions {Pb2+, Hg2+, Cd2+, Cu2+, Zn2+, Ni2+, Cr4+} on different adsorbates {activated carbon, chitosan, Danish peat, Heilongjiang peat, carbon sunflower head, and carbon sunflower stem). Results show that support vector regression (SVR) performed slightly better, more accurate, stable, and more rapid than least-square support vector regression (LS-SVR) and artificial neural networks (ANN). The SVR model is highly recommended for estimating the ternary adsorption kinetics of a multicomponent system. [ABSTRACT FROM AUTHOR]

The objective of this study was to model the removal efficiency of ternary adsorption system using feed-forward back propagation artificial neural network (FFBP-ANN). The ANN model was trained with Levenberg–Marquardt back propagation algorithm and the best model was found with the architecture of {9-11-4-3} neurons for the input layer, first and second hidden layers, and the output layer, respectively, based on two metrics, namely, mean squared error (MSE) = (0.2717–0.5445) and determination coefficient (R2 ) = (0.9997–0.9999). Results confirmed the robustness and the efficiency of the developed ANN model to model the adsorption process. [ABSTRACT FROM AUTHOR]

In this study, the solubility of 145 solid solutes in supercritical CO2 (scCO2) was correlated using computational intelligence techniques based on Quantitative Structure-Property Relationship (QSPR) models. A database of 3637 solubility values has been collected from previously published papers. Dragon software was used to calculate molecular descriptors of 145 solid systems. The genetic algorithm (GA) was implemented to optimise the subset of the significantly contributed descriptors. The overall average absolute relative deviation MAARD of about 1.345 % between experimental and calculated values by support vector regress SVR-QSPR model was obtained to predict the solubility of 145 solid solutes in supercritical CO2, which is better than that obtained using ANN-QSPR model of 2.772 %. The results show that the developed SVR-QSPR model is more accurate and can be used as an alternative powerful modelling tool for QSAR studies of the solubility of solid solutes in supercritical carbon dioxide (scCO2). The accuracy of the proposed model was evaluated using statistical analysis by comparing the results with other models reported in the literature. [ABSTRACT FROM AUTHOR]

Copyright of Kemija u Industriji is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, two novel models have been proposed based on semi-empirical and factional calculus incorporating non-integer time derivatives in the Fick’s first law of anomalous diffusion. The experimental data has been collected from literature of 15 kinetics investigated in a convective dryer under the effect of temperatures ranging from 40 to 80 °C at 10 °C interval, and thickness of the slices of 2 to 6 mm at 2 mm interval. The collected experimental dataset was of apple slices (Golab variety). Results from this study were compared with a set of 64 thin-layer drying models previously published in the literature. The fitting capability of the models was compared using the mean of root mean square error MRMSE (%) of all kinetics and the global determination coefficient R² . All models’ constants and coefficients were optimised by dragonfly algorithm programmed in MATLAB software. Results showed that the fractional model is highly capable of describing the drying curve of the apple slices with a determination coefficient (R²) of 0.99981, and average root mean square error (MRMSE) of 0.43 % in comparison to the best empirical models with R² of 0.99968 and MRMSE of 0.61 %. [ABSTRACT FROM AUTHOR]

Copyright of Kemija u Industriji is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Kemija u Industriji is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Kemija u Industriji is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this novel article, a comparative study has been conducted between analytical and fractional solution of Fick’s first- and second-order of diffusion to model the solar drying process of different products and dryers under different operating parameters. Laplace and Laplace’s inverse transform have been used to obtain the solution in function with two parameters: the order index n and the fractional time index α of the above Fick’s laws, and their parameters have been non-linearly optimised using a grey wolf optimisation (GWO) algorithm. The results showed that the anomalous diffusion phenomenon during the drying process is best described by the fractional‐order model. Values obtained using the model MR12 were in better agreement with the experimental data than the values obtained using the other selected models with very acceptable statistical parameters. [ABSTRACT FROM AUTHOR]

Copyright of Kemija u Industriji is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Kemija u Industriji is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Major advances in modeling and control are required to meet future technical challenges in polymers manufacturing. This work investigates the recent applications of artificial neural network (ANN) in modeling the carbonyl groups during photo-thermal and thermal aging of PE, LDPE, PP, PVC, PS and EPDM. A set of 2450 data points for carbonyl index (CI) contains 15 polymer systems which are 5 pure, 5 binary, 3 tertiary and 2 quaternary systems, and 577 data points for concentration of carbonyl ([CO])including 4 systems, within 1 pure, 1 binary, 1 tertiary and 1 quaternary system, were used to test the neural networks proficiency. For the most promising neural network models, the predicted carbonyl index and concentration of carbonyl values of the total dataset were compared to measured carbonyl index and concentration of carbonyl values; good correlations were found (R= 0.9471 for ANN1 and R= 0.9830 for ANN2). The root mean square errors for the total dataset were 0.0958 and 0.0291 mol/l for CI and [CO] respectively. The comparison between the first and the second model proves the importance of the common properties of polymers and their additives in order to distinguish them. [ABSTRACT FROM AUTHOR]

