A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO2

Abstract Nilotinib hydrochloride monohydrate (NHM) is an anti-cancer drug whose solubility was statically determined in supercritical carbon dioxide (SC-CO2) for the first time at various temperatures (308–338 K) and pressures (120–270 bar). The mole fraction of the drug dissolved in SC-CO2 ranged from 0.1 × 10–5 to 0.59 × 10–5, corresponding to the solubility range of 0.016–0.094 g/L. Four sets of models were employed to evaluate the correlation of experimental data; (1) ten empirical and semi-empirical models with three to six adjustable parameters, such as Chrastil, Bartle, Sparks, Sodeifian, Mendez-Santiago and Teja (MST), Bian, Jouyban, Garlapati-Madras, Gordillo, and Jafari-Nejad; (2) Peng-Robinson equation of state (Van der Waals mixing rule, had an AARD% of 10.73); (3) expanded liquid theory (modified Wilson model, on average, the AARD of this model was 11.28%); and (4) machine learning (ML) algorithms (random forest, decision trees, multilayer perceptron, and deep neural network with respective R2 values of 0.9933, 0.9799, 0.9724 and 0.9701). All the models showed an acceptable agreement with the experimental data, among them, the Bian model exhibited excellent performance with an AARD% of 8.11. Finally, the vaporization (73.49 kJ/mol) and solvation (− 21.14 kJ/mol) enthalpies were also calculated for the first time

Thermodynamic modeling and solubility assessment of oxycodone hydrochloride in supercritical CO2: Semi-empirical, EoSs models and machine learning algorithms

In this study, the solubility of oxycodone hydrochloride (OXH) in supercritical carbon dioxide (SC–CO2) was investigated at various conditions, temperature (308–338 K) and pressure (120–270 bar), for the first time. The solubility ranged from 0.007 to 0.109 g/L, corresponding to mole fractions ranging from 0.051 × 10−5 to 0.699 × 10−5. Three different model groups were used to analyze the experimental data. The first group comprised seven semi-empirical models, with 3–6 adjustable parameters. These models include Sparks, Sodeifian 1 and 2, Bian, Jouyban, Gordillo and Jafari-Nejad. The second group employed two state equations, namely the Peng-Robinson (PR) and Soave-Redlich-Kwong (SRK) with van der Waals mixing rule. The average absolute relative deviation percentage (AARD%) was 9.73 and 10.63 for PR and SRK, respectively. The third group utilized four machine learning algorithms including DNN, RF, MLP and DTs with the respective R2 values 0.992, 0.980, 0.964 and 0.961, respectively. All of the models exhibited satisfactory agreement with the experimental data. Finally, the enthalpies of vaporization (79.71 kJ/mol) and solvation (−19.25 kJ/mol) were calculated for the first time

A neuro-fuzzy fan speed controller for dynamic thermal management of multi-core processors

Cooling equipments is a thermal management technique that reduces the thermal resistance of the heat sink without any performance degradation. However, higher fan speed produces a lower thermal resistance, but at the expense of higher power consumption. Our proposed Neuro-Fuzzy fan controller (NFSC), minimizes fan power consumption while avoiding the temperature increase above a certain threshold. The experimental results indicate that our proposed model can significantly decrease the average fan power with negligible temperature overhead compared to the traditional fan controller

Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport – a machine learning approach

Human-generated droplets constitute the main route for the transmission of coronavirus. However, the details of such transmission in enclosed environments are yet to be understood. This is because geometrical and environmental parameters can immensely complicate the problem and turn the conventional analyses inefficient. As a remedy, this work develops a predictive tool based on computational fluid dynamics and machine learning to examine the distribution of sneezing droplets in realistic configurations. The time-dependent effects of environmental parameters, including temperature, humidity and ventilation rate, upon the droplets with diameters between 1 and 250μm are investigated inside a bus. It is shown that humidity can profoundly affect the droplets distribution, such that 10% increase in relative humidity results in 30% increase in the droplets density at the farthest point from a sneezing passenger. Further, ventilation process is found to feature dual effects on the droplets distribution. Simple increases in the ventilation rate may accelerate the droplets transmission. However, carefully tailored injection of fresh air enhances deposition of droplets on the surfaces and thus reduces their concentration in the bus. Finally, the analysis identifies an optimal range of temperature, humidity and ventilation rate to maintain human comfort while minimising the transmission of droplets

Application of Machine Learning to Investigation of Heat and Mass Transfer Over a Cylinder Surrounded by Porous Media-The Radial Basic Function Network

This paper investigates heat and mass transport around a cylinder featuring non-isothermal homogenous and heterogeneous chemical reactions in a surrounding porous medium. The system is subject to an impinging flow, while local thermal non-equilibrium, non-linear thermal radiation within the porous region, and the temperature dependency of the reaction rates are considered. Further, non-equilibrium thermodynamics, including Soret and Dufour effects are taken into account. The governing equations are numerically solved using a finite-difference method after reducing them to a system of non-linear ordinary differential equations. Since the current problem contains a large number of parameters with complex interconnections, low-cost models such as those based on artificial intelligence are desirable for the conduction of extensive parametric studies. Therefore, the simulations are used to train an artificial neural network. Comparing various algorithms of the artificial neural network, the radial basic function network is selected. The results show that variations in radiative heat transfer as well as those in Soret and Dufour effects can significantly change the heat and mass transfer responses. Within the investigated parametric range, it is found that the diffusion mechanism is dominantly responsible for heat and mass transfer. Importantly, it is noted that the developed predictor algorithm offers a considerable saving of the computational burden

Analysis of transport processes in a reacting flow of hybrid nanofluid around a bluff-body embedded in porous media using artificial neural network and particle swarm optimization

This paper investigates heat and mass transfer in a hybrid nanofluid flow impinging upon a cylindrical bluff-body embedded in porous media and featuring homogenous and heterogeneous chemical reactions. The analysis includes mixed convection and local thermal non-equilibrium in the porous medium as well as Soret and Dufour effects. Assuming single-phase mixture, a laminar flow of Al2O3-Cu-water (Aluminium oxide-Copper-water) hybrid nanofluid is considered and coupled transport processes are simulated computationally. Due to the significant complexity of this problem, containing a large number of variables, conventional approaches to parametric study struggle to provide meaningful outcomes. As a remedy, the simulation data are fed into an artificial neural network to estimate the target responses. This shows that the volume fraction of nanoparticles, interfacial area of the porous medium and mixed convection parameter, are of primary importance. It is also observed that small variation in the volume fraction of nanoparticles can considerably alter the response of thermal and solutal domains. Further, it is shown that the parameters affecting the thermal process can modify the problem chemically. In particular, raising the volume fraction of nanoparticles enhances the production of chemical species. Furthermore, particle swarm optimization is applied to predict correlations for Nusselt and Sherwood numbers through a systematic identification of the most influential parameters. The current study clearly demonstrates the advantages of using the estimator algorithms to understand and predict the behaviours of complex thermo-chemical and solutal systems