Surface tension of binary mixtures containing environmentally friendly ionic liquids: Insights from artifcial intelligence

The surface tension (ST) of ionic liquids (ILs) and their accompanying mixtures allows engineers to accurately arrange new processes on the industrial scale. Without any doubt, experimental methods for the specification of the ST of every supposable IL and its mixtures with other compounds would be an arduous job. Also, experimental measurements are effortful and prohibitive; thus, a precise estimation of the property via a dependable method would be greatly desirable. For doing this task, a new modeling method according to artificial neural network (ANN) disciplined by four optimization algorithms, namely teaching�learning-based optimization (TLBO), particle swarm optimization (PSO), genetic algorithm (GA) and imperialist competitive algorithm (ICA), has been suggested to estimate ST of the binary ILs mixtures. For training and testing the applied network, a set of 748 data points of binary ST of IL systems within the temperature range of 283.1�348.15 K was utilized. Furthermore, an outlier analysis was used to discover doubtful data points. Gained values of MSE & R2 were 0.0000007 and 0.993, 0.0000002 and 0.998, 0.0000004 and 0.996 and 0.0000006 and 0.994 for the ICA-ANN, TLBO-ANN, PSO-ANN and GA-ANN, respectively. Results demonstrated that the experimental data and predicted values of the TLBO-ANN model for such target are wholly matched

Modeliranje adsorpcije metana, dušika, ugljikova dioksida te njihovih binarnih i ternarnih smjesa na aktivnim ugljenima pomoću umjetne neuronske mreže

This work examines the use of neural networks in modelling the adsorption process of gas mixtures (CO2, CH4, and N2) on different activated carbons. Seven feed-forward neural network models, characterized by different structures, were constructed with the aim of predicting the adsorption of gas mixtures. A set of 417, 625, 143, 87, 64, 64, and 40 data points for NN1 to NN7, respectively, were used to test the neural networks. Of the total data, 60 %, 20 %, and 20 % were used, respectively, for training, validation, and testing of the seven models. Results show a good fit between the predicted and experimental values for each model; good correlations were found (R = 0.99656 for NN1, R = 0.99284 for NN2, R = 0.99388 for NN3, R = 0.99639 for Q1 for NN4, R = 0.99472 for Q2 for NN4, R = 0.99716 for Q1 for NN5, R = 0.99752 for Q3 for NN5, R = 0.99746 for Q2 for NN6, R = 0.99783 for Q3 for NN6, R = 0.9946 for Q1 for NN7, R = 0.99089 for Q2 for NN7, and R = 0.9947 for Q3 for NN7). Moreover, the comparison between the predicted results and the classical models (Gibbs model, Generalized dual-site Langmuir model, and Ideal Adsorption Solution Theory) shows that the neural network models gave far better results. This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom radu ispitana je primjena neuronskih mreža u modeliranju procesa adsorpcije smjese plinova (CO2, CH4 i N2) na različitim aktivnim ugljicima. Izrađeno je sedam modela neuronskih mreža, karakteriziranih različitim strukturama s ciljem predviđanja adsorpcije smjesa plinova. Za testiranje neuronskih mreža primijenjen je skup od 417, 625, 143, 87, 64, 64 i 40 podatkovnih točaka za NN1 do NN7. Od ukupnih podataka 60 %, 20 % i 20 % rabljeno je za obuku, validaciju i testiranje sedam modela. Rezultati pokazuju dobar odnos predviđenih i eksperimentalnih vrijednosti za svaki model; pronađene su dobre korelacije (R = 0,99656 za NN1, R = 0,99284 za NN2, R = 0,99388 za NN3, R = 0,99639 za Q1 za NN4, R = 0,99472 za Q2 za NN4, R = 0,99716 za Q1 za NN5, R = 0,99972 za Q3 za NN5, R = 0,99746 za Q2 za NN6, R = 0,99783 za Q3 za NN6, R = 0,9946 za Q1 za NN7, R = 0,99089 za Q2 za NN7 i R = 0,9947 za Q3 za NN7). Dodatno, usporedba predviđenih rezultata i klasičnih modela (Gibbsov model, generalizirani Langmuirov model i teorija idealne adsorpcije otopine) pokazuje da su modeli neuronskih mreža dali daleko bolje rezultate. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

Predicting Density and Refractive Index of Ionic Liquids

The determination of the physicochemical properties of ionic liquids (ILs), such as density and refractive index, is essential for the design of processes that involve ILs. Density has been widely studied in ILs because of its importance whereas refractive index has received less attention even though its determination is rapid, highly accurate and needs a small amount of sample in most techniques. Due to the large number of possible cation and anion combinations, it is not practical to use trial and error methods to find a suitable ionic liquid for a given function. It would be preferable to predict physical properties of ILs from their structure. We compile in this work different methods to predict density and refractive index of ILs from literature. Especially, we describe the method developed by the authors in a previous work for predicting density of ILs through their molecular volume. We also correlate our experimental measurements of density and refractive index of ILs in order to predict one of the parameters knowing the other one as a function of temperature. As the measurement of refractive index is very fast and needs only a drop of the ionic liquid, this is also a very useful approach

Methodology for predicting oily mixture properties in the mathematical modeling of molecular distillation

A methodology for predicting the thermodynamic and transport properties of a multi-component oily mixture, in which the different mixture components are grouped into a small number of pseudo components is shown. This prediction of properties is used in the mathematical modeling of molecular distillation, which consists of a system of differential equations in partial derivatives, according to the principles of the Transport Phenomena and is solved by an implicit finite difference method using a computer code. The mathematical model was validated with experimental data, specifically the molecular distillation of a deodorizer distillate (DD) of sunflower oil. The results obtained were satisfactory, with errors less than 10% with respect to the experimental data in a temperature range in which it is possible to apply the proposed method.Fil: Gayol, Maria Fernanda. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Departamento de Tecnología Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Pramparo, Maria del Carmen. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Departamento de Tecnología Química; ArgentinaFil: Miro Erdmann, Silvia M.. Universidad Nacional de San Luis. Facultad de Ingeniería y Ciencias Agropecuarias; Argentin

Thermodynamic Assessment and Optimisation of Supercritical and Transcritical Power Cycles Operating on CO2 Mixtures by Means of Artificial Neural Networks

Feb 21, 2022 to Feb 24, 2022, San Antonio, TX, United StatesClosed supercritical and transcritical power cycles operating on Carbon Dioxide have proven to be a promising technology for power generation and, as such, they are being researched by numerous international projects today. Despite the advantageous features of these cycles enabling very high efficiencies in intermediate temperature applications, the major shortcoming of the technology is a strong dependence on ambient temperature; in order to perform compression near the CO2 critical point (31ºC), low ambient temperatures are needed. This is particularly challenging in Concentrated Solar Power applications, typically found in hot, semi-arid locations. To overcome this limitation, the SCARABEUS project explores the idea of blending raw carbon dioxide with small amounts of certain dopants in order to shift the critical temperature of the resulting working fluid to higher values, hence enabling gaseous compression near the critical point or even liquid compression regardless of a high ambient temperature. Different dopants have been studied within the project so far (i.e. C6F6, TiCl4 and SO2) but the final selection will have to account for trade-offs between thermodynamic performance, economic metrics and system reliability. Bearing all this in mind, the present paper deals with the development of a non-physics-based model using Artificial Neural Networks (ANN), developed using Matlab’s Deep Learning Toolbox, to enable SCARABEUS system optimisation without running the detailed – and extremely time consuming – thermal models, developed with Thermoflex and Matlab software. In the first part of the paper, the candidate dopants and cycle layouts are presented and discussed, and a thorough description of the ANN training methodology is provided, along with all the main assumptions and hypothesis made. In the second part of the manuscript, results confirms that the ANN is a reliable tool capable of successfully reproducing the detailed Thermoflex model, estimating the cycle thermal efficiency with a Root Mean Square Error lower than 0.2 percentage points. Furthermore, the great advantage of using the Artificial Neural Network proposed is demonstrated by the huge reduction in the computational time needed, up to 99% lower than the one consumed by the detailed model. Finally, the high flexibility and versatility of the ANN is shown, applying this tool in different scenarios and estimating different cycle thermal efficiency for a great variety of boundary conditions.Unión Europea H2020-81498

Study of the Viscometric and Volumetric Properties of Multicomponent Regular Liquid Systems at Different Temperature Levels

The densities and viscosities of the following quinary system: Chlorobenzene, p-xylene, octane, ethylbenzene, and 1-hexanol and all its quaternary, ternary and binary subsystems were experimentally measured over the entire composition range at 293.15, 298.15, 308.15, and 313.15 K. The sets of data obtained were employed, along with other data reported in the literature, to test the predictive capabilities of some of the most widely accepted and used models available from the literature. The predictive capabilities of the following models were tested: the generalized McAllister three-body interaction model, the pseudo-binary McAllister model, the GC-UNIMOD model, the generalized corresponding states principle (GCSP) model, the Artificial Neural Network (ANN), and the Allan and Teja correlation. The models testing results show that the generalized McAllister three-body interaction model gave the best predictive capability for the regular mixtures under investigation

Defining the critical material attributes of lactose monohydrate in carrier based dry powder inhaler formulations using artificial neural networks

The study aimed to establish a function-based relationship between the physical and bulk properties of pre-blended mixtures of fine and coarse lactose grades with the in vitro performance of an adhesive active pharmaceutical ingredient (API). Different grades of micronised and milled lactose (Lactohale (LH) LH300, LH230, LH210 and Sorbolac 400) were pre-blended with coarse grades of lactose (LH100, LH206 and Respitose SV010) at concentrations of 2.5, 5, 10 and 20 wt.%. The bulk and rheological properties and particle size distributions were characterised. The pre-blends were formulated with micronised budesonide and in vitro performance in a Cyclohaler device tested using a next-generation impactor (NGI) at 90 l/min. Correlations between the lactose properties and in vitro performance were established using linear regression and artificial neural network (ANN) analyses. The addition of milled and micronised lactose fines with the coarse lactose had a significant influence on physical and rheological properties of the bulk lactose. Formulations of the different pre-blends with budesonide directly influenced in vitro performance attributes including fine particle fraction, mass median aerodynamic diameter and pre-separator deposition. While linear regression suggested a number of physical and bulk properties may influence in vitro performance, ANN analysis suggested the critical parameters in describing in vitro deposition patterns were the relative concentrations of lactose fines % < 4.5 μm and % < 15 μm. These data suggest that, for an adhesive API, the proportion of fine particles below % < 4.5 μm and % < 15 μm could be used in rational dry powder inhaler formulation design

Evolutionary polymorphic neural networks in chemical engineering modeling

Evolutionary Polymorphic Neural Network (EPNN) is a novel approach to modeling chemical, biochemical and physical processes. This approach has its basis in modern artificial intelligence, especially neural networks and evolutionary computing. EPNN can perform networked symbolic regressions for input-output data, while providing information about both the structure and complexity of a process during its own evolution. In this work three different processes are modeled: 1. A dynamic neutralization process. 2. An aqueous two-phase system. 3. Reduction of a biodegradation model. In all three cases, EPNN shows better or at least equal performances over published data than traditional thermodynamics /transport or neural network models. Furthermore, in those cases where traditional modeling parameters are difficult to determine, EPNN can be used as an auxiliary tool to produce equivalent empirical formulae for the target process. Feedback links in EPNN network can be formed through training (evolution) to perform multiple steps ahead predictions for dynamic nonlinear systems. Unlike existing applications combining neural networks and genetic algorithms, symbolic formulae can be extracted from EPNN modeling results for further theoretical analysis and process optimization. EPNN system can also be used for data prediction tuning. In which case, only a minimum number of initial system conditions need to be adjusted. Therefore, the network structure of EPNN is more flexible and adaptable than traditional neural networks. Due to the polymorphic and evolutionary nature of the EPNN system, the initially randomized values of constants in EPNN networks will converge to the same or similar forms of functions in separate runs until the training process ends. The EPNN system is not sensitive to differences in initial values of the EPNN population. However, if there exists significant larger noise in one or more data sets in the whole data composition, the EPNN system will probably fail to converge to a satisfactory level of prediction on these data sets. EPNN networks with a relatively small number of neurons can achieve similar or better performance than both traditional thermodynamic and neural network models. The developed EPNN approach provides alternative methods for efficiently modeling complex, dynamic or steady-state chemical processes. EPNN is capable of producing symbolic empirical formulae for chemical processes, regardless of whether or not traditional thermodynamic models are available or can be applied. The EPNN approach does overcome some of the limitations of traditional thermodynamic /transport models and traditional neural network models

Modelling of hydrogen blending into the UK natural gas network driven by a solid oxide fuel cell for electricity and district heating system

A thorough investigation of the thermodynamics and economic performance of a cogeneration system based on solid oxide fuel cells that provides heat and power to homes has been carried out in this study. Additionally, different percentages of green hydrogen have been blended with natural gas to examine the techno-economic performance of the suggested cogeneration system. The energy and exergy efficiency of the system rises steadily as the hydrogen blending percentage rises from 0% to 20%, then slightly drops at 50% H2 blending, and then rises steadily again until 100% H2 supply. The system's minimal levelised cost of energy was calculated to be 4.64 £/kWh for 100% H2. Artificial Neural Network (ANN) model was also used to further train a sizable quantity of data that was received from the simulation model. Heat, power, and levelised cost of energy estimates using the ANN model were found to be extremely accurate, with coefficients of determination of 0.99918, 0.99999, and 0.99888, respectively