Copyright of Kemija u Industriji is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Kemija u Industriji is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Kemija u Industriji is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A robust Quantitative Structure-Property Relationship (QSPR) model was presented to predict the surface tension property of psychoanaleptic (psychostimulant and antidepressant) drugs. A dataset of 112 molecules was utilized, and three feature selection methods were applied: genetic algorithm combined with Ordinary Least Squares (GA-OLS), Partial Least Squares (GA-PLS), and Support Vector Machines (GA-SVM), each identifying ten pertinent AlvaDesc descriptors. The models were constructed using the Dragonfly Algorithm combined with the Support Vector Regressor (DA-SVR), with the GA-SVM-based model emerging as the top performer. Rigorous statistical metrics validate its superior predictive accuracy (R 2  = 0.98142, Q 2 LOO  = 0.98142, RMSE = 1.12836, AARD = 0.78746). Furthermore, an external test set of ten compounds was employed for model validation and extrapolation, along with assessing the applicability domain, further underscoring the model's reliability. The selected descriptors (X0Av, VE1sign_B(e), ATSC1e, MATS6v, P_VSA_ppp_A, TDB01u, E1s, R2m+, N-067, SssO) collectively elucidate the key structural factors influencing surface tension in the studied drugs. The model provides excellent predictions and can be used to determine the surface tension of new psychoanaleptic drugs. Its outcomes will guide the design of novel medications with targeted surface tension properties., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Knowledge of the optical properties of blood plays important role in medical diagnostics and therapeutic applications in laser medicine. In this paper, we present a very rapid and accurate artificial intelligent approach using Dragonfly Algorithm/Support Vector Machine models to estimate the optical properties of blood, specifically the absorption coefficient, and the scattering coefficient using key parameters such as wavelength (nm), hematocrit percentage (%), and saturation of oxygen (%), in building very highly accurate Dragonfly Algorithm-Support Vector Regression models (DA-SVR). 1000 training and testing sets were selected in the wavelength range of 250-1200 nm and the hematocrit of 0-100%. The performance of the proposed method is characterized by high accuracy indicated in the correlation coefficients ( R ) of 0.9994 and 0.9957 for absorption and scattering coefficients, respectively. In addition, the root mean squared error values ( RMSE ) of 0.972 and 2.9193, as well as low mean absolute error values ( MAE ) of 0.2173 and 0.2423, this result showed a strong match with the experimental data. The models can be used to accurately predict the absorption and scattering coefficients of blood, and provide a reliable reference for future studies on the optical properties of human blood.

Many-objective optimization, which deals with balancing multiple competing objectives to find compromised solutions, is essential for solving complex problems. This study explores evolutionary algorithms for optimizing the microstructural, rheological, stability, and drug release properties of bigel systems formulated using structured almond oil, mixed organogelators, and carbopol. The oleogel was identified as the dispersed phase, with droplet sizes ranging from 1.43 µm to 7.37 µm, indicating improved characteristics compared to other bigels. Each formulation exhibited non-Newtonian shear-thinning and thixotropic behaviors, which were positively influenced by the proportions of the excipients. After undergoing multiple stress cycles, highly concentrated bigels exhibited phase separation. Unexpectedly, bigels with lower viscosity exhibited reduced rates of drug release. FT-IR and HPLC analyses confirmed the compatibility and stability of drug-excipient interactions, with impurities remaining below 4%. This study emphasizes the complex interactions within mixed lipid-based bigels, requiring many-objective optimization techniques to address conflicting objectives. The objectives of optimization involve simultaneously minimizing microstructural properties while maximizing structural recovery and drug release properties. This led to conflicting objectives, where achieving higher structural recovery did not align with the desired drug release rate. Additionally, more stable formulations did not meet the optimal microstructural objectives. To resolve these conflicts, an RSM-MaOEAs approach was applied, employing various decision-making methods. Among EAs, RSM-RVEA notably achieved exceptional convergence. Furthermore, three MaOEAs-integrated decision-making methods-WSM, WPM, NED-and the RSM-desirability, offered potential solutions. Overall, this research proposes a robust framework for compromising the bigels' performance and stability, with broader applications in drug delivery and related fields., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